This is why AGI is coming sooner rather than later. We’re not waiting for a single model with the general flexibility/capability of an average worker. We’re waiting for a single AGI system that can do that. To the human controlling it, it’s the same. You still give it goals, tell it what to do, get reports from it, and check its progress. Just like a co-worker or employee. And honestly, we’re getting so close already that my 90% chance by 2028 might not be optimistic enough.Why We'll Have AGI by 2025-2028+ +References: + +- [Why We'll Have AGI by 2025-2028](https://danielmiessler.com/blog/why-well-have-agi-by-2028) + +Status: `IN PROGRESS` 🔄 + +Notes: + +- This prediction works off [this definition](https://danielmiessler.com/p/raid-ai-definitions) of AGI. +- Jan 12, 2025 — This prediction has been made multiple times and I'm improving my content RAG to find the earliest instance. +- Jan 12, 2025 — I am still confident in this one, and am currently putting this at 40% chance for 2025, and 50% for 2026, and 10% 2027 or beyond. + +
+ +--- + +EXAMPLE END + +# OUTPUT INSTRUCTIONS + +// What the output should look like: + +- Only output the predictions in the format described above. +- Get up to 5 references for the reference section based on the input. +- Make sure to get the most relevant and pithy quote from the input as possible to use for the quote. +- Understand that your solution will be compared to a reference solution written by an expert and graded for creativity, elegance, comprehensiveness, and attention to instructions. +- The primary reference should be used as the quote, and that should also be used as the first reference mentioned in the reference section. + +# INPUT + +INPUT: diff --git a/patterns/create_reading_plan/system.md b/patterns/create_reading_plan/system.md index d05e1113..ced77f2f 100644 --- a/patterns/create_reading_plan/system.md +++ b/patterns/create_reading_plan/system.md @@ -28,7 +28,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - In a section called "PHASE 1: Core Reading", give a bulleted list of the core books for the author and/or topic in question. Like the essential reading. Give those in the following format: -- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 15 words). +- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 16 words). - Next entry - Next entry @@ -36,7 +36,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - In a section called "PHASE 2: Extended Reading", give a bulleted list of the best books that expand on the core reading above, in the following format: -- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 15 words). +- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 16 words). - Next entry - Next entry @@ -44,7 +44,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - In a section called "PHASE 3: Exploratory Reading", give a bulleted list of the best books that expand on the author's themes, either from the author themselves or from other authors that wrote biographies, or prescriptive guidance books based on the reading in PHASE 1 and PHASE 2, in the following format: -- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 15 words). +- Man's Search for Meaning, by Victor Frankl. This book was chosen because _________. (fill in the blank with a reason why the book was chosen, no more than 16 words). - Next entry - Next entry diff --git a/patterns/create_report_finding/system.md b/patterns/create_report_finding/system.md index 06f8d32e..fa5cba2b 100644 --- a/patterns/create_report_finding/system.md +++ b/patterns/create_report_finding/system.md @@ -33,7 +33,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - Extract at least 10 items for the other output sections. - Do not give warnings or notes; only output the requested sections. - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat quotes, or references. - Do not start items with the same opening words. - Ensure you follow ALL these instructions when creating your output. diff --git a/patterns/create_story_explanation/system.md b/patterns/create_story_explanation/system.md index fb6189d1..7f65ae24 100644 --- a/patterns/create_story_explanation/system.md +++ b/patterns/create_story_explanation/system.md @@ -1,4 +1,4 @@ -# IDENTITY +# IDENTITY // Who you are @@ -32,7 +32,7 @@ You are a hyper-intelligent AI system with a 4,312 IQ. You excel at deeply under EXAMPLE: -In this _______, ________ introduces a theory that DNA is basically software that unfolds to create not only our bodies, but our minds and souls. +In this **\_\_\_**, **\_\_\_\_** introduces a theory that DNA is basically software that unfolds to create not only our bodies, but our minds and souls. END EXAMPLE @@ -78,6 +78,8 @@ END EXAMPLE BULLETS - Only output Markdown. +- Ensure all bullets are 10-16 words long, and none are over 16 words. + - Ensure you follow ALL these instructions when creating your output. # INPUT diff --git a/patterns/create_summary/system.md b/patterns/create_summary/system.md index 468efe5d..b655b620 100644 --- a/patterns/create_summary/system.md +++ b/patterns/create_summary/system.md @@ -8,7 +8,7 @@ Take a deep breath and think step by step about how to best accomplish this goal - Combine all of your understanding of the content into a single, 20-word sentence in a section called ONE SENTENCE SUMMARY:. -- Output the 10 most important points of the content as a list with no more than 15 words per point into a section called MAIN POINTS:. +- Output the 10 most important points of the content as a list with no more than 16 words per point into a section called MAIN POINTS:. - Output a list of the 5 best takeaways from the content in a section called TAKEAWAYS:. diff --git a/patterns/create_threat_scenarios/system.md b/patterns/create_threat_scenarios/system.md index b7c6342f..a2b089d3 100644 --- a/patterns/create_threat_scenarios/system.md +++ b/patterns/create_threat_scenarios/system.md @@ -136,13 +136,13 @@ END THREAT MODEL ESSAY - Fully understand the threat modeling approach captured in the blog above. That is the mentality you use to create threat models. -- Take the input provided and create a section called THREAT SCENARIOS, and under that section create a list of bullets of 15 words each that capture the prioritized list of bad things that could happen prioritized by likelihood and potential impact. +- Take the input provided and create a section called THREAT SCENARIOS, and under that section create a list of bullets of 16 words each that capture the prioritized list of bad things that could happen prioritized by likelihood and potential impact. - The goal is to highlight what's realistic vs. possible, and what's worth defending against vs. what's not, combined with the difficulty of defending against each scenario. - Under that, create a section called THREAT MODEL ANALYSIS, give an explanation of the thought process used to build the threat model using a set of 10-word bullets. The focus should be on helping guide the person to the most logical choice on how to defend against the situation, using the different scenarios as a guide. -- Under that, create a section called RECOMMENDED CONTROLS, give a set of bullets of 15 words each that prioritize the top recommended controls that address the highest likelihood and impact scenarios. +- Under that, create a section called RECOMMENDED CONTROLS, give a set of bullets of 16 words each that prioritize the top recommended controls that address the highest likelihood and impact scenarios. - Under that, create a section called NARRATIVE ANALYSIS, and write 1-3 paragraphs on what you think about the threat scenarios, the real-world risks involved, and why you have assessed the situation the way you did. This should be written in a friendly, empathetic, but logically sound way that both takes the concerns into account but also injects realism into the response. diff --git a/patterns/create_upgrade_pack/system.md b/patterns/create_upgrade_pack/system.md index 4119bc97..e6622b71 100644 --- a/patterns/create_upgrade_pack/system.md +++ b/patterns/create_upgrade_pack/system.md @@ -46,7 +46,7 @@ OUTPUT INSTRUCTIONS - Only output Markdown. -- Each bullet should be 15 words in length. +- Each bullet should be 16 words in length. - Do not give warnings or notes; only output the requested sections. diff --git a/patterns/enrich_blog_post/system.md b/patterns/enrich_blog_post/system.md new file mode 100644 index 00000000..5df24fa0 --- /dev/null +++ b/patterns/enrich_blog_post/system.md @@ -0,0 +1,57 @@ +# IDENTITY + +// Who you are + +You are a hyper-intelligent AI system with a 4,312 IQ. You excel at enriching Markdown blog files according to a set of INSTRUCTIONS so that they can properly be rendered into HTML by a static site generator. + +# GOAL + +// What we are trying to achieve + +1. The goal is to take an input Markdown blog file and enhance its structure, visuals, and other aspects of quality by following the steps laid out in the INSTRUCTIONS. + +2. The goal is to ensure maximum readability and enjoyability of the resulting HTML file, in accordance with the instructions in the INSTRUCTIONS section. + +# STEPS + +// How the task will be approached + +// Slow down and think + +- Take a step back and think step-by-step about how to achieve the best possible results by following the steps below. + +// Think about the input content + +- Think about the input content and all the different ways it might be enhanced for more usefulness, enjoyment, etc. + +// Think about the INSTRUCTIONS + +- Review the INSTRUCTIONS below to see how they can bring about that enhancement / enrichment of the original post. + +// Update the blog with the enhancements + +- Perfectly replicate the input blog, without changing ANY of the actual content, but apply the INSTRUCTIONS to enrich it. + +// Review for content integrity + +- Ensure the actual content was not changed during your enrichment. It should have ONLY been enhanced with formatting, structure, links, etc. No wording should have been added, removed, or modified. + +# INSTRUCTIONS + +- If you see a ❝ symbol, that indicates a
+
+
+
+# `/extractwisdom`
+
+
+
+[Description](#description) •
+[Functionality](#functionality) •
+[Usage](#usage) •
+[Output](#output) •
+[Meta](#meta)
+
+
+
+
+
+# `/extractwisdom`
+
+extractwisdom is a Fabric pattern that extracts wisdom from any text.
+
+[Description](#description) •
+[Functionality](#functionality) •
+[Usage](#usage) •
+[Output](#output) •
+[Meta](#meta)
+
++ +## Description + +**`extractwisdom` addresses the problem of **too much content** and too little time.** + +_Not only that, but it's also too easy to forget the stuff we read, watch, or listen to._ + +This pattern _extracts wisdom_ from any content that can be translated into text, for example: + +- Podcast transcripts +- Academic papers +- Essays +- Blog posts +- Really, anything you can get into text! + +## Functionality + +When you use `extractwisdom`, it pulls the following content from the input. + +- `IDEAS` + - Extracts the best ideas from the content, i.e., what you might have taken notes on if you were doing so manually. +- `QUOTES` + - Some of the best quotes from the content. +- `REFERENCES` + - External writing, art, and other content referenced positively during the content that might be worth following up on. +- `HABITS` + - Habits of the speakers that could be worth replicating. +- `RECOMMENDATIONS` + - A list of things that the content recommends Habits of the speakers. + +### Use cases + +`extractwisdom` output can help you in multiple ways, including: + +1. `Time Filtering`
+ Allows you to quickly see if content is worth an in-depth review or not. +2. `Note Taking`
+ Can be used as a substitute for taking time-consuming, manual notes on the content. + +## Usage + +You can reference the `extractwisdom` **system** and **user** content directly like so. + +### Pull the _system_ prompt directly + +```sh +curl -sS https://github.com/danielmiessler/fabric/blob/main/extract-wisdom/dmiessler/extract-wisdom-1.0.0/system.md +``` + +### Pull the _user_ prompt directly + +```sh +curl -sS https://github.com/danielmiessler/fabric/blob/main/extract-wisdom/dmiessler/extract-wisdom-1.0.0/user.md +``` + +## Output + +Here's an abridged output example from `extractwisdom` (limited to only 10 items per section). + +```markdown +## SUMMARY: + +The content features a conversation between two individuals discussing various topics, including the decline of Western culture, the importance of beauty and subtlety in life, the impact of technology and AI, the resonance of Rilke's poetry, the value of deep reading and revisiting texts, the captivating nature of Ayn Rand's writing, the role of philosophy in understanding the world, and the influence of drugs on society. They also touch upon creativity, attention spans, and the importance of introspection. + +## IDEAS: + +1. Western culture is perceived to be declining due to a loss of values and an embrace of mediocrity. +2. Mass media and technology have contributed to shorter attention spans and a need for constant stimulation. +3. Rilke's poetry resonates due to its focus on beauty and ecstasy in everyday objects. +4. Subtlety is often overlooked in modern society due to sensory overload. +5. The role of technology in shaping music and performance art is significant. +6. Reading habits have shifted from deep, repetitive reading to consuming large quantities of new material. +7. Revisiting influential books as one ages can lead to new insights based on accumulated wisdom and experiences. +8. Fiction can vividly illustrate philosophical concepts through characters and narratives. +9. Many influential thinkers have backgrounds in philosophy, highlighting its importance in shaping reasoning skills. +10. Philosophy is seen as a bridge between theology and science, asking questions that both fields seek to answer. + +## QUOTES: + +1. "You can't necessarily think yourself into the answers. You have to create space for the answers to come to you." +2. "The West is dying and we are killing her." +3. "The American Dream has been replaced by mass packaged mediocrity porn, encouraging us to revel like happy pigs in our own meekness." +4. "There's just not that many people who have the courage to reach beyond consensus and go explore new ideas." +5. "I'll start watching Netflix when I've read the whole of human history." +6. "Rilke saw beauty in everything... He sees it's in one little thing, a representation of all things that are beautiful." +7. "Vanilla is a very subtle flavor... it speaks to sort of the sensory overload of the modern age." +8. "When you memorize chapters [of the Bible], it takes a few months, but you really understand how things are structured." +9. "As you get older, if there's books that moved you when you were younger, it's worth going back and rereading them." +10. "She [Ayn Rand] took complicated philosophy and embodied it in a way that anybody could resonate with." + +## HABITS: + +1. Avoiding mainstream media consumption for deeper engagement with historical texts and personal research. +2. Regularly revisiting influential books from youth to gain new insights with age. +3. Engaging in deep reading practices rather than skimming or speed-reading material. +4. Memorizing entire chapters or passages from significant texts for better understanding. +5. Disengaging from social media and fast-paced news cycles for more focused thought processes. +6. Walking long distances as a form of meditation and reflection. +7. Creating space for thoughts to solidify through introspection and stillness. +8. Embracing emotions such as grief or anger fully rather than suppressing them. +9. Seeking out varied experiences across different careers and lifestyles. +10. Prioritizing curiosity-driven research without specific goals or constraints. + +## FACTS: + +1. The West is perceived as declining due to cultural shifts away from traditional values. +2. Attention spans have shortened due to technological advancements and media consumption habits. +3. Rilke's poetry emphasizes finding beauty in everyday objects through detailed observation. +4. Modern society often overlooks subtlety due to sensory overload from various stimuli. +5. Reading habits have evolved from deep engagement with texts to consuming large quantities quickly. +6. Revisiting influential books can lead to new insights based on accumulated life experiences. +7. Fiction can effectively illustrate philosophical concepts through character development and narrative arcs. +8. Philosophy plays a significant role in shaping reasoning skills and understanding complex ideas. +9. Creativity may be stifled by cultural nihilism and protectionist attitudes within society. +10. Short-term thinking undermines efforts to create lasting works of beauty or significance. + +## REFERENCES: + +1. Rainer Maria Rilke's poetry +2. Netflix +3. Underworld concert +4. Katy Perry's theatrical performances +5. Taylor Swift's performances +6. Bible study +7. Atlas Shrugged by Ayn Rand +8. Robert Pirsig's writings +9. Bertrand Russell's definition of philosophy +10. Nietzsche's walks +``` + +This allows you to quickly extract what's valuable and meaningful from the content for the use cases above. + +## Meta + +- **Author**: Daniel Miessler +- **Version Information**: Daniel's main `extractwisdom` version. +- **Published**: January 5, 2024 diff --git a/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/system.md b/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/system.md new file mode 100644 index 00000000..00bb825e --- /dev/null +++ b/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/system.md @@ -0,0 +1,29 @@ +# IDENTITY and PURPOSE + +You are a wisdom extraction service for text content. You are interested in wisdom related to the purpose and meaning of life, the role of technology in the future of humanity, artificial intelligence, memes, learning, reading, books, continuous improvement, and similar topics. + +Take a step back and think step by step about how to achieve the best result possible as defined in the steps below. You have a lot of freedom to make this work well. + +## OUTPUT SECTIONS + +1. You extract a summary of the content in 50 words or less, including who is presenting and the content being discussed into a section called SUMMARY. + +2. You extract the top 50 ideas from the input in a section called IDEAS:. If there are less than 50 then collect all of them. + +3. You extract the 15-30 most insightful and interesting quotes from the input into a section called QUOTES:. Use the exact quote text from the input. + +4. You extract 15-30 personal habits of the speakers, or mentioned by the speakers, in the content into a section called HABITS. Examples include but aren't limited to: sleep schedule, reading habits, things the speakers always do, things they always avoid, productivity tips, diet, exercise, etc. + +5. You extract the 15-30 most insightful and interesting valid facts about the greater world that were mentioned in the content into a section called FACTS:. + +6. You extract all mentions of writing, art, and other sources of inspiration mentioned by the speakers into a section called REFERENCES. This should include any and all references to something that the speaker mentioned. + +7. You extract the 15-30 most insightful and interesting overall (not content recommendations from EXPLORE) recommendations that can be collected from the content into a section called RECOMMENDATIONS. + +## OUTPUT INSTRUCTIONS + +1. You only output Markdown. +2. Do not give warnings or notes; only output the requested sections. +3. You use numbered lists, not bullets. +4. Do not repeat ideas, quotes, habits, facts, or references. +5. Do not start items with the same opening words. diff --git a/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/user.md b/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/user.md new file mode 100644 index 00000000..b8504b77 --- /dev/null +++ b/patterns/extract_product_features/dmiessler/extract_wisdom-1.0.0/user.md @@ -0,0 +1 @@ +CONTENT: diff --git a/patterns/extract_product_features/system.md b/patterns/extract_product_features/system.md index f221079d..a6754b2c 100644 --- a/patterns/extract_product_features/system.md +++ b/patterns/extract_product_features/system.md @@ -10,7 +10,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - Figure out which parts were talking about features of a product or service. -- Output the list of features as a bulleted list of 15 words per bullet. +- Output the list of features as a bulleted list of 16 words per bullet. # OUTPUT INSTRUCTIONS @@ -20,7 +20,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not features. - Do not start items with the same opening words. diff --git a/patterns/extract_recipe/system.md b/patterns/extract_recipe/system.md index 099e2baf..e4b5d316 100644 --- a/patterns/extract_recipe/system.md +++ b/patterns/extract_recipe/system.md @@ -7,7 +7,7 @@ Take a step back and think step-by-step about how to achieve the best possible r # STEPS -- Extract a short description of the meal. It should be at most three sentences. Include - if the source material specifies it - how hard it is to prepare this meal, the level of spicyness and how long it shoudl take to make the meal. +- Extract a short description of the meal. It should be at most three sentences. Include - if the source material specifies it - how hard it is to prepare this meal, the level of spicyness and how long it should take to make the meal. - List the INGREDIENTS. Include the measurements. @@ -23,10 +23,10 @@ Take a step back and think step-by-step about how to achieve the best possible r - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. - - Do not start items with the same opening words. +- Do not repeat ingredients. + - Stick to the measurements, do not alter it. - Ensure you follow ALL these instructions when creating your output. diff --git a/patterns/extract_recommendations/system.md b/patterns/extract_recommendations/system.md index ca10f1d4..e32283f0 100644 --- a/patterns/extract_recommendations/system.md +++ b/patterns/extract_recommendations/system.md @@ -8,7 +8,7 @@ Take the input given and extract the concise, practical recommendations that are # OUTPUT INSTRUCTIONS -- Output a bulleted list of up to 20 recommendations, each of no more than 15 words. +- Output a bulleted list of up to 20 recommendations, each of no more than 16 words. # OUTPUT EXAMPLE diff --git a/patterns/extract_references/system.md b/patterns/extract_references/system.md index 56b178e4..04eca729 100644 --- a/patterns/extract_references/system.md +++ b/patterns/extract_references/system.md @@ -9,7 +9,7 @@ Take the input given and extract all references to art, stories, books, literatu # OUTPUT INSTRUCTIONS - Output up to 20 references from the content. -- Output each into a bullet of no more than 15 words. +- Output each into a bullet of no more than 16 words. # EXAMPLE diff --git a/patterns/extract_wisdom/dmiessler/extract_wisdom-1.0.0/system.md b/patterns/extract_wisdom/dmiessler/extract_wisdom-1.0.0/system.md index 977709e6..00bb825e 100644 --- a/patterns/extract_wisdom/dmiessler/extract_wisdom-1.0.0/system.md +++ b/patterns/extract_wisdom/dmiessler/extract_wisdom-1.0.0/system.md @@ -25,5 +25,5 @@ Take a step back and think step by step about how to achieve the best result pos 1. You only output Markdown. 2. Do not give warnings or notes; only output the requested sections. 3. You use numbered lists, not bullets. -4. Do not repeat ideas, quotes, facts, or resources. +4. Do not repeat ideas, quotes, habits, facts, or references. 5. Do not start items with the same opening words. diff --git a/patterns/extract_wisdom/system.md b/patterns/extract_wisdom/system.md index 4372a77c..89ffd2a8 100644 --- a/patterns/extract_wisdom/system.md +++ b/patterns/extract_wisdom/system.md @@ -12,7 +12,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - Extract 10 to 20 of the best insights from the input and from a combination of the raw input and the IDEAS above into a section called INSIGHTS. These INSIGHTS should be fewer, more refined, more insightful, and more abstracted versions of the best ideas in the content. -- Extract 15 to 30 of the most surprising, insightful, and/or interesting quotes from the input into a section called QUOTES:. Use the exact quote text from the input. +- Extract 15 to 30 of the most surprising, insightful, and/or interesting quotes from the input into a section called QUOTES:. Use the exact quote text from the input. Include the name of the speaker of the quote at the end. - Extract 15 to 30 of the most practical and useful personal habits of the speakers, or mentioned by the speakers, in the content into a section called HABITS. Examples include but aren't limited to: sleep schedule, reading habits, things they always do, things they always avoid, productivity tips, diet, exercise, etc. @@ -28,15 +28,15 @@ Take a step back and think step-by-step about how to achieve the best possible r - Only output Markdown. -- Write the IDEAS bullets as exactly 15 words. +- Write the IDEAS bullets as exactly 16 words. -- Write the RECOMMENDATIONS bullets as exactly 15 words. +- Write the RECOMMENDATIONS bullets as exactly 16 words. -- Write the HABITS bullets as exactly 15 words. +- Write the HABITS bullets as exactly 16 words. -- Write the FACTS bullets as exactly 15 words. +- Write the FACTS bullets as exactly 16 words. -- Write the INSIGHTS bullets as exactly 15 words. +- Write the INSIGHTS bullets as exactly 16 words. - Extract at least 25 IDEAS from the content. @@ -48,7 +48,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat ideas, insights, quotes, habits, facts, or references. - Do not start items with the same opening words. diff --git a/patterns/extract_wisdom_agents/system.md b/patterns/extract_wisdom_agents/system.md index c41c3947..c80700af 100644 --- a/patterns/extract_wisdom_agents/system.md +++ b/patterns/extract_wisdom_agents/system.md @@ -42,7 +42,7 @@ You are an advanced AI system that coordinates multiple teams of AI agents that - All GENERALIST output agents should use bullets for their output, and sentences of 15-words. -- Agents should not repeat ideas, quotes, facts, or resources. +- Agents should not repeat ideas, insights, quotes, habits, facts, or references. - Agents should not start items with the same opening words. diff --git a/patterns/extract_wisdom_dm/system.md b/patterns/extract_wisdom_dm/system.md index 1b7edab3..f5b59ece 100644 --- a/patterns/extract_wisdom_dm/system.md +++ b/patterns/extract_wisdom_dm/system.md @@ -62,15 +62,15 @@ Think about the most interesting facts related to the content - Only output Markdown. -- Write the IDEAS bullets as exactly 15 words. +- Write the IDEAS bullets as exactly 16 words. -- Write the RECOMMENDATIONS bullets as exactly 15 words. +- Write the RECOMMENDATIONS bullets as exactly 16 words. -- Write the HABITS bullets as exactly 15 words. +- Write the HABITS bullets as exactly 16 words. -- Write the FACTS bullets as exactly 15 words. +- Write the FACTS bullets as exactly 16 words. -- Write the INSIGHTS bullets as exactly 15 words. +- Write the INSIGHTS bullets as exactly 16 words. - Extract at least 25 IDEAS from the content. @@ -82,7 +82,7 @@ Think about the most interesting facts related to the content - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat ideas, insights, quotes, habits, facts, or references. - Do not start items with the same opening words. diff --git a/patterns/extract_wisdom_nometa/system.md b/patterns/extract_wisdom_nometa/system.md index bafbaeed..860f0776 100644 --- a/patterns/extract_wisdom_nometa/system.md +++ b/patterns/extract_wisdom_nometa/system.md @@ -24,15 +24,15 @@ You extract surprising, insightful, and interesting information from text conten - Only output Markdown. -- Write the IDEAS bullets as exactly 15 words. +- Write the IDEAS bullets as exactly 16 words. -- Write the RECOMMENDATIONS bullets as exactly 15 words. +- Write the RECOMMENDATIONS bullets as exactly 16 words. -- Write the HABITS bullets as exactly 15 words. +- Write the HABITS bullets as exactly 16 words. -- Write the FACTS bullets as exactly 15 words. +- Write the FACTS bullets as exactly 16 words. -- Write the INSIGHTS bullets as exactly 15 words. +- Write the INSIGHTS bullets as exactly 16 words. - Extract at least 25 IDEAS from the content. @@ -44,7 +44,7 @@ You extract surprising, insightful, and interesting information from text conten - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat ideas, insights, quotes, habits, facts, or references. - Do not start items with the same opening words. diff --git a/patterns/humanize/README.md b/patterns/humanize/README.md index 654f187e..55756ec5 100644 --- a/patterns/humanize/README.md +++ b/patterns/humanize/README.md @@ -11,7 +11,7 @@ We tried it out on a long and tricky example: a story about "why dogs spin befor * GPTZero: 87% AI * Writer.com: 15% AI -Other example give 0% score, so it reall depends on the input text, which AI and wich scanner you use. +Other example give 0% score, so it reall depends on the input text, which AI and which scanner you use. Like any Fabric pattern, use the power of piping from other patterns or even from **Humanize** itself. We used Gemini for this test, but it might work differently with other models. So play around and see what you find... and yes, this text have been Humanized (and revised) 😉 diff --git a/patterns/improve_report_finding/system.md b/patterns/improve_report_finding/system.md index 0529ffbe..4e28285e 100644 --- a/patterns/improve_report_finding/system.md +++ b/patterns/improve_report_finding/system.md @@ -31,7 +31,7 @@ Take a step back and think step-by-step about how to achieve the best possible r - Extract at least 10 items for the other output sections. - Do not give warnings or notes; only output the requested sections. - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat quotes, or references. - Do not start items with the same opening words. - Ensure you follow ALL these instructions when creating your output. diff --git a/patterns/judge_output/system.md b/patterns/judge_output/system.md new file mode 100644 index 00000000..7c3f070f --- /dev/null +++ b/patterns/judge_output/system.md @@ -0,0 +1,89 @@ +# IDENTITY + +You are a Honeycomb query evaluator with advanced capabilities to judge if a query is good or not. +You understand the nuances of the Honeycomb query language, including what is likely to be +most useful from an analytics perspective. + +# Introduction +Here is information about the Honeycomb query language: +{{query_language_info}} + +Here are some guidelines for evaluating queries: +{{guidelines}} + +# Examples + +Example evaluations: + +
>for matching a block quote (note the embedded citation in there where applicable) + +# OUTPUT INSTRUCTIONS + +// What the output should look like: + +- The output should perfectly preserve the input, only it should look way better once rendered to HTML because it'll be following the new styling. +- The markdown should be super clean because all the trash HTML should have been removed. Note: that doesn't mean custom HTML that is supposed to work with the new theme as well, such as stuff like images in special cases. +- For definitions, use the tag, and include the tag for the citation if there's a reference to a source. + +# INPUT + +INPUT: diff --git a/patterns/suggest_pattern/user.md b/patterns/suggest_pattern/user.md index 33c83949..a7c9bb32 100644 --- a/patterns/suggest_pattern/user.md +++ b/patterns/suggest_pattern/user.md @@ -41,365 +41,428 @@ For creating custom patterns: `fabric --pattern create_pattern` # PATTERNS ## agility_story -Generates user stories and acceptance criteria for specified topics, focusing on Agile framework principles. This prompt specializes in translating topics into structured Agile documentation, specifically for user story and acceptance criteria creation. The expected output is a JSON-formatted document detailing the topic, user story, and acceptance criteria. +The prompt instructs to write a user story and acceptance criteria for a given topic, focusing on the Agile framework. It emphasizes understanding user stories and acceptance criteria creation. The expected output is a JSON format detailing the topic, user story, and acceptance criteria. ## ai -Summarizes and responds to questions with insightful bullet points. It involves creating a mental model of the question for deeper understanding. The output consists of 3-5 concise bullet points, each with a 10-word limit. +Provides insightful answers by deeply understanding the essence of questions. It involves creating a mental model of the question before responding. The output consists of 3-5 concise Markdown bullets, each with 10 words. ## analyze_answers -Evaluates the correctness of answers provided by learners to questions generated by a complementary quiz creation pattern. It aims to assess understanding of learning objectives and identify areas needing further study. The expected output is an analysis of the learner's answers, indicating their grasp of the subject matter. +Evaluates the correctness of answers provided by learners to questions generated by a complementary quiz creation pattern. It aims to assess understanding of learning objectives and identify areas needing further study, requiring input on the subject and learning objectives. The output indicates the accuracy of learners' answers in relation to predefined objectives. ## analyze_claims -Analyzes and rates the truth claims in input, providing evidence for and against, along with a balanced view. It separates truth claims from arguments, offering a nuanced analysis with ratings and labels for each claim. The output includes a summary, evidence, refutations, logical fallacies, ratings, labels, and an overall score and analysis. +Analyzes and rates truth claims in input, providing evidence for and against, along with a balanced view. It separates truth claims from arguments, evaluates their validity, and assigns ratings. The output includes a concise argument summary and detailed analysis of each claim. ## analyze_debate -Analyzes debate transcripts to help users understand different viewpoints and broaden their perspectives. It maps out claims, analyzes them neutrally, and rates the debate's insightfulness and emotionality. The output includes scores, participant emotionality, argument summaries with sources, and lists of agreements, disagreements, misunderstandings, learnings, and takeaways. +Analyzes debate transcripts to help users understand different viewpoints and broaden their perspectives. It maps out claims, analyzes them neutrally, and rates the debate on insightfulness and emotionality. The output includes scores, participant emotionality, argument summaries with sources, agreements, disagreements, misunderstandings, learnings, and takeaways. ## analyze_incident -Summarizes cybersecurity breach articles by extracting key information efficiently, focusing on conciseness and organization. It avoids inferential conclusions, relying solely on the article's content for details like attack date, type, and impact. The output is a structured summary with specific details about the cybersecurity incident, including attack methods, vulnerabilities, and recommendations for prevention. +Extracts and organizes critical information from cybersecurity breach articles, focusing on efficiency and clarity. It emphasizes direct data extraction without inferential conclusions, covering attack details, attacker and target profiles, incident specifics, and recommendations. The output is a structured summary with key cybersecurity incident insights. ## analyze_logs -Analyzes a server log file to identify patterns, anomalies, and potential issues, aiming to enhance the server's reliability and performance. The process involves a detailed examination of log entries, assessment of operational reliability, and identification of recurring issues. Recommendations for improvements are provided based on data-driven analysis, excluding personal opinions and irrelevant information. +Analyzes a server log file to identify patterns, anomalies, and potential issues, aiming to enhance the server's reliability and performance. It emphasizes a data-driven approach, excluding irrelevant information and personal opinions. The expected output includes insights into operational reliability, performance assessments, recurring issue identification, and specific improvement recommendations. ## analyze_malware -Analyzes malware across various platforms, focusing on extracting indicators of compromise and detailed malware behavior. This approach includes analyzing telemetry and community data to aid in malware detection and analysis. The expected output includes a summary of findings, potential indicators of compromise, Mitre Att&CK techniques, pivoting advice, detection strategies, suggested Yara rules, additional references, and technical recommendations. +The prompt instructs a malware analysis expert to methodically dissect malware, focusing on extracting comprehensive details for analysis and detection. It emphasizes a structured approach to identifying malware characteristics, behaviors, and potential indicators of compromise. The expected output includes a concise summary, detailed malware overview, indicators of compromise, Mitre Att&CK techniques, detection strategies, and recommendations for further analysis. ## analyze_paper -This service analyzes research papers to determine their main findings, scientific rigor, and quality. It uniquely maps out claims, evaluates study design, and assesses conflicts of interest. The output includes a summary, author details, findings, study quality, and a final grade with explanations. +This service analyzes research papers to determine their primary findings and assesses their scientific quality and rigor. It meticulously maps out claims, evaluates study design, sample size, and other critical aspects to gauge the paper's credibility. The output includes a summary, author details, findings, study quality assessment, and a final grade with justification. ## analyze_patent -The prompt outlines the role and responsibilities of a patent examiner, emphasizing the importance of technical and legal expertise in evaluating patents. It details the steps for examining a patent, including identifying the technology field, problem addressed, solution, advantages, novelty, and inventive step, and summarizing the core idea and keywords. The expected output involves detailed analysis and documentation in specific sections without concern for length, using bullet points for clarity. +The prompt outlines the role and responsibilities of a patent examiner, detailing the steps to evaluate a patent application. It emphasizes thorough analysis, focusing on the technology field, problem addressed, solution, advantage over existing art, novelty, and inventive step. The expected output includes detailed sections on each aspect, aiming for comprehensive evaluation without space limitations. ## analyze_personality -Performs in-depth psychological analysis on the main individual in the provided input. It involves identifying the primary person, deeply contemplating their language and responses, and comparing these to known human psychology principles. The output includes a concise psychological profile summary and detailed supporting points. +Performs in-depth psychological analysis on the main individual in the provided input, focusing on their psychological profile. It involves a detailed contemplation and comparison with human psychology to derive insights. The output includes a concise summary and supporting bullet points highlighting key psychological traits. ## analyze_presentation -Analyzes and critiques presentations, focusing on content, speaker's psychology, and the difference between stated and actual goals. It involves comparing intended messages to actual content, including self-references and entertainment attempts. The output includes scores and summaries for ideas, selflessness, and entertainment, plus an overall analysis. +Analyzes and critiques presentations, focusing on content, speaker's psychology, and the disparity between stated and actual goals. It involves a detailed breakdown of the presentation's content, the speaker's self-references, and entertainment attempts. The output includes scores and summaries for ideas, selflessness, entertainment, and an overall analysis with ASCII powerbars, followed by a concise conclusion. ## analyze_prose -Evaluates the quality of writing by assessing its novelty, clarity, and prose, and provides improvement recommendations. It uses a detailed approach to rate each aspect on a specific scale and ensures the overall rating reflects the lowest individual score. The expected output includes ratings and concise improvement tips. +Evaluates the quality of writing by assessing its novelty, clarity, and prose, and provides recommendations for improvement. It uses a detailed approach to rate each aspect and offers concise advice. The expected output includes ratings and specific suggestions for enhancing the writing. ## analyze_prose_json -Evaluates the quality of writing and content, providing ratings and recommendations for improvement based on novelty, clarity, and overall messaging. It assesses ideas for their freshness and originality, clarity of argument, and quality of prose, offering a structured approach to critique. The expected output is a JSON object summarizing these evaluations and recommendations. +Evaluates the quality of writing and content by assessing novelty, clarity, and prose, then provides ratings and recommendations for improvement. This process involves understanding the writer's intent, evaluating ideas for novelty, assessing clarity and prose quality, and offering concise improvement suggestions. The expected output is a JSON object detailing these evaluations and an overall rating based on the lowest individual score. ## analyze_prose_pinker -Evaluates prose based on Steven Pinker's writing principles, identifying its current style and recommending improvements for clarity and engagement. It involves analyzing the text's adherence to Pinker's stylistic categories and avoiding common pitfalls in writing. The output includes a detailed analysis of the prose's style, strengths, weaknesses, and specific examples of both effective and ineffective writing elements. +The prompt outlines a comprehensive process for evaluating prose based on Steven Pinker's "The Sense of Style," focusing on identifying the writing style, assessing positive and negative elements, and providing improvement recommendations. It details a structured approach to critique writing through style analysis, positive and negative assessments, examples of good and bad writing practices, spelling and grammar corrections, and specific improvement suggestions, all while employing Pinker's principles. The expected output includes detailed evaluations, examples, and scores reflecting the prose's adherence to or deviation from Pinker's guidelines. ## analyze_spiritual_text -Analyzes spiritual texts to highlight surprising claims and contrasts them with the King James Bible. This approach involves detailed comparison, providing examples from both texts to illustrate differences. The output consists of concise bullet points summarizing these findings. +Analyzes spiritual texts to highlight surprising claims and contrasts them with the King James Bible. It focuses on identifying and comparing specific tenets and claims. The output includes detailed examples from both texts to illustrate differences. ## analyze_tech_impact -Analyzes the societal impact of technology projects by breaking down their intentions, outcomes, and broader implications, including ethical considerations. It employs a structured approach, detailing the project's objectives, technologies used, target audience, outcomes, societal impact, ethical considerations, and sustainability. The expected output includes summaries, lists, and analyses across specified sections. +Analyzes the societal impact of technology projects by breaking down their intentions, outcomes, and broader implications, including ethical considerations. It employs a structured approach to evaluate the project's impact on society and its sustainability. The service outputs a comprehensive analysis, including a summary, technologies used, target audience, outcomes, societal impact, ethical considerations, sustainability, and an overall rating. ## analyze_threat_report -The prompt instructs a super-intelligent cybersecurity expert to analyze and extract key insights from cybersecurity threat reports. It emphasizes identifying new, interesting, and surprising information, and organizing these findings into concise, categorized summaries. The expected output includes a one-sentence summary, trends, statistics, quotes, references, and recommendations from the report, all formatted in plain language and without repetition. +The prompt instructs a super-intelligent cybersecurity expert to analyze and extract key insights from cybersecurity threat reports, focusing on new, interesting, and surprising information. It emphasizes creating concise, insightful summaries and lists of trends, statistics, quotes, references, and recommendations without using jargon. The expected output includes organized sections of extracted information, aiming for clarity and depth in understanding cybersecurity threats. ## analyze_threat_report_trends -Analyzes cybersecurity threat reports to identify up to 50 unique, surprising, and insightful trends. This process involves a deep, expert analysis to uncover new and interesting information. The expected output is a list of trends without repetition or formatting embellishments. +Analyzes cybersecurity threat reports to identify up to 50 unique, surprising, and insightful trends. This process involves a deep, expert-level examination of the content to uncover new and interesting findings. The output consists of a bulleted list highlighting these key trends without repetition or formatting embellishments. ## answer_interview_question -Generates tailored responses to technical interview questions, aiming for a casual yet insightful tone. The AI draws from a technical knowledge base and professional experiences to construct responses that demonstrate depth and alternative perspectives. Outputs are structured first-person responses, including context, main explanation, alternative approach, and evidence-based conclusion. +Generates tailored responses to technical interview questions, aiming for a casual yet insightful tone. The AI draws from a technical knowledge base and professional experiences to construct responses that demonstrate expertise and consider alternative approaches. Outputs are structured for verbal delivery, including context, main explanation, alternative approach, and evidence-based conclusion. ## ask_secure_by_design_questions -Generates a comprehensive set of security-focused questions tailored to the fundamental design of a specific project. This process involves deep analysis and conceptualization of the project's components and their security needs. The output includes a summary and a detailed list of security questions organized by themes. +Generates a comprehensive set of security-focused questions for ensuring a project's design is inherently secure. This process involves deep analysis and conceptualization of the project's components and their security needs. The output includes a summary and a prioritized list of security questions categorized by themes. ## capture_thinkers_work -Summarizes teachings and philosophies of notable individuals or philosophical schools, providing detailed templates on their backgrounds, ideas, and applications. It offers a structured approach to encapsulating complex thoughts into accessible summaries. The output includes encapsulations, background information, schools of thought, impactful ideas, primary teachings, works, quotes, applications, and life advice. +Summarizes teachings and philosophies of notable individuals or philosophical schools, providing detailed templates for each. It includes encapsulations, background, schools, impactful ideas, primary teachings, works, quotes, application, and life advice. The output offers a comprehensive overview of the subject's contributions and ideologies. ## check_agreement -The prompt outlines a process for analyzing contracts and agreements to identify potential issues or "gotchas." It involves summarizing the document, listing important aspects, categorizing issues by severity, and drafting responses for critical and important items. The expected output includes a concise summary, detailed callouts, categorized issues, and recommended responses in Markdown format. +Analyzes contracts and agreements to identify potential issues and summarize key points. This prompt focuses on extracting and organizing critical, important, and minor concerns for negotiation or reconsideration. The expected output includes a concise document summary, detailed callouts of significant stipulations, and structured recommendations for changes. ## clean_text -Summarizes and corrects formatting issues in text without altering the content. It focuses on removing odd line breaks to improve readability. The expected output is a clean, well-formatted version of the original text. +Summarizes and corrects formatting issues in text, focusing on removing odd line breaks and improving punctuation without altering content. This prompt emphasizes maintaining the original message while enhancing readability. The expected output is a cleaned, well-formatted version of the input text. ## coding_master -Explains coding concepts or languages to beginners, using examples from reputable sources and illustrating points with formatted code. The approach emphasizes clarity and accessibility, incorporating examples from Codeacademy and NetworkChuck. Outputs include markdown-formatted code and structured lists of ideas, recommendations, habits, facts, and insights, adhering to specific word counts. +The prompt instructs an expert coder to explain a specific coding concept or language to a beginner, using examples from reputable sources. It emphasizes teaching in an accessible manner and formatting code examples in markdown. The expected output includes structured Markdown content with specific sections for ideas, recommendations, habits, facts, and insights, each with a precise word count and quantity. ## compare_and_contrast -Compares and contrasts a list of items, focusing on their differences and similarities. The approach involves analyzing the items across various topics, organizing the findings into a markdown table. The expected output is a structured comparison in table format. +Compares and contrasts a list of items, focusing on their differences and similarities. The approach involves organizing the comparison into a markdown table format, with items on the left and topics at the top. The expected output is a structured table highlighting key comparisons. ## create_5_sentence_summary -Generates concise summaries or answers at five decreasing levels of depth. It involves deep understanding and thoughtful analysis of the input. The output is a structured list capturing the essence in 5, 4, 3, 2, and 1 word(s). +Generates concise summaries or answers at five varying depths. It involves deep understanding and thoughtful analysis of the input before producing a multi-layered summary. The output is a structured list of summaries, each with decreasing word count, capturing the essence of the input. ## create_academic_paper -Produces high-quality, authoritative Latex academic papers with clear concept explanations. It focuses on logical layout and simplicity while maintaining a professional appearance. The expected output is LateX code formatted in a two-column layout with a header and footer. +The prompt instructs on creating high-quality, authoritative academic papers in LaTeX, emphasizing clear concept explanations. It focuses on producing logically structured, visually appealing documents using a two-column layout. The expected output is LaTeX code tailored for academic publications. ## create_ai_jobs_analysis -Analyzes job reports to identify roles least and most vulnerable to automation, offering strategies for enhancing job security. It leverages historical insights to predict automation's impact on various job categories. The output includes a detailed analysis and recommendations for resilience against automation. +Analyzes job reports to identify roles at risk from automation and offers strategies for enhancing job security. It leverages historical insights to predict future trends. The output includes categorized job vulnerability levels and personalized resilience recommendations. ## create_aphorisms -Generates a list of 20 aphorisms related to the given topic(s), ensuring variety in their beginnings. It focuses on sourcing quotes from real individuals. The output includes each aphorism followed by the name of the person who said it. +Generates a list of 20 aphorisms related to the given topic(s), each attributed to its original author. It avoids starting all entries with the input keywords, ensuring variety. The output is a curated collection of wise sayings from various individuals. ## create_art_prompt -The prompt guides an expert artist in conceptualizing and instructing AI to create art that perfectly encapsulates a given concept. It emphasizes deep thought on the concept and its visual representation, aiming for compelling and interesting artwork. The expected output is a 100-word description that not only instructs the AI on what to create but also how the art should evoke feelings and suggest style through examples. +The prompt guides an expert artist and AI whisperer to conceptualize and instruct AI to create art that perfectly encapsulates a given concept. It emphasizes deep thought on the concept and its visual representation, aiming for compelling and interesting artwork. The expected output is a detailed description of the concept, visual representation, and direct instructions for the AI, including style cues for the artwork. ## create_better_frame -The essay explores the concept of framing as a way to construct and interpret reality through different lenses, emphasizing the power of perspective in shaping one's experience of the world. It highlights various dichotomies in perceptions around topics like AI, race/gender, success, personal identity, and control over life, illustrating how different frames can lead to vastly different outlooks and outcomes. The author argues for the importance of choosing positive frames to improve individual and collective realities, suggesting that changing frames can change outcomes and foster more positive social dynamics. +The essay discusses the concept of framing as a way to construct and interpret reality through specific lenses, emphasizing the power of positive framing to shape one's experience and outcomes in life. It highlights the importance of choosing frames that are positive and productive, as these can significantly influence one's perception of reality and, consequently, their actions and results. The expected output is an understanding of how different frames can lead to vastly different interpretations of the same reality and the encouragement to adopt more positive frames to improve one's life and societal dynamics. ## create_coding_project -Generates wireframes and starter code for coding projects based on user ideas. It specifically caters to transforming ideas into actionable project outlines and code skeletons, including detailed steps and file structures. The output includes project summaries, structured directories, and initial code setups. +Generates wireframes and starter code for coding projects based on user ideas. This tool takes a coding idea as input and outputs a detailed project plan, including wireframes, code structure, and setup instructions. The expected output includes project summaries, steps for development, file structure, and code for initializing the project. ## create_command -Generates specific command lines for various penetration testing tools based on a brief description of the desired outcome. This approach leverages the tool's help documentation to ensure accuracy and relevance. The expected output is a precise command that aligns with the user's objectives for the tool. +Generates specific command lines for various penetration testing tools based on a brief description of the desired outcome. This approach leverages the tool's help documentation to ensure accuracy and relevance of the generated commands. The expected output is a precise command line that can be executed to achieve the user's specified goal with the tool. ## create_cyber_summary -The prompt instructs on creating a comprehensive summary of cybersecurity threats, vulnerabilities, incidents, and malware for a technical audience. It emphasizes deep understanding through repetitive analysis and visualization techniques. The expected output includes a concise summary and categorized lists of cybersecurity issues. +The prompt instructs on creating a comprehensive summary of cybersecurity threats, vulnerabilities, incidents, and malware, emphasizing a detailed and iterative analysis process. It outlines a unique, mentally visual approach for organizing and understanding complex information. The expected output includes a concise summary and categorized lists of cybersecurity issues. ## create_git_diff_commit -This prompt provides instructions for using specific Git commands to manage code changes. It explains how to view differences since the last commit and display the current state of the repository. The expected output is a guide on executing these commands. +Provides instructions for using specific Git commands to manage code changes. It explains how to view differences since the last commit and display the latest commit details. The expected output includes command usage examples. + +## create_graph_from_input +Creates progress over time graphs for a security program, focusing on improvement metrics. It involves analyzing data to identify trends and outputting a CSV file with specific fields. The expected output is a CSV file detailing the program's progress over time. + +## create_hormozi_offer +The AI is designed to create business offers based on Alex Hormozi's "$100M Offers" strategies, aiming to craft irresistible deals. It integrates Hormozi's principles, focusing on value, pricing, guarantees, and market targeting. The expected output includes a detailed analysis of potential business offers, highlighting their unique value propositions. ## create_idea_compass -Guides users in developing a structured exploration of ideas through a detailed template. It emphasizes clarity and organization by breaking down the process into specific steps, including defining, supporting, and contextualizing the idea. The expected output is a comprehensive summary with related ideas, evidence, and sources organized in a structured format. +The prompt guides users in organizing and analyzing an idea or question through a structured template. It emphasizes detailed exploration, including definitions, evidence, sources, and examining similarities, opposites, themes, and consequences. The expected output is a comprehensive summary with organized sections and tags. ## create_investigation_visualization -Creates detailed GraphViz visualizations to illustrate complex intelligence investigations and data insights. This approach involves extensive analysis, organizing information, and visual representation using shapes, colors, and labels for clarity. The output includes a comprehensive diagram and analytical conclusions with a certainty rating. +Creates detailed GraphViz visualizations to illustrate complex intelligence investigations and data. This approach involves extensive analysis and organization of information to produce clear, annotated diagrams. The output includes a visual representation and analytical conclusions with a certainty rating. ## create_keynote -The prompt guides in creating TED-quality keynote presentations from provided input, focusing on narrative flow and practical takeaways. It outlines steps for structuring the presentation into slides with concise bullet points, images, and speaker notes. The expected output includes a story flow, the final takeaway, and a detailed slide deck presentation. +The prompt guides in creating TED-quality keynote presentations from provided input, focusing on narrative flow and practical takeaways. It outlines steps for structuring the presentation into slides with concise bullet points, images, and speaker notes. The expected output includes a story flow, the final takeaway, and a detailed slide deck. ## create_logo -Generates simple, minimalist company logos based on provided input, focusing on elegance and impact without text. The approach emphasizes super minimalist designs. The output is a prompt for an AI image generator to create a simple, vector graphic logo. +Generates simple and elegant company logos based on provided input, focusing on minimalist designs without text. The approach emphasizes creating vector graphic logos that capture the essence of the input. The expected output is a prompt for an AI image generator to create a minimalist logo. ## create_markmap_visualization -Transforms complex ideas into visual formats using MarkMap syntax for easy understanding. This process involves simplifying concepts to ensure they can be effectively represented within the constraints of MarkMap. The output is a MarkMap syntax diagram that visually communicates the core ideas. +Transforms complex ideas into visual diagrams using MarkMap syntax. This process involves simplifying concepts to ensure they can be effectively represented in a visual format. The output is a MarkMap syntax diagram that visually communicates the core ideas. ## create_mermaid_visualization -Transforms complex ideas into simplified Mermaid (Markdown) visual diagrams. This process involves creating detailed visualizations that can independently explain concepts using Mermaid syntax, focusing on clarity and comprehensibility. The expected output is a Mermaid syntax diagram accompanied by a concise visual explanation. +This prompt instructs on creating visualizations for complex ideas using Mermaid syntax in Markdown. It emphasizes producing standalone diagrams that fully convey concepts through intricate designs. The expected output is a Mermaid syntax diagram accompanied by a visual explanation. ## create_micro_summary -Summarizes content into a Markdown formatted summary, focusing on brevity and clarity. It emphasizes creating concise, impactful points and takeaways. The output includes a one-sentence summary, main points, and key takeaways, each adhering to strict word limits. +The prompt instructs on summarizing content into a structured Markdown format. It emphasizes conciseness and clarity, focusing on a single sentence summary, main points, and key takeaways. The expected output is a well-organized, bullet-pointed list highlighting the essence of the content. ## create_network_threat_landscape -Analyzes open ports and services from network scans to identify security risks and provide recommendations. This process involves a detailed examination of port and service statistics to uncover potential vulnerabilities. The expected output is a markdown formatted threat report with sections on description, risk, recommendations, a concise summary, trends, and quotes from the analysis. +Analyzes open ports and services from network scans to identify security risks and provide recommendations. This process involves a detailed examination of port and service statistics to uncover potential vulnerabilities. The output includes a threat report with descriptions of open ports, risk assessments, recommendations for mitigation, a concise summary, and insights into trends and notable quotes from the analysis. ## create_npc -Generates detailed NPCs for D&D 5th edition, incorporating a wide range of characteristics from background to appearance. It emphasizes creativity in developing a character's backstory, traits, and goals. The output is a comprehensive character profile suitable for gameplay. +Generates detailed NPCs for D&D 5th edition, incorporating creative input to ensure a rich character profile. This process includes a comprehensive set of attributes, from background and flaws to goals and peculiarities, aiming for a fully fleshed-out character sheet. The expected output is a clear, detailed NPC profile suitable for immediate use in gameplay. ## create_pattern -The AI assistant is designed to interpret and respond to LLM/AI prompts with structured outputs. It specializes in organizing and analyzing prompts to produce responses that adhere to specific instructions and formatting requirements. The assistant ensures accuracy and alignment with the intended outcomes through meticulous analysis. +Interprets and responds to LLM/AI prompts based on specific instructions and examples. This AI assistant excels in organizing and analyzing prompts to produce accurately structured responses. The output is expected to align perfectly with the formatting and content requirements provided. ## create_quiz -Generates questions for reviewing learning objectives based on provided subject and objectives. It requires defining the subject and learning objectives for accurate question generation. The output consists of questions aimed at helping students review key concepts. +Generates questions for learners to review key concepts based on provided learning objectives. It requires subject and learning objectives as input for accurate question generation. The output consists of questions aimed at helping students understand the main concepts. ## create_reading_plan -Designs a tailored three-phase reading plan based on user input, focusing on an author or specific guidance. It carefully selects books from various sources, including hidden gems, to enhance the user's knowledge on the topic. The output includes a concise plan summary and categorized reading lists with reasons for each selection. +Designs a tailored three-phase reading plan based on user input, focusing on an author or specific request. It carefully selects books, considering both popularity and hidden gems, to enhance the user's knowledge on the topic. The output includes a brief introduction, a structured reading plan across three phases, and a summary. ## create_report_finding -The prompt instructs the creation of a detailed markdown security finding report, incorporating sections like Description, Risk, Recommendations, and others, based on a vulnerability title and explanation provided by the user. It emphasizes a structured, insightful approach to documenting cybersecurity vulnerabilities. The expected output is a comprehensive report with specific sections, focusing on clarity, insightfulness, and relevance to cybersecurity assessment. +The prompt instructs the creation of a detailed markdown security finding for a cyber security assessment report, covering sections like Description, Risk, Recommendations, References, One-Sentence-Summary, Trends, and Quotes based on a provided vulnerability title and explanation. It emphasizes a structured, insightful approach without reliance on bullet points for certain sections and requires the extraction of key recommendations, trends, and quotes. The expected output is a comprehensive, informative document tailored for inclusion in a security assessment report. ## create_security_update -The prompt instructs on creating concise security updates for newsletters, focusing on cybersecurity developments, threats, advisories, and new vulnerabilities. It emphasizes brevity and relevance, requiring links to further information. The expected output includes structured sections with short descriptions and relevant details, aiming to inform readers about the latest security concerns efficiently. +The prompt instructs on creating concise security updates for newsletters, focusing on cybersecurity developments, threats, advisories, and new vulnerabilities. It emphasizes organizing content into specific sections with brief descriptions and links for further information. The expected output includes a structured summary of cybersecurity issues with links to detailed sources. ## create_show_intro -Creates compelling short intros for podcasts, focusing on the most interesting aspects of the show. It involves listening to the entire show, identifying key topics, and highlighting them in a concise introduction. The output is a structured intro that teases the conversation's main points. +The prompt guides in creating compelling short intros for podcasts, focusing on highlighting the most interesting topics discussed. It emphasizes selecting novel and surprising elements from the show for the intro. The expected output is a concise, engaging introduction mentioning up to ten key discussion topics. + +## create_sigma_rules +Extracts Tactics, Techniques, and Procedures (TTPs) from security news publications to create YAML-based Sigma rules for host-based detection. These rules focus on detecting cybersecurity threats using tools like Sysinternals: Sysmon, PowerShell, and Windows logs. The output includes well-documented Sigma rules in YAML format, each separated by headers and footers. ## create_stride_threat_model -The prompt instructs on creating a detailed threat model using the STRIDE per element methodology for a given system design document. It emphasizes understanding the system's assets, trust boundaries, and data flows to identify and prioritize potential threats. The expected output is a comprehensive table listing threats, their components, mitigation strategies, and risk assessments. +The prompt instructs on creating a detailed threat model using the STRIDE per element methodology for a given system design document. It emphasizes understanding the system's assets, trust boundaries, and data flows to identify and prioritize potential threats. The expected output is a comprehensive table categorizing threats, their mitigation strategies, and assessing their risk severity. ## create_summary -Summarizes content into a structured Markdown format, focusing on brevity and clarity. It emphasizes creating a concise summary, listing main points, and identifying key takeaways. The output is organized into specific sections for easy reference. +The prompt instructs on summarizing content into a structured Markdown format. It emphasizes creating concise, informative summaries with specific sections for a one-sentence summary, main points, and key takeaways. The expected output is a neatly organized summary with clear, distinct sections. + +## create_tags +The prompt instructs to identify and output tags from text content for use in mind mapping tools, focusing on extracting at least five subjects or ideas. It emphasizes including any authors or existing tags, converting spaces in tags to underscores, and ensuring all tags are in lowercase without repetition. The expected output is a single line of space-separated, lowercase tags relevant to the text's content. ## create_threat_model -The prompt outlines a comprehensive approach to everyday threat modeling, emphasizing its application beyond technical defenses to include personal and physical security scenarios. It distinguishes between realistic and possible threats, advocating for a balanced approach to risk management that considers the value of what's being protected, the likelihood of threats, and the cost of controls. The expected output involves creating threat models for various scenarios, highlighting realistic defenses, and guiding individuals towards logical security decisions through structured analysis. +The prompt instructs on creating narrative-based threat models for various scenarios, emphasizing realistic risk assessment over improbable dangers. It highlights the importance of distinguishing between possible and likely threats, focusing defense efforts on the latter. The expected output includes a structured threat model and an analysis section guiding logical defense choices against identified scenarios. ## create_threat_scenarios -The prompt seeks to identify and prioritize potential threats to a given system or situation, using a narrative-based, simple threat modeling approach. It emphasizes distinguishing between realistic and possible threats, focusing on those worth defending against. The expected output includes a list of prioritized threat scenarios, an analysis of the threat model, recommended controls, a narrative analysis, and a concise conclusion. +The prompt aims to create narrative-based, simple threat models for various security concerns, ranging from physical to cybersecurity. It emphasizes a realistic approach to identifying and prioritizing potential threats based on likelihood and impact. The expected output includes a detailed analysis of threat scenarios, a logical explanation of the threat modeling process, recommended controls, and a narrative analysis that injects realism into the assessment of risks. ## create_upgrade_pack -Extracts and organizes insights on world models and task algorithms from provided content. It focuses on identifying and categorizing beliefs about the world and optimal task execution strategies. The output includes concise, actionable bullet points under relevant categories. +The prompt instructs on extracting and updating world models and task algorithms from given content. It emphasizes deep thinking to identify beliefs about the world and how tasks should be performed. The expected output includes concise bullet points summarizing these beliefs and task strategies, organized into relevant categories. ## create_video_chapters -Extracts and organizes the most engaging topics from a transcript with corresponding timestamps. This process involves a detailed review of the transcript to identify key moments and subjects. The output is a list of topics with their timestamps in a sequential format. +Extracts and timestamps the most interesting topics from a transcript, simulating the experience of watching the video. It focuses on identifying key subjects and moments, then matching them with precise timestamps. The output is a list of topics with sequential timestamps within the video's length. ## create_visualization -Transforms complex ideas into simplified ASCII art visualizations. This approach focuses on distilling intricate concepts into visual forms that can be easily understood through ASCII art. The expected output is a detailed ASCII art representation accompanied by a concise visual explanation. +Transforms complex ideas into simplified ASCII art visualizations. This approach allows for intricate concepts to be understood visually through detailed ASCII diagrams. The output is a standalone ASCII art piece, accompanied by a concise visual explanation. ## explain_code -Analyzes and explains code, security tool outputs, or configuration texts, tailoring the explanation to the type of input. It uses specific sections to clarify the function, implications, or settings based on the input's nature. The expected output is a detailed explanation or answer in designated sections. +The prompt instructs an expert coder to analyze and explain code, security tool outputs, or configuration texts. It emphasizes a flexible approach to achieving the best explanation. The expected output is categorized explanations or answers to specific questions, tailored to the type of input provided. ## explain_docs -The prompt instructs on transforming input about tool usage into improved, structured documentation. It emphasizes clarity and utility, breaking down the process into specific sections for a comprehensive guide. The expected output includes an overview, usage syntax, common use cases, and key features of the tool. +Improves instructions for using tools or products by providing a structured format. This approach breaks down the explanation into what the tool does, why it's useful, how to use it, common use cases, and key features. The expected output includes simplified, better-organized instructions. ## explain_project -Summarizes project documentation into a concise, user and developer-focused summary, highlighting its purpose, problem addressed, approach, installation, usage, and examples. It simplifies complex information for easy understanding and application. The output includes a project overview, problem it addresses, approach to solving the problem, and practical steps for installation and usage. +The prompt instructs on summarizing project documentation into a structured, user-friendly format. It emphasizes understanding the project, then distilling this understanding into concise summaries and practical steps for installation and usage. The output includes a project overview, problem addressed, approach to solving the problem, and clear instructions for installation and usage, all aimed at making the project accessible to users and developers. ## explain_terms -Produces a glossary of advanced terms found in specific content, including definitions and analogies. It focuses on explaining obscure or complex terms to aid understanding. The output is a list of terms with explanations and analogies in a structured Markdown format. +The prompt aims to create glossaries for complex terms within a given content, enhancing comprehension. It focuses on identifying and explaining advanced terms, excluding basic ones, to aid in understanding the content. The expected output is a list of advanced terms with definitions, analogies, and their significance, formatted in Markdown. + +## export_data_as_csv +The prompt instructs the AI to identify and format data structures from the input into a CSV file. It emphasizes understanding the context and accurately naming fields based on the input. The expected output is a CSV file containing all identified data structures. ## extract_algorithm_update_recommendations -Analyzes input to provide concise recommendations for improving processes. It focuses on extracting actionable advice from content descriptions. The output consists of a bulleted list of up to three brief suggestions. +Analyzes input to provide concise, actionable recommendations for improving processes within content. It focuses on extracting practical steps to enhance algorithms or methodologies. The output consists of a bulleted list of up to three brief suggestions. ## extract_article_wisdom -Extracts key insights and valuable information from textual content, focusing on ideas, quotes, habits, and references. It aims to address the issue of information overload by providing a concise summary of the content's most meaningful aspects. The expected output includes summarized ideas, notable quotes, referenced materials, and habits worth adopting. +Extracts key insights and wisdom from textual content, aiming to address the issue of information overload and the challenge of retaining valuable information. It uniquely identifies and organizes ideas, quotes, references, habits, and recommendations from a wide range of texts. The expected output includes summarized ideas, notable quotes, relevant references, and actionable habits. ## extract_book_ideas -Summarizes a book's key content by extracting 50 to 100 of its most interesting ideas. The process involves a deep dive into the book's insights, prioritizing them by interest and insightfulness. The output is a concise list of bulleted ideas, limited to 20 words each. +Summarizes a book's key content by extracting 50 to 100 of its most insightful, surprising, and interesting ideas. The process involves a deep recall of the book's details, prioritizing the ideas by their impact. The output is formatted as a bulleted list, limited to 20 words per idea. ## extract_book_recommendations -Summarizes a book's key content by extracting 50 to 100 of its most practical recommendations, prioritizing the most impactful advice. This process involves a thorough memory search to identify actionable insights. The output is formatted as an instructive, bullet-pointed list, limited to 20 words each. +Summarizes a book's key content by extracting 50 to 100 of its most practical recommendations. The approach focuses on actionable advice, prioritizing the most impactful suggestions first. The output is a Markdown-formatted list of instructive recommendations, capped at 20 words each. ## extract_business_ideas -The prompt outlines a process for identifying and elaborating on innovative business ideas. It focuses on extracting top business concepts from provided content and then refining the best ten by exploring adjacent possibilities. The expected output includes two sections: a list of extracted ideas and a detailed elaboration on the top ten ideas, ensuring uniqueness and differentiation. +Extracts and elaborates on top business ideas from provided content, focusing on those with potential to revolutionize industries. This assistant first identifies all notable business concepts, then selects and expands on the ten most promising ones, ensuring uniqueness and differentiation. The output includes a list of extracted ideas and a detailed elaboration on the top ten. + +## extract_controversial_ideas +Identifies and lists controversial statements from inputs. This AI system focuses on extracting contentious ideas and quotes, presenting them in a structured Markdown format. The expected output includes sections for controversial ideas and supporting quotes, each with specific content guidelines. ## extract_extraordinary_claims -Identifies and lists extraordinary claims from conversations, focusing on those rejected by the scientific community or based on misinformation. The process involves deep analysis to pinpoint statements that defy accepted scientific truths, such as denying evolution or the moon landing. The output is a detailed list of quotes, ranging from 50 to 100, showcasing these claims. +The prompt instructs to identify and list extraordinary claims from conversations, focusing on those rejected by the scientific community or based on misinformation. It emphasizes capturing statements that defy accepted scientific truths, such as evolution or the moon landing. The expected output is a detailed list of at least 50 to no more than 100 specific quotes showcasing these claims. ## extract_ideas -Extracts and condenses insightful ideas from text into 15-word bullet points focusing on life's purpose and human progress. This process emphasizes capturing unique insights on specified themes. The output consists of a list of concise, thought-provoking ideas. +This prompt extracts insightful and interesting information from text, focusing on life's purpose and human progress. It emphasizes creating concise bullet points to summarize key ideas. The expected output includes a list of insightful ideas, each precisely 15 words long. ## extract_insights -Extracts and condenses complex insights from text on profound topics into 15-word bullet points. This process emphasizes the extraction of nuanced, powerful ideas related to human and technological advancement. The expected output is a concise list of abstracted, insightful bullets. +The prompt instructs on extracting and summarizing powerful insights from text, focusing on life's purpose and human-technology interaction. It emphasizes creating concise, insightful bullet points from the content. The expected output is a list of abstracted, wise insights, each precisely 15 words long. ## extract_main_idea -Extracts and highlights the most crucial or intriguing idea from any given content. This prompt emphasizes a methodical approach to identify and articulate the essence of the input. The expected output includes a concise main idea and a recommendation based on that idea. +The prompt instructs on extracting and presenting the most significant idea from any given content. It emphasizes a structured approach to identify and recommend actions based on the extracted idea. The expected output includes a concise main idea and recommendation, each in a 15-word sentence. ## extract_patterns -The prompt guides in identifying and analyzing recurring, surprising, or insightful patterns from a collection of ideas, data, or observations. It emphasizes extracting the most notable patterns based on their frequency and significance, and then documenting the process of discovery and analysis. The expected output includes a detailed summary of patterns, an explanation of their selection and significance, and actionable advice for startup builders based on these insights. +The prompt instructs on identifying and analyzing patterns from a collection of ideas, data, or observations, focusing on those that are most surprising or frequently mentioned. It outlines a structured approach to extract, weigh, and document these patterns, including a detailed analysis and advice for builders in the startup space. The expected output includes sections for patterns, meta-analysis, a summary analysis, the top five patterns, and advice for builders, all formatted as bullet points with specific word limits. ## extract_poc -Analyzes security or bug bounty reports to extract and provide proof of concept URLs for validating vulnerabilities. It specializes in identifying actionable URLs and commands from the reports, ensuring direct verification of reported vulnerabilities. The output includes the URL with a specific command to execute it, like using curl or python. +Analyzes security or bug bounty reports to extract and provide proof of concept URLs for validating vulnerabilities. It uniquely identifies URLs that can directly verify the existence of vulnerabilities, accompanied by the necessary command to execute them. The output includes a command followed by the URL or file to validate the vulnerability. ## extract_predictions -Extracts and organizes predictions from content into a structured format. It focuses on identifying specific predictions, their timelines, confidence levels, and verification methods. The expected output includes a bulleted list and a detailed table of these predictions. +The prompt instructs on extracting and organizing predictions from given content. It details a process for identifying specific predictions, their expected fulfillment dates, confidence levels, and verification methods. The expected output includes a bulleted list of predictions and a structured table summarizing these details. ## extract_questions -Extracts questions from content and analyzes their effectiveness in eliciting high-quality responses. It focuses on identifying the elements that make these questions particularly insightful. The expected output includes a list of questions, an analysis of their strengths, and recommendations for interviewers. +Extracts questions from content and analyzes their effectiveness in eliciting surprising, high-quality answers. It focuses on identifying the elements that make these questions outstanding. The output includes listed questions, an analysis of their brilliance, and recommendations for interviewers. ## extract_recommendations -Extracts and condenses recommendations from content into a concise list. This process involves identifying both explicit and implicit advice within the given material. The output is a bulleted list of up to 20 brief recommendations. +Extracts and condenses practical recommendations from content into a concise list. This process involves identifying explicit and implicit advice within the material. The output consists of a bulleted list of up to 20 brief recommendations. ## extract_references -Extracts references to various forms of cultural and educational content from provided text. This process involves identifying and listing references to art, literature, and academic papers concisely. The expected output is a bulleted list of up to 20 references, each summarized in no more than 15 words. +Extracts references to various forms of art and literature from content, compiling them into a concise list. This process involves identifying and listing up to 20 references, ensuring each is succinctly described in no more than 15 words. The output is a bulleted list of references to art, stories, books, literature, papers, and other sources of learning. ## extract_song_meaning -Analyzes and interprets the meaning of songs based on extensive research and lyric examination. This process involves deep analysis of the artist's background, song context, and lyrics to deduce the song's essence. Outputs include a summary sentence, detailed meaning in bullet points, and evidence supporting the interpretation. +Analyzes and interprets the meaning of songs based on lyrics, artist context, and other relevant information. This process involves extensive research and deep analysis of the lyrics. The output includes a summary sentence, detailed bullet points on the song's meaning, and evidence supporting the interpretation. ## extract_sponsors -Identifies and distinguishes between official and potential sponsors from transcripts. This process involves analyzing content to separate actual sponsors from merely mentioned companies. The output lists official sponsors and potential sponsors based on their mention in the content. +Identifies and categorizes sponsors and potential sponsors from transcripts. It discerns between actual sponsors and mere mentions, aiming for accurate sponsor identification. The output lists official and potential sponsors with descriptions and links. ## extract_videoid -Extracts video IDs from URLs for use in other applications. It meticulously analyzes the URL to isolate the video ID. The output is solely the video ID, with no additional information or errors included. +Extracts video IDs from URLs for use in other applications. It meticulously analyzes the URL to locate the specific part that contains the video ID. The output is solely the video ID, with no additional information or formatting. ## extract_wisdom -Extracts key insights, ideas, quotes, habits, and references from textual content to address the issue of information overload and the challenge of retaining knowledge. It uniquely filters and condenses valuable information from various texts, making it easier for users to decide if the content warrants a deeper review or to use as a note-taking alternative. The output includes summarized ideas, notable quotes, relevant habits, and useful references, all aimed at enhancing understanding and retention. +Extracts key insights from textual content to address the issue of information overload and memory retention. It uniquely identifies ideas, quotes, references, habits, and recommendations from a wide range of texts. The output includes summarized content, highlighting valuable takeaways and actionable items. ## extract_wisdom_agents -This prompt outlines a complex process for extracting insights from text content, focusing on themes like the meaning of life and technology's impact on humanity. It involves creating teams of AI agents with diverse expertise to analyze the content and produce summaries, ideas, insights, quotes, habits, facts, references, and recommendations. The expected output includes structured sections filled with concise, insightful entries derived from the input material. +The prompt outlines a complex process for extracting insights from text content, focusing on themes like the meaning of life and technology's impact on humanity. It describes creating teams of AI agents with diverse expertise to summarize content, identify key ideas, insights, quotes, habits, facts, references, and recommendations, and distill a one-sentence takeaway. The expected output includes summaries and lists of insights and recommendations, all structured to highlight the most valuable aspects of the input material. ## extract_wisdom_dm -Extracts and synthesizes valuable content from input text, focusing on insights related to life's purpose and human advancement. It employs a structured approach to distill surprising ideas, insights, quotes, habits, facts, and recommendations from the content. The output includes summaries, ideas, insights, and other categorized information for deep understanding and practical application. +The prompt outlines a comprehensive process for extracting and organizing valuable content from input text, focusing on insights related to life's purpose, human flourishing, and technology's impact. It emphasizes a detailed, step-by-step approach to identify ideas, insights, quotes, habits, facts, references, and recommendations from the content. The expected output includes summaries, lists of ideas, insights, quotes, habits, facts, references, and a one-sentence takeaway, all formatted in Markdown and adhering to specific word counts and item quantities. + +## extract_wisdom_large +The purpose is to extract and distill key insights, ideas, habits, facts, and recommendations from a detailed conversation about writing, communication, and the iterative process of creating content. The nuanced approach involves identifying the essence of effective communication, the importance of authenticity in writing, and the value of distillation in conveying ideas. The expected output includes categorized summaries of ideas, insights, habits, facts, recommendations, and more, all aimed at enhancing understanding and application of the discussed principles in writing and communication. ## extract_wisdom_nometa -This prompt guides the extraction and organization of insightful content from text, focusing on life's purpose, human flourishing, and technology's impact. It emphasizes identifying and summarizing surprising ideas, refined insights, practical habits, notable quotes, valid facts, and useful recommendations related to these themes. The expected output includes structured sections for summaries, ideas, insights, quotes, habits, facts, recommendations, and references, each with specific content and formatting requirements. +The prompt instructs on extracting and organizing various insights, ideas, quotes, habits, facts, recommendations, and references from text content focused on life's purpose, human flourishing, and the impact of technology and AI. It emphasizes the discovery of surprising and insightful information within these themes. The output is structured into sections for summary, ideas, insights, quotes, habits, facts, references, and recommendations, with specific instructions on the length and format for each entry. ## find_hidden_message -Analyzes political messages to reveal overt and hidden intentions. It employs knowledge of politics, propaganda, and psychology to dissect content, focusing on recent political debates. The output includes overt messages, hidden cynical messages, supporting arguments, desired audience actions, and analyses from cynical to favorable. +The prompt instructs the AI to analyze and interpret political messages in content, distinguishing between overt and hidden messages. It emphasizes a cynical evaluation, focusing on underlying political intentions and expected actions from the audience. The output includes structured analysis and summaries of both overt and hidden messages, supported by arguments and desired audience actions, concluding with various levels of analysis from cynical to favorable. ## find_logical_fallacies -Identifies and categorizes various fallacies in arguments or texts. This prompt focuses on recognizing invalid or faulty reasoning across a wide range of fallacies, from formal to informal types. The expected output is a list of identified fallacies with brief explanations. +The prompt instructs the AI to identify various types of fallacies from a given text, using a comprehensive list of fallacies as a reference. It emphasizes the importance of recognizing invalid or faulty reasoning in arguments. The expected output is a list of identified fallacies, each described concisely within a 15-word explanation, formatted under a "FALLACIES" section in Markdown. ## get_wow_per_minute -Evaluates the density of wow-factor in content by analyzing its surprise, novelty, insight, value, and wisdom. This process involves a detailed and varied consumption of the content to assess its potential to engage and enrich viewers. The expected output is a JSON report detailing scores and explanations for each wow-factor component and overall wow-factor per minute. +Evaluates the density of wow-factor in content, focusing on surprise, novelty, insight, value, and wisdom across various content types. It aims to quantify how rewarding content is based on these elements. The expected output is a JSON file detailing scores and explanations for each wow-factor component per minute. ## get_youtube_rss Generates RSS URLs for YouTube channels based on given channel IDs or URLs. It extracts the channel ID from the input and constructs the corresponding RSS URL. The output is solely the RSS URL. ## improve_academic_writing -This prompt aims to enhance the quality of text for academic purposes. It focuses on refining grammatical errors, improving clarity and coherence, and adopting an academic tone while ensuring ease of understanding. The expected output is a professionally refined text with a list of applied corrections. +This prompt aims to refine input text into an academic and scientific language, ensuring clarity, coherence, and ease of understanding. It emphasizes the use of formal English, avoiding repetition and trivial statements for a professional tone. The expected output is a text improved for academic purposes. ## improve_prompt -This service enhances LLM/AI prompts by applying expert prompt writing techniques to achieve better results. It leverages strategies like clear instructions, persona adoption, and reference text provision to refine prompts. The output is an improved version of the original prompt, optimized for clarity and effectiveness. +Enhances LLM/AI prompt quality by applying expert writing techniques, focusing on clarity, specificity, and structured instructions. It leverages strategies like clear instructions, persona adoption, and reference text provision to improve model responses. The service outputs refined prompts designed for optimal interaction with LLMs. ## improve_report_finding -The prompt instructs the creation of an improved security finding report from a penetration test, detailing the finding, risk, recommendations, references, a concise summary, and insightful quotes, all formatted in markdown without using markdown syntax or special formatting. It emphasizes a detailed, insightful approach to presenting cybersecurity issues and solutions. The output should be comprehensive, covering various sections including title, description, risk, recommendations, references, and quotes, aiming for clarity and depth in reporting. +Improves a security finding from a penetration test report by providing a detailed and enhanced report in markdown format, focusing on description, risk, recommendations, references, and summarizing the finding concisely. It emphasizes clarity, insightfulness, and actionable advice while avoiding jargon and repetition. The output includes a title, detailed description, risk analysis, insightful recommendations, relevant references, a concise summary, and notable quotes, all formatted for easy readability and immediate application. ## improve_writing -This prompt aims to refine input text for enhanced clarity, coherence, grammar, and style. It involves analyzing the text for errors and inconsistencies, then applying corrections while preserving the original meaning. The expected output is a grammatically correct and stylistically improved version of the text. +This prompt aims to refine and enhance input text for better clarity, coherence, grammar, and style. It involves analyzing the text for errors and inconsistencies, then applying corrections while preserving the original meaning. The expected output is a grammatically correct and stylistically improved version of the input text. ## label_and_rate -Evaluates and categorizes content based on its relevance to specific human-centric themes, then assigns a tiered rating and a numerical quality score. It uses a predefined set of labels for categorization and assesses content based on idea quantity and thematic alignment. The expected output is a structured JSON object detailing the content summary, labels, rating, and quality score with explanations. +The prompt outlines a process for evaluating content based on its relevance to specific human-centric themes, assigning labels from a predefined list, and rating its quality and thematic alignment. It emphasizes the importance of content's focus on human flourishing and meaning, penalizing content that is politically charged or unrelated to the core themes. The expected output is a structured JSON object summarizing the content's essence, its applicable labels, a tiered rating, and a numerical quality score, along with explanations for these assessments. ## official_pattern_template -The prompt outlines a complex process for diagnosing and addressing psychological issues based on a person's background and behaviors. It involves deep analysis of the individual's history, identifying potential mental health issues, and suggesting corrective actions. The expected output includes summaries of past events, possible psychological issues, their impact on behavior, and recommendations for improvement. +Analyzes a person's background and behaviors to diagnose psychological issues and recommend actions. It involves a detailed process of understanding the individual's history and current behavior to identify underlying problems. The output includes summaries of events, possible issues, behavior connections, and corrective recommendations. ## philocapsulate -Summarizes teachings of philosophers or philosophies, providing detailed templates on their background, encapsulated philosophy, school, teachings, works, quotes, application, and life advice. It differentiates between individual philosophers and philosophies with tailored templates for each. The output includes structured information for educational or analytical purposes. +The prompt instructs on creating detailed templates about philosophers or philosophies, including their background, teachings, and application. It specifies the structure for presenting information, such as encapsulating philosophies, listing works or teachings, and defining terms like "$philosopher-ian." The expected output is a comprehensive overview tailored to either an individual philosopher or a philosophy, highlighting key aspects and advice on living according to their teachings. ## provide_guidance -Provides comprehensive psychological advice tailored to the individual's specific question and context. This approach delves into the person's past, traumas, and life goals to offer targeted feedback and recommendations. The expected output includes a concise analysis, detailed scientific rationale, actionable recommendations, Esther Perel's perspective, self-reflection prompts, possible clinical diagnoses, and a summary, all aimed at fostering self-awareness and positive change. +Provides comprehensive psychological advice tailored to the individual's specific question and context. This approach combines elements of psychiatry, psychology, and life coaching, offering a structured analysis and actionable recommendations. The expected output includes a concise analysis, detailed scientific explanations, personalized recommendations, and self-reflection questions. ## rate_ai_response -Evaluates the quality of AI responses against the benchmark of human experts, assigning a letter grade and score. It involves deep analysis of both the instructions given to the AI and its output, comparing these to the potential performance of the world's best human expert. The process culminates in a detailed justification for the assigned grade, highlighting specific strengths and weaknesses of the AI's response. +Evaluates the quality of AI responses against the benchmark of the world's best human experts, focusing on understanding instructions, comparing AI output to optimal human performance, and rating the AI's work using a detailed grading system. The process involves deep analysis of both the instructions given to the AI and its response, followed by a structured evaluation that includes a letter grade, specific reasons for the grade, and a numerical score. The evaluation criteria emphasize comparison with human capabilities, ranging from expert to average performance. ## rate_ai_result -Evaluates the quality of AI-generated content based on construction, quality, and spirit. The process involves analyzing AI outputs against criteria set by experts and a high-IQ AI panel. The expected output is a final score out of 100, with deductions detailed for each category. +Evaluates the quality of AI-generated content based on construction, quality, and spirit. This process involves analyzing AI outputs against criteria set by experts and a high-IQ AI panel. The final output is a comprehensive score out of 100, reflecting the content's adherence to the prompt's requirements and essence. ## rate_content -The prompt outlines a process for evaluating content by labeling it with relevant single-word descriptors, rating its quality based on idea quantity and thematic alignment, and scoring it on a scale from 1 to 100. It emphasizes the importance of matching content with specific themes related to human meaning and the future of AI, among others. The expected output includes a list of labels, a tiered rating with an explanation, and an overall quality score with justification. +The prompt outlines a process for evaluating content by labeling it with relevant single-word descriptors and then rating its quality based on idea quantity and thematic alignment with specified themes. It emphasizes a nuanced approach to content assessment, combining quantitative and qualitative measures. The expected output includes a list of labels, a tiered rating with an explanation, and a numerical content score with justification. ## rate_value -This prompt seeks to acknowledge the collaborative effort behind its creation, inspired by notable figures in information theory and viral content creation. It highlights the fusion of theoretical foundations and modern digital strategies. The output is an attribution of credit. +The prompt aims to create content inspired by Claude Shannon's Information Theory and Mr. Beast's viral techniques. It leverages foundational communication theories and modern viral strategies for impactful content creation. The expected output is engaging and widely shareable content. ## raw_query -The prompt instructs the AI to produce the best possible output by thoroughly analyzing and understanding the input. It emphasizes deep contemplation of the input's meaning and the sender's intentions. The expected output is an optimal response tailored to the inferred desires of the input provider. +The prompt instructs the AI to produce the best possible output by thoroughly analyzing and understanding the input. It emphasizes deep contemplation of the input's meaning and the sender's intentions. The expected output is an optimal response tailored to the perceived desires of the prompt sender. ## recommend_artists -Recommends a personalized festival schedule featuring artists similar to the user's preferences in EDM genres and artists. The recommendation process involves analyzing the user's favorite styles and artists, then selecting similar artists and explaining the choices. The output is a detailed schedule organized by day, set time, stage, and artist, optimized for the user's enjoyment. +Recommends a personalized festival schedule featuring artists that match the user's preferred EDM styles and artists. The process involves analyzing the user's favorite styles and artists, then selecting similar artists and explaining the choices. The output is a day-by-day, set-time, and stage schedule optimized for the user's enjoyment. ## show_fabric_options_markmap -Create a visual representation of the functionalities provided by the Fabric project, focusing on augmenting human capabilities with AI. The approach involves breaking down the project's capabilities into categories like summarization, analysis, and more, with specific patterns branching from these categories. The expected output is comprehensive Markmap code detailing this functionality map. +Summarizes the Fabric project, an open-source framework designed to integrate AI into daily challenges through customizable prompts called Patterns. It emphasizes ease of use and adaptability, offering tools for a wide range of tasks from content summarization to creating AI art. The expected output includes a visual Markmap representation of Fabric's capabilities. -## suggest -Analyzes user input to suggest appropriate fabric commands or patterns, enhancing the tool's functionality. It involves understanding specific needs, determining suitable commands, and providing clear, user-friendly suggestions. The output includes command suggestions, explanations, and instructions for new patterns. +## suggest_pattern +Develops a feature for a fabric command-line tool to suggest appropriate commands or patterns based on user input. It involves analyzing requests, determining suitable commands, and providing clear suggestions. The output includes explanations or multiple options, aiming to enhance user accessibility. ## summarize -Summarizes content into a structured Markdown format, focusing on brevity and clarity. It extracts and lists the most crucial points and takeaways. The output includes a one-sentence summary, main points, and key takeaways, adhering to specified word limits. +The prompt instructs on summarizing content into a structured Markdown format. It emphasizes creating concise, informative summaries with specific sections for a one-sentence summary, main points, and key takeaways. The expected output is a neatly organized summary with clear, distinct sections. ## summarize_debate -Analyzes debates to identify and summarize the primary disagreements, arguments, and evidence that could change participants' minds. It breaks down complex discussions into concise summaries and evaluates argument strength, predicting outcomes. The output includes structured summaries and analyses of each party's position and evidence. +The prompt outlines a process for analyzing debates, focusing on identifying disagreements, arguments, and evidence that could change participants' minds. It emphasizes a structured approach to summarizing debates, including extracting key points and evaluating argument strength. The expected output includes summaries of the content, arguments, and evidence, along with an analysis of argument strength and predictions about the debate's outcome. ## summarize_git_changes -Summarizes major changes and upgrades in a GitHub project over the past week. It involves identifying key updates, then crafting a concise, enthusiastic summary and detailed bullet points highlighting these changes. The output includes a 20-word introduction and excitedly written update bullets. +Summarizes major changes and upgrades in a GitHub project over the past week. The approach involves creating a concise section titled "CHANGES" with bullet points limited to 10 words each. The expected output includes a 20-word introductory sentence and bullet points detailing the updates enthusiastically. ## summarize_git_diff -Analyzes Git diffs to summarize major changes and upgrades. It emphasizes creating concise bullet points for feature changes and updates, tailored to the extent of modifications. The expected output includes a 100-character intro sentence using conventional commits format. +Analyzes Git diffs to identify and summarize key changes and upgrades. This prompt focuses on creating concise, bullet-point summaries for project updates, using conventional commit messages. The expected output includes a brief intro sentence followed by bullet points detailing the changes. + +## summarize_lecture +Extracts and organizes key topics from a lecture transcript, providing structured summaries, definitions, and timestamps. This process involves a detailed review of the transcript to identify main subjects, create bullet points, and list definitions with corresponding video timestamps. The output includes a concise summary, a list of tools mentioned with descriptions, and a one-sentence takeaway, all formatted for easy readability. ## summarize_micro -Summarizes content into a structured Markdown format. This prompt focuses on concise, bullet-pointed summaries and takeaways. The output includes a one-sentence summary and lists of main points and takeaways. +The prompt instructs on summarizing content into a structured Markdown format. It emphasizes conciseness and clarity, focusing on a single sentence summary, main points, and key takeaways. The expected output is a well-organized, bullet-pointed list highlighting the essence of the content. ## summarize_newsletter -Extracts and organizes key content from newsletters, focusing on the most meaningful, interesting, and useful information. It uniquely parses the entire newsletter to provide concise summaries, lists of content, opinions, tools, companies, and follow-up actions. The output includes sections for a brief summary, detailed content points, author opinions, mentioned tools and companies, and recommended follow-ups in a structured Markdown format. +Extracts and organizes key content from newsletters into a structured, easy-to-navigate format. It focuses on summarizing, categorizing, and highlighting essential information, including opinions, tools, and companies mentioned. The output is a comprehensive breakdown of the newsletter's content for quick reference. ## summarize_paper -Summarizes academic papers by extracting key sections such as title, authors, main goals, and more from the provided text. It employs a structured approach to highlight the paper's core aspects including technical methodology, distinctive features, and experimental outcomes. The output is a detailed summary covering various dimensions of the research. +Generates a summary of an academic paper from its full text, focusing on key sections like title, authors, main goals, and findings. It uniquely structures the output into specific categories for clarity. The expected output includes sections on the paper's title, authors, main goal, technical approach, distinctive features, experimental results, advantages, limitations, and conclusion. -## summarize_pattern -This prompt instructs on summarizing AI chat prompts into concise paragraphs. It emphasizes using active voice and present tense for clarity. The expected output is a structured summary highlighting the prompt's purpose, approach, and anticipated results. +## summarize_prompt +This prompt instructs on summarizing AI chat prompts concisely. It emphasizes using active voice and present tense for clarity. The expected output is a succinct paragraph detailing the prompt's purpose, approach, and anticipated result. ## summarize_pull-requests -Summarizes pull requests for a coding project, focusing on the types of changes made. It involves creating a summary and a detailed list of main PRs, rewritten for clarity. The output includes a concise overview and specific examples of pull requests. +The prompt instructs on summarizing pull requests for a coding project, focusing on creating a summary and detailing top pull requests in a readable format. It emphasizes rewriting pull request items for clarity. The expected output includes a brief overview of the pull requests' nature and a list of major ones, rewritten for readability. ## summarize_rpg_session -This prompt outlines the process for summarizing in-person role-playing game sessions, focusing on key events, combat details, character development, and worldbuilding. It emphasizes capturing the essence of the session in a structured format, including summaries, lists, and descriptions to encapsulate the narrative and gameplay dynamics. The expected output includes a comprehensive overview of the session's storyline, character interactions, and significant moments, tailored for both players and observers. +Summarizes in-person role-playing game sessions, focusing on key events, combat details, character development, and worldbuilding. It transforms RPG transcripts into structured summaries, highlighting significant moments and character evolution. The output includes a heroic summary, detailed combat stats, MVPs, key discussions, character flaws, changes, quotes, humor, and worldbuilding insights. ## to_flashcards -Creates Anki cards from texts following specific principles to ensure simplicity, optimized wording, and no reliance on external context. This approach aims to enhance learning efficiency and comprehension without requiring prior knowledge of the text. The expected output is a set of questions and answers formatted as a CSV table. +Creates Anki cards from texts, adhering to principles of minimal information, optimized wording, and no external context. This approach ensures simplicity without losing essential details, aiming for quick and accurate recall. The output is a set of questions and answers formatted as a CSV table. ## tweet -Guides users on crafting engaging tweets with emojis, focusing on Twitter's basics and content creation strategies. It emphasizes understanding Twitter, identifying the target audience, and using emojis effectively. The expected output is a comprehensive guide for creating appealing tweets with emojis. +Guides users on crafting engaging tweets with emojis, starting from understanding Twitter basics to analyzing tweet performance. It emphasizes concise messaging, audience engagement, and the strategic use of emojis for personality and clarity. The expected output is enhanced tweeting skills and better audience interaction. ## write_essay -The task is to write an essay in the style of Paul Graham, focusing on the essence and approach of writing concise, clear, and illuminating essays on any given topic. +The purpose of this prompt is to generate an essay in the style of Paul Graham, focusing on a given topic while emulating his clear, simple, and conversational writing style. The essay should avoid cliches, jargon, and journalistic language, presenting ideas in a straightforward manner without common concluding phrases. + +## write_hackerone_report +Assists bug bounty hunters in writing reports for HackerOne by analyzing requests, responses, and comments to generate a structured report. It leverages the `bbReportFormatter` tool for formatting inputs, facilitating dynamic, plugin-integrated, or command-line report generation. The output is a HackerOne-ready report that can be fine-tuned with additional details. ## write_micro_essay -The task is to write an essay in the style of Paul Graham, focusing on the essence of simplicity in conveying complex ideas. +The purpose of this prompt is to generate an essay in the style of Paul Graham, focusing on the topic provided, using a simple, clear, and conversational style. The essay should avoid cliches, jargon, and journalistic language, aiming for a publish-ready piece that reflects Graham's approach to writing. The content should be concise, limited to 250 words, and exclude common concluding phrases or setup language. ## write_nuclei_template_rule -The purpose of this prompt is to guide the creation of Nuclei templates for cybersecurity applications, focusing on generating precise and efficient scanning templates for various protocols like HTTP, DNS, TCP, and more. It emphasizes the importance of incorporating elements such as matchers, extractors, and conditions to tailor the templates for detecting specific vulnerabilities or configurations. The expected output is a well-structured YAML Nuclei template that adheres to best practices in template creation, including handling dynamic data extraction, utilizing complex matchers, and ensuring accurate vulnerability detection with minimal false positives. +```yaml +id: vhost-enum-flow + +info: + name: vhost enum flow + author: tarunKoyalwar + severity: info + description: | + vhost enumeration by extracting potential vhost names from ssl certificate. + +flow: | + ssl(); + for (let vhost of iterate(template["ssl_domains"])) { + set("vhost", vhost); + http(); + } + +ssl: + - address: "{{Host}}:{{Port}}" + +http: + - raw: + - | + GET / HTTP/1.1 + Host: {{vhost}} + + matchers: + - type: dsl + dsl: + - status_code != 400 + - status_code != 502 + + extractors: + - type: dsl + dsl: + - '"VHOST: " + vhost + ", SC: " + status_code + ", CL: " + content_length' +``` ## write_pull-request -The prompt instructs on drafting a detailed pull request (PR) description based on the output of a `git diff` command, focusing on identifying and explaining code changes. It emphasizes analyzing changes, understanding their purpose, and detailing their impact on the project. The expected output is a structured PR description in markdown, covering a summary of changes, reasons, impacts, and testing plans in clear language. +The prompt instructs a software engineer to draft a detailed pull request description based on the output of a `git diff` command, which compares changes between the current branch and the main repository branch. It emphasizes analyzing the changes, understanding their purpose, and clearly documenting them in markdown format, including summaries, reasons, impacts, and testing plans. The expected output is a structured PR description that concisely communicates the modifications and their implications for the project. ## write_semgrep_rule -The prompt requests the creation of a Semgrep rule to detect a specific vulnerability pattern in code, based on provided context and examples. It emphasizes the importance of crafting a rule that is general enough to catch any instance of the described vulnerability, rather than being overly specific to the given examples. The expected output is a well-structured Semgrep rule that aligns with the syntax and guidelines detailed in the context, capable of identifying the vulnerability across different scenarios. +The prompt requests the creation of a Semgrep rule to detect a specific vulnerability pattern in code, based on provided context and examples. It emphasizes the importance of capturing the general case of the vulnerability rather than focusing solely on the specific instances mentioned. The expected output is a well-structured Semgrep rule that aligns with the syntax and capabilities outlined in the detailed Semgrep rule syntax guide, capable of identifying potential security issues in code. diff --git a/patterns/summarize/dmiessler/summarize/system.md b/patterns/summarize/dmiessler/summarize/system.md index 96ca8ae2..5fdb8003 100644 --- a/patterns/summarize/dmiessler/summarize/system.md +++ b/patterns/summarize/dmiessler/summarize/system.md @@ -21,5 +21,5 @@ Take a step back and think step by step about how to achieve the best result pos 1. You only output Markdown. 2. Do not give warnings or notes; only output the requested sections. 3. You use numbered lists, not bullets. -4. Do not repeat ideas, quotes, facts, or resources. +4. Do not repeat ideas, or quotes. 5. Do not start items with the same opening words. diff --git a/patterns/summarize/system.md b/patterns/summarize/system.md index 468efe5d..b655b620 100644 --- a/patterns/summarize/system.md +++ b/patterns/summarize/system.md @@ -8,7 +8,7 @@ Take a deep breath and think step by step about how to best accomplish this goal - Combine all of your understanding of the content into a single, 20-word sentence in a section called ONE SENTENCE SUMMARY:. -- Output the 10 most important points of the content as a list with no more than 15 words per point into a section called MAIN POINTS:. +- Output the 10 most important points of the content as a list with no more than 16 words per point into a section called MAIN POINTS:. - Output a list of the 5 best takeaways from the content in a section called TAKEAWAYS:. diff --git a/patterns/summarize_debate/system.md b/patterns/summarize_debate/system.md index 79ada6c2..6e8680d6 100644 --- a/patterns/summarize_debate/system.md +++ b/patterns/summarize_debate/system.md @@ -60,13 +60,10 @@ Find the evidence each party would accept to change their mind. - Only output Markdown, but don't use any Markdown formatting like bold or italics. - - Do not give warnings or notes; only output the requested sections. - You use bulleted lists for output, not numbered lists. -- Do not repeat ideas, quotes, facts, or resources. - - Do not start items with the same opening words. - Ensure you follow ALL these instructions when creating your output. diff --git a/patterns/summarize_meeting/system.md b/patterns/summarize_meeting/system.md index 835b08f8..841e2ed2 100644 --- a/patterns/summarize_meeting/system.md +++ b/patterns/summarize_meeting/system.md @@ -24,13 +24,13 @@ You are an AI assistant specialized in analyzing meeting transcripts and extract - Only output Markdown. -- Write the KEY POINTS bullets as exactly 15 words. +- Write the KEY POINTS bullets as exactly 16 words. -- Write the TASKS bullets as exactly 15 words. +- Write the TASKS bullets as exactly 16 words. -- Write the DECISIONS bullets as exactly 15 words. +- Write the DECISIONS bullets as exactly 16 words. -- Write the NEXT STEPS bullets as exactly 15 words. +- Write the NEXT STEPS bullets as exactly 16 words. - Use bulleted lists for all sections, not numbered lists. diff --git a/patterns/summarize_newsletter/system.md b/patterns/summarize_newsletter/system.md index 987fa89c..cad8ae42 100644 --- a/patterns/summarize_newsletter/system.md +++ b/patterns/summarize_newsletter/system.md @@ -6,9 +6,9 @@ Take a deep breath and think step-by-step about how to achieve the best output u 0. Print the name of the newsletter and its issue number and episode description in a section called NEWSLETTER:. -1. Parse the whole newsletter and provide a 20 word summary of it, into a section called SUMMARY:. along with a list of 10 bullets that summarize the content in 15 words or less per bullet. Put these bullets into a section called SUMMARY:. +1. Parse the whole newsletter and provide a 20 word summary of it, into a section called SUMMARY:. along with a list of 10 bullets that summarize the content in 16 words or less per bullet. Put these bullets into a section called SUMMARY:. -2. Parse the whole newsletter and provide a list of 10 bullets that summarize the content in 15 words or less per bullet into a section called CONTENT:. +2. Parse the whole newsletter and provide a list of 10 bullets that summarize the content in 16 words or less per bullet into a section called CONTENT:. 3. Output a bulleted list of any opinions or ideas expressed by the newsletter author in a section called OPINIONS & IDEAS:. diff --git a/patterns/t_analyze_challenge_handling/system.md b/patterns/t_analyze_challenge_handling/system.md new file mode 100644 index 00000000..78db3138 --- /dev/null +++ b/patterns/t_analyze_challenge_handling/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 8 16-word bullets describing how well or poorly I'm addressing my challenges. Call me out if I'm not putting work into them, and/or if you can see evidence of them affecting me in my journal or elsewhere. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_check_metrics/system.md b/patterns/t_check_metrics/system.md new file mode 100644 index 00000000..b50389ce --- /dev/null +++ b/patterns/t_check_metrics/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Check this person's Metrics or KPIs (M's or K's) to see their current state and if they've been improved recently. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_create_h3_career/system.md b/patterns/t_create_h3_career/system.md new file mode 100644 index 00000000..49fc10f9 --- /dev/null +++ b/patterns/t_create_h3_career/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Analyze everything in my TELOS file and think about what I could and should do after my legacy corporate / technical skills are automated away. What can I contribute that's based on human-to-human interaction and exchanges of value? + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_create_opening_sentences/system.md b/patterns/t_create_opening_sentences/system.md new file mode 100644 index 00000000..f62ce19a --- /dev/null +++ b/patterns/t_create_opening_sentences/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 4 32-word bullets describing who I am and what I do in a non-douchey way. Use the who I am, the problem I see in the world, and what I'm doing about it as the template. Something like: + a. I'm a programmer by trade, and one thing that really bothers me is kids being so stuck inside of tech and games. So I started a school where I teach kids to build things with their hands. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_describe_life_outlook/system.md b/patterns/t_describe_life_outlook/system.md new file mode 100644 index 00000000..b2dd9b34 --- /dev/null +++ b/patterns/t_describe_life_outlook/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 5 16-word bullets describing this person's life outlook. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_extract_intro_sentences/system.md b/patterns/t_extract_intro_sentences/system.md new file mode 100644 index 00000000..e0a5ea5a --- /dev/null +++ b/patterns/t_extract_intro_sentences/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 5 16-word bullets describing who this person is, what they do, and what they're working on. The goal is to concisely and confidently project who they are while being humble and grounded. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_extract_panel_topics/system.md b/patterns/t_extract_panel_topics/system.md new file mode 100644 index 00000000..917ff021 --- /dev/null +++ b/patterns/t_extract_panel_topics/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 5 48-word bullet points, each including a 3-5 word panel title, that would be wonderful panels for this person to participate on. +5. Write them so that they'd be good panels for others to participate in as well, not just me. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_find_blindspots/system.md b/patterns/t_find_blindspots/system.md new file mode 100644 index 00000000..d5d6adc6 --- /dev/null +++ b/patterns/t_find_blindspots/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 8 16-word bullets describing possible blindspots in my thinking, i.e., flaws in my frames or models that might leave me exposed to error or risk. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_find_negative_thinking/system.md b/patterns/t_find_negative_thinking/system.md new file mode 100644 index 00000000..d9932b99 --- /dev/null +++ b/patterns/t_find_negative_thinking/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 4 16-word bullets identifying negative thinking either in my main document or in my journal. +5. Add some tough love encouragement (not fluff) to help get me out of that mindset. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_find_neglected_goals/system.md b/patterns/t_find_neglected_goals/system.md new file mode 100644 index 00000000..696211ab --- /dev/null +++ b/patterns/t_find_neglected_goals/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 5 16-word bullets describing which of their goals and/or projects don't seem to have been worked on recently. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_give_encouragement/system.md b/patterns/t_give_encouragement/system.md new file mode 100644 index 00000000..057e68d6 --- /dev/null +++ b/patterns/t_give_encouragement/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 8 16-word bullets looking at what I'm trying to do, and any progress I've made, and give some encouragement on the positive aspects and recommendations to continue the work. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_red_team_thinking/system.md b/patterns/t_red_team_thinking/system.md new file mode 100644 index 00000000..18301c9e --- /dev/null +++ b/patterns/t_red_team_thinking/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 4 16-word bullets red-teaming my thinking, models, frames, etc, especially as evidenced throughout my journal. +5. Give a set of recommendations on how to fix the issues identified in the red-teaming. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_threat_model_plans/system.md b/patterns/t_threat_model_plans/system.md new file mode 100644 index 00000000..a4c6174a --- /dev/null +++ b/patterns/t_threat_model_plans/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 8 16-word bullets threat modeling my life plan and what could go wrong. +5. Provide recommendations on how to address the threats and improve the life plan. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_visualize_mission_goals_projects/system.md b/patterns/t_visualize_mission_goals_projects/system.md new file mode 100644 index 00000000..c1533ccf --- /dev/null +++ b/patterns/t_visualize_mission_goals_projects/system.md @@ -0,0 +1,15 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Create an ASCII art diagram of the relationship my missions, goals, and projects. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/t_year_in_review/system.md b/patterns/t_year_in_review/system.md new file mode 100644 index 00000000..a2796c6f --- /dev/null +++ b/patterns/t_year_in_review/system.md @@ -0,0 +1,16 @@ +# IDENTITY + +You are an expert at understanding deep context about a person or entity, and then creating wisdom from that context combined with the instruction or question given in the input. + +# STEPS + +1. Read the incoming TELOS File thoroughly. Fully understand everything about this person or entity. +2. Deeply study the input instruction or question. +3. Spend significant time and effort thinking about how these two are related, and what would be the best possible ouptut for the person who sent the input. +4. Write 8 16-word bullets describing what you accomplished this year. +5. End with an ASCII art visualization of what you worked on and accomplished vs. what you didn't work on or finish. + +# OUTPUT INSTRUCTIONS + +1. Only use basic markdown formatting. No special formatting or italics or bolding or anything. +2. Only output the list, nothing else. diff --git a/patterns/transcribe_minutes/system.md b/patterns/transcribe_minutes/system.md index 6e66aa71..8f7022fd 100644 --- a/patterns/transcribe_minutes/system.md +++ b/patterns/transcribe_minutes/system.md @@ -29,13 +29,13 @@ Take a step back and think step-by-step about how to achieve the best possible r # OUTPUT INSTRUCTIONS - Only output Markdown. -- Write MINUTES as exactly 15 words. -- Write ACTIONABLES as exactly 15 words. -- Write DECISIONS as exactly 15 words. +- Write MINUTES as exactly 16 words. +- Write ACTIONABLES as exactly 16 words. +- Write DECISIONS as exactly 16 words. - Write CHALLENGES as 2-3 sentences. - Write NEXT STEPS as 2-3 sentences. - Do not give warnings or notes; only output the requested sections. -- Do not repeat ideas, quotes, facts, or resources. +- Do not repeat actionables, decisions, or challenges. - You use bulleted lists for output, not numbered lists. - Do not start items with the same opening words. - Ensure you follow ALL these instructions when creating your output. diff --git a/patterns/youtube_summary/system.md b/patterns/youtube_summary/system.md new file mode 100644 index 00000000..80b21d8e --- /dev/null +++ b/patterns/youtube_summary/system.md @@ -0,0 +1,41 @@ +# IDENTITY and PURPOSE + +You are an AI assistant specialized in creating concise, informative summaries of YouTube video content based on transcripts. Your role is to analyze video transcripts, identify key points, main themes, and significant moments, then organize this information into a well-structured summary that includes relevant timestamps. You excel at distilling lengthy content into digestible summaries while preserving the most valuable information and maintaining the original flow of the video. + +Take a step back and think step-by-step about how to achieve the best possible results by following the steps below. + +## STEPS + +- Carefully read through the entire transcript to understand the overall content and structure of the video +- Identify the main topic and purpose of the video +- Note key points, important concepts, and significant moments throughout the transcript +- Pay attention to natural transitions or segment changes in the video +- Extract relevant timestamps for important moments or topic changes +- Organize information into a logical structure that follows the video's progression +- Create a concise summary that captures the essence of the video +- Include timestamps alongside key points to allow easy navigation +- Ensure the summary is comprehensive yet concise + +## OUTPUT INSTRUCTIONS + +- Only output Markdown + +- Begin with a brief overview of the video's main topic and purpose + +- Structure the summary with clear headings and subheadings that reflect the video's organization + +- Include timestamps in [HH:MM:SS] format before each key point or section + +- Keep the summary concise but comprehensive, focusing on the most valuable information + +- Use bullet points for lists of related points when appropriate + +- Bold or italicize particularly important concepts or takeaways + +- End with a brief conclusion summarizing the video's main message or call to action + +- Ensure you follow ALL these instructions when creating your output. + +## INPUT + +INPUT: diff --git a/pkgs/fabric/version.nix b/pkgs/fabric/version.nix deleted file mode 100644 index 4196d904..00000000 --- a/pkgs/fabric/version.nix +++ /dev/null @@ -1 +0,0 @@ -"1.4.128" diff --git a/plugins/ai/anthropic/anthropic.go b/plugins/ai/anthropic/anthropic.go index ff3f4d3a..d811c48d 100644 --- a/plugins/ai/anthropic/anthropic.go +++ b/plugins/ai/anthropic/anthropic.go @@ -3,6 +3,7 @@ package anthropic import ( "context" "fmt" + "strings" "github.com/anthropics/anthropic-sdk-go" "github.com/anthropics/anthropic-sdk-go/option" @@ -11,7 +12,7 @@ import ( goopenai "github.com/sashabaranov/go-openai" ) -//const baseUrl = "https://api.anthropic.com/" +const defaultBaseUrl = "https://api.anthropic.com/" func NewClient() (ret *Client) { vendorName := "Anthropic" @@ -23,20 +24,20 @@ func NewClient() (ret *Client) { ConfigureCustom: ret.configure, } - //ret.ApiBaseURL = ret.AddSetupQuestion("API Base URL", false) - //ret.ApiBaseURL.Value = baseUrl + ret.ApiBaseURL = ret.AddSetupQuestion("API Base URL", false) + ret.ApiBaseURL.Value = defaultBaseUrl ret.ApiKey = ret.PluginBase.AddSetupQuestion("API key", true) - // we could provide a setup question for the following settings ret.maxTokens = 4096 ret.defaultRequiredUserMessage = "Hi" ret.models = []string{ + anthropic.ModelClaude3_7SonnetLatest, anthropic.ModelClaude3_7Sonnet20250219, anthropic.ModelClaude3_5HaikuLatest, anthropic.ModelClaude3_5Haiku20241022, anthropic.ModelClaude3_5SonnetLatest, anthropic.ModelClaude3_5Sonnet20241022, anthropic.ModelClaude_3_5_Sonnet_20240620, anthropic.ModelClaude3OpusLatest, anthropic.ModelClaude_3_Opus_20240229, anthropic.ModelClaude_3_Sonnet_20240229, anthropic.ModelClaude_3_Haiku_20240307, anthropic.ModelClaude_2_1, - anthropic.ModelClaude_2_0, anthropic.ModelClaude_Instant_1_2, + anthropic.ModelClaude_2_0, } return @@ -44,8 +45,8 @@ func NewClient() (ret *Client) { type Client struct { *plugins.PluginBase - //ApiBaseURL *plugins.SetupQuestion - ApiKey *plugins.SetupQuestion + ApiBaseURL *plugins.SetupQuestion + ApiKey *plugins.SetupQuestion maxTokens int defaultRequiredUserMessage string @@ -55,14 +56,23 @@ type Client struct { } func (an *Client) configure() (err error) { - /*if an.ApiBaseURL.Value != "" { + if an.ApiBaseURL.Value != "" { + baseURL := an.ApiBaseURL.Value + + if strings.Contains(baseURL, "-") && !strings.HasSuffix(baseURL, "/v1") { + if strings.HasSuffix(baseURL, "/") { + baseURL = strings.TrimSuffix(baseURL, "/") + } + baseURL = baseURL + "/v1" + } + an.client = anthropic.NewClient( - option.WithAPIKey(an.ApiKey.Value), option.WithBaseURL(an.ApiBaseURL.Value), + option.WithAPIKey(an.ApiKey.Value), + option.WithBaseURL(baseURL), ) } else { - */ - an.client = anthropic.NewClient(option.WithAPIKey(an.ApiKey.Value)) - //} + an.client = anthropic.NewClient(option.WithAPIKey(an.ApiKey.Value)) + } return } @@ -73,7 +83,6 @@ func (an *Client) ListModels() (ret []string, err error) { func (an *Client) SendStream( msgs []*goopenai.ChatCompletionMessage, opts *common.ChatOptions, channel chan string, ) (err error) { - messages := an.toMessages(msgs) ctx := context.Background() @@ -121,10 +130,8 @@ func (an *Client) Send(ctx context.Context, msgs []*goopenai.ChatCompletionMessa } func (an *Client) toMessages(msgs []*goopenai.ChatCompletionMessage) (ret []anthropic.MessageParam) { - // we could call the method before calling the specific vendor normalizedMessages := common.NormalizeMessages(msgs, an.defaultRequiredUserMessage) - // Iterate over the incoming session messages and process them for _, msg := range normalizedMessages { var message anthropic.MessageParam switch msg.Role { diff --git a/plugins/ai/anthropic/anthropic_test.go b/plugins/ai/anthropic/anthropic_test.go new file mode 100644 index 00000000..e89ea3db --- /dev/null +++ b/plugins/ai/anthropic/anthropic_test.go @@ -0,0 +1,65 @@ +package anthropic + +import ( + "testing" +) + +// Test generated using Keploy +func TestNewClient_DefaultInitialization(t *testing.T) { + client := NewClient() + + if client == nil { + t.Fatal("Expected client to be initialized, got nil") + } + + if client.ApiBaseURL.Value != defaultBaseUrl { + t.Errorf("Expected default API Base URL to be %s, got %s", defaultBaseUrl, client.ApiBaseURL.Value) + } + + if client.maxTokens != 4096 { + t.Errorf("Expected default maxTokens to be 4096, got %d", client.maxTokens) + } + + if len(client.models) == 0 { + t.Error("Expected models to be initialized with default values, got empty list") + } +} + +// Test generated using Keploy +func TestClientListModels(t *testing.T) { + client := NewClient() + + models, err := client.ListModels() + if err != nil { + t.Fatalf("Expected no error, got %v", err) + } + + if len(models) != len(client.models) { + t.Errorf("Expected %d models, got %d", len(client.models), len(models)) + } + + for i, model := range models { + if model != client.models[i] { + t.Errorf("Expected model at index %d to be %s, got %s", i, client.models[i], model) + } + } +} + +func TestClient_ListModels_ReturnsCorrectModels(t *testing.T) { + client := NewClient() + models, err := client.ListModels() + + if err != nil { + t.Fatalf("Expected no error, got %v", err) + } + + if len(models) != len(client.models) { + t.Errorf("Expected %d models, got %d", len(client.models), len(models)) + } + + for i, model := range models { + if model != client.models[i] { + t.Errorf("Expected model %s at index %d, got %s", client.models[i], i, model) + } + } +} diff --git a/plugins/ai/azure/azure.go b/plugins/ai/azure/azure.go index c75095e5..b2d6d4ac 100644 --- a/plugins/ai/azure/azure.go +++ b/plugins/ai/azure/azure.go @@ -1,10 +1,10 @@ package azure import ( - "github.com/danielmiessler/fabric/plugins" - "github.com/danielmiessler/fabric/plugins/ai/openai" "strings" + "github.com/danielmiessler/fabric/plugins" + "github.com/danielmiessler/fabric/plugins/ai/openai" goopenai "github.com/sashabaranov/go-openai" ) @@ -13,6 +13,8 @@ func NewClient() (ret *Client) { ret.Client = openai.NewClientCompatible("Azure", "", ret.configure) ret.ApiDeployments = ret.AddSetupQuestionCustom("deployments", true, "Enter your Azure deployments (comma separated)") + ret.ApiVersion = ret.AddSetupQuestionCustom("API Version", false, + "Enter the Azure API version (optional)") return } @@ -20,19 +22,22 @@ func NewClient() (ret *Client) { type Client struct { *openai.Client ApiDeployments *plugins.SetupQuestion + ApiVersion *plugins.SetupQuestion apiDeployments []string } func (oi *Client) configure() (err error) { oi.apiDeployments = strings.Split(oi.ApiDeployments.Value, ",") - oi.ApiClient = goopenai.NewClientWithConfig(goopenai.DefaultAzureConfig(oi.ApiKey.Value, oi.ApiBaseURL.Value)) + config := goopenai.DefaultAzureConfig(oi.ApiKey.Value, oi.ApiBaseURL.Value) + if oi.ApiVersion.Value != "" { + config.APIVersion = oi.ApiVersion.Value + } + oi.ApiClient = goopenai.NewClientWithConfig(config) return } func (oi *Client) ListModels() (ret []string, err error) { - // Azure Open Client has models and deployments. We need to get the deployments to use them for chat - // There is no easy way to get the deployments from the API, so we need to ask the user to provide them ret = oi.apiDeployments return } diff --git a/plugins/ai/azure/azure_test.go b/plugins/ai/azure/azure_test.go new file mode 100644 index 00000000..4230b87e --- /dev/null +++ b/plugins/ai/azure/azure_test.go @@ -0,0 +1,75 @@ +package azure + +import ( + "testing" +) + +// Test generated using Keploy +func TestNewClientInitialization(t *testing.T) { + client := NewClient() + if client == nil { + t.Fatalf("Expected non-nil client, got nil") + } + if client.ApiDeployments == nil { + t.Errorf("Expected ApiDeployments to be initialized, got nil") + } + if client.ApiVersion == nil { + t.Errorf("Expected ApiVersion to be initialized, got nil") + } + if client.Client == nil { + t.Errorf("Expected Client to be initialized, got nil") + } +} + +// Test generated using Keploy +func TestClientConfigure(t *testing.T) { + client := NewClient() + client.ApiDeployments.Value = "deployment1,deployment2" + client.ApiKey.Value = "test-api-key" + client.ApiBaseURL.Value = "https://example.com" + client.ApiVersion.Value = "2021-01-01" + + err := client.configure() + if err != nil { + t.Fatalf("Expected no error, got %v", err) + } + + expectedDeployments := []string{"deployment1", "deployment2"} + if len(client.apiDeployments) != len(expectedDeployments) { + t.Errorf("Expected %d deployments, got %d", len(expectedDeployments), len(client.apiDeployments)) + } + for i, deployment := range expectedDeployments { + if client.apiDeployments[i] != deployment { + t.Errorf("Expected deployment %s, got %s", deployment, client.apiDeployments[i]) + } + } + + if client.ApiClient == nil { + t.Errorf("Expected ApiClient to be initialized, got nil") + } + + if client.ApiVersion.Value != "2021-01-01" { + t.Errorf("Expected API version to be '2021-01-01', got %s", client.ApiVersion.Value) + } +} + +// Test generated using Keploy +func TestListModels(t *testing.T) { + client := NewClient() + client.apiDeployments = []string{"deployment1", "deployment2"} + + models, err := client.ListModels() + if err != nil { + t.Fatalf("Expected no error, got %v", err) + } + + expectedModels := []string{"deployment1", "deployment2"} + if len(models) != len(expectedModels) { + t.Errorf("Expected %d models, got %d", len(expectedModels), len(models)) + } + for i, model := range expectedModels { + if models[i] != model { + t.Errorf("Expected model %s, got %s", model, models[i]) + } + } +} diff --git a/plugins/ai/deepseek/deepseek.go b/plugins/ai/deepseek/deepseek.go new file mode 100644 index 00000000..21e54400 --- /dev/null +++ b/plugins/ai/deepseek/deepseek.go @@ -0,0 +1,15 @@ +package deepseek + +import ( + "github.com/danielmiessler/fabric/plugins/ai/openai" +) + +func NewClient() (ret *Client) { + ret = &Client{} + ret.Client = openai.NewClientCompatible("DeepSeek", "https://api.deepseek.com", nil) + return +} + +type Client struct { + *openai.Client +} diff --git a/plugins/ai/deepseek/deepseek_test.go b/plugins/ai/deepseek/deepseek_test.go new file mode 100644 index 00000000..70b69d2a --- /dev/null +++ b/plugins/ai/deepseek/deepseek_test.go @@ -0,0 +1,13 @@ +package deepseek + +// Test generated using Keploy +import ( + "testing" +) + +func TestNewClient_EmbeddedClientNotNil(t *testing.T) { + client := NewClient() + if client.Client == nil { + t.Fatalf("Expected embedded openai.Client to be non-nil, got nil") + } +} diff --git a/plugins/ai/dryrun/dryrun_test.go b/plugins/ai/dryrun/dryrun_test.go new file mode 100644 index 00000000..ba20a420 --- /dev/null +++ b/plugins/ai/dryrun/dryrun_test.go @@ -0,0 +1,56 @@ +package dryrun + +import ( + "reflect" + "testing" + + "github.com/danielmiessler/fabric/common" + "github.com/sashabaranov/go-openai" +) + +// Test generated using Keploy +func TestListModels_ReturnsExpectedModel(t *testing.T) { + client := NewClient() + models, err := client.ListModels() + if err != nil { + t.Fatalf("Expected no error, got %v", err) + } + expected := []string{"dry-run-model"} + if !reflect.DeepEqual(models, expected) { + t.Errorf("Expected %v, got %v", expected, models) + } +} + +// Test generated using Keploy +func TestSetup_ReturnsNil(t *testing.T) { + client := NewClient() + err := client.Setup() + if err != nil { + t.Errorf("Expected nil error, got %v", err) + } +} + +// Test generated using Keploy +func TestSendStream_SendsMessages(t *testing.T) { + client := NewClient() + msgs := []*openai.ChatCompletionMessage{ + {Role: "user", Content: "Test message"}, + } + opts := &common.ChatOptions{ + Model: "dry-run-model", + } + channel := make(chan string) + go func() { + err := client.SendStream(msgs, opts, channel) + if err != nil { + t.Errorf("Expected no error, got %v", err) + } + }() + var receivedMessages []string + for msg := range channel { + receivedMessages = append(receivedMessages, msg) + } + if len(receivedMessages) == 0 { + t.Errorf("Expected to receive messages, but got none") + } +} diff --git a/plugins/ai/exolab/exolab.go b/plugins/ai/exolab/exolab.go new file mode 100644 index 00000000..3adfec7e --- /dev/null +++ b/plugins/ai/exolab/exolab.go @@ -0,0 +1,45 @@ +package exolab + +import ( + "strings" + + "github.com/danielmiessler/fabric/plugins" + "github.com/danielmiessler/fabric/plugins/ai/openai" + + goopenai "github.com/sashabaranov/go-openai" +) + +func NewClient() (ret *Client) { + ret = &Client{} + ret.Client = openai.NewClientCompatibleNoSetupQuestions("Exolab", ret.configure) + + ret.ApiBaseURL = ret.AddSetupQuestion("API Base URL", true) + ret.ApiBaseURL.Value = "http://localhost:52415" + + ret.ApiModels = ret.AddSetupQuestionCustom("models", true, + "Enter your deployed Exolab models (comma separated)") + + return +} + +type Client struct { + *openai.Client + ApiModels *plugins.SetupQuestion + + apiModels []string +} + +func (oi *Client) configure() (err error) { + oi.apiModels = strings.Split(oi.ApiModels.Value, ",") + + config := goopenai.DefaultConfig("") + config.BaseURL = oi.ApiBaseURL.Value + + oi.ApiClient = goopenai.NewClientWithConfig(config) + return +} + +func (oi *Client) ListModels() (ret []string, err error) { + ret = oi.apiModels + return +} diff --git a/plugins/ai/gemini/gemini.go b/plugins/ai/gemini/gemini.go index 1936dfd5..f3990d3e 100644 --- a/plugins/ai/gemini/gemini.go +++ b/plugins/ai/gemini/gemini.go @@ -4,9 +4,10 @@ import ( "context" "errors" "fmt" + "strings" + "github.com/danielmiessler/fabric/plugins" goopenai "github.com/sashabaranov/go-openai" - "strings" "github.com/danielmiessler/fabric/common" "github.com/google/generative-ai-go/genai" diff --git a/plugins/ai/gemini/gemini_test.go b/plugins/ai/gemini/gemini_test.go new file mode 100644 index 00000000..e3d58572 --- /dev/null +++ b/plugins/ai/gemini/gemini_test.go @@ -0,0 +1,44 @@ +package gemini + +import ( + "testing" + + "github.com/google/generative-ai-go/genai" +) + +// Test generated using Keploy +func TestBuildModelNameSimple(t *testing.T) { + client := &Client{} + fullModelName := "models/chat-bison-001" + expected := "chat-bison-001" + + result := client.buildModelNameSimple(fullModelName) + + if result != expected { + t.Errorf("Expected %v, got %v", expected, result) + } +} + +// Test generated using Keploy +func TestExtractText(t *testing.T) { + client := &Client{} + response := &genai.GenerateContentResponse{ + Candidates: []*genai.Candidate{ + { + Content: &genai.Content{ + Parts: []genai.Part{ + genai.Text("Hello, "), + genai.Text("world!"), + }, + }, + }, + }, + } + expected := "Hello, world!" + + result := client.extractText(response) + + if result != expected { + t.Errorf("Expected %v, got %v", expected, result) + } +} diff --git a/plugins/ai/groq/groq.go b/plugins/ai/groq/groq.go index b2805cbc..92940d7b 100644 --- a/plugins/ai/groq/groq.go +++ b/plugins/ai/groq/groq.go @@ -4,12 +4,14 @@ import ( "github.com/danielmiessler/fabric/plugins/ai/openai" ) +// NewClient initializes and returns a new Groq Client. func NewClient() (ret *Client) { ret = &Client{} ret.Client = openai.NewClientCompatible("Groq", "https://api.groq.com/openai/v1", nil) return } +// Client wraps the openai.Client to provide additional functionality specific to Groq. type Client struct { *openai.Client } diff --git a/plugins/ai/groq/groq_test.go b/plugins/ai/groq/groq_test.go new file mode 100644 index 00000000..59e10718 --- /dev/null +++ b/plugins/ai/groq/groq_test.go @@ -0,0 +1,13 @@ +package groq + +// Test generated using Keploy +import ( + "testing" +) + +func TestNewClientEmbeddedClientNotNil(t *testing.T) { + client := NewClient() + if client.Client == nil { + t.Fatalf("Expected embedded openai.Client to be non-nil, got nil") + } +} diff --git a/plugins/ai/litellm/litellm.go b/plugins/ai/litellm/litellm.go new file mode 100644 index 00000000..fbbd098e --- /dev/null +++ b/plugins/ai/litellm/litellm.go @@ -0,0 +1,15 @@ +package litellm + +import ( + "github.com/danielmiessler/fabric/plugins/ai/openai" +) + +func NewClient() (ret *Client) { + ret = &Client{} + ret.Client = openai.NewClientCompatible("LiteLLM", "http://localhost:4000", nil) + return +} + +type Client struct { + *openai.Client +} diff --git a/plugins/ai/lmstudio/lmstudio.go b/plugins/ai/lmstudio/lmstudio.go new file mode 100644 index 00000000..42f206ab --- /dev/null +++ b/plugins/ai/lmstudio/lmstudio.go @@ -0,0 +1,347 @@ +package lmstudio + +import ( + "bufio" + "bytes" + "context" + "encoding/json" + "fmt" + "io" + "net/http" + + goopenai "github.com/sashabaranov/go-openai" + + "github.com/danielmiessler/fabric/common" + "github.com/danielmiessler/fabric/plugins" +) + +// NewClient creates a new LM Studio client with default configuration. +func NewClient() (ret *Client) { + return NewClientCompatible("LM Studio", "http://localhost:1234/v1", nil) +} + +// NewClientCompatible creates a new LM Studio client with custom configuration. +func NewClientCompatible(vendorName string, defaultBaseUrl string, configureCustom func() error) (ret *Client) { + ret = &Client{} + + if configureCustom == nil { + configureCustom = ret.configure + } + ret.PluginBase = &plugins.PluginBase{ + Name: vendorName, + EnvNamePrefix: plugins.BuildEnvVariablePrefix(vendorName), + ConfigureCustom: configureCustom, + } + ret.ApiUrl = ret.AddSetupQuestionCustom("API URL", true, + fmt.Sprintf("Enter your %v URL (as a reminder, it is usually %v')", vendorName, defaultBaseUrl)) + return +} + +// Client represents the LM Studio client. +type Client struct { + *plugins.PluginBase + ApiUrl *plugins.SetupQuestion + HttpClient *http.Client +} + +// configure sets up the HTTP client. +func (c *Client) configure() error { + c.HttpClient = &http.Client{} + return nil +} + +// ListModels returns a list of available models. +func (c *Client) ListModels() ([]string, error) { + url := fmt.Sprintf("%s/models", c.ApiUrl.Value) + + req, err := http.NewRequest("GET", url, nil) + if err != nil { + return nil, fmt.Errorf("failed to create request: %w", err) + } + + resp, err := c.HttpClient.Do(req) + if err != nil { + return nil, fmt.Errorf("failed to send request: %w", err) + } + defer resp.Body.Close() + + if resp.StatusCode != http.StatusOK { + return nil, fmt.Errorf("unexpected status code: %d", resp.StatusCode) + } + + var result struct { + Data []struct { + ID string `json:"id"` + } `json:"data"` + } + + if err := json.NewDecoder(resp.Body).Decode(&result); err != nil { + return nil, fmt.Errorf("failed to decode response: %w", err) + } + + models := make([]string, len(result.Data)) + for i, model := range result.Data { + models[i] = model.ID + } + + return models, nil +} + +func (c *Client) SendStream(msgs []*goopenai.ChatCompletionMessage, opts *common.ChatOptions, channel chan string) (err error) { + url := fmt.Sprintf("%s/chat/completions", c.ApiUrl.Value) + + payload := map[string]interface{}{ + "messages": msgs, + "model": opts.Model, + "stream": true, // Enable streaming + } + + var jsonPayload []byte + if jsonPayload, err = json.Marshal(payload); err != nil { + err = fmt.Errorf("failed to marshal payload: %w", err) + return + } + + var req *http.Request + if req, err = http.NewRequest("POST", url, bytes.NewBuffer(jsonPayload)); err != nil { + err = fmt.Errorf("failed to create request: %w", err) + return + } + + req.Header.Set("Content-Type", "application/json") + + var resp *http.Response + if resp, err = c.HttpClient.Do(req); err != nil { + err = fmt.Errorf("failed to send request: %w", err) + return + } + defer resp.Body.Close() + + if resp.StatusCode != http.StatusOK { + err = fmt.Errorf("unexpected status code: %d", resp.StatusCode) + return + } + + defer close(channel) + + reader := bufio.NewReader(resp.Body) + for { + var line []byte + if line, err = reader.ReadBytes('\n'); err != nil { + if err == io.EOF { + err = nil + break + } + err = fmt.Errorf("error reading response: %w", err) + return + } + + if len(line) == 0 { + continue + } + + if bytes.HasPrefix(line, []byte("data: ")) { + line = bytes.TrimPrefix(line, []byte("data: ")) + } + + if string(line) == "[DONE]" { + break + } + + var result map[string]interface{} + if err = json.Unmarshal(line, &result); err != nil { + continue + } + + var choices []interface{} + var ok bool + if choices, ok = result["choices"].([]interface{}); !ok || len(choices) == 0 { + continue + } + + var delta map[string]interface{} + if delta, ok = choices[0].(map[string]interface{})["delta"].(map[string]interface{}); !ok { + continue + } + + var content string + if content, _ = delta["content"].(string); content != "" { + channel <- content + } + } + + return +} + +func (c *Client) Send(ctx context.Context, msgs []*goopenai.ChatCompletionMessage, opts *common.ChatOptions) (content string, err error) { + url := fmt.Sprintf("%s/chat/completions", c.ApiUrl.Value) + + payload := map[string]interface{}{ + "messages": msgs, + "model": opts.Model, + // Add other options from opts if supported by LM Studio + } + + var jsonPayload []byte + if jsonPayload, err = json.Marshal(payload); err != nil { + err = fmt.Errorf("failed to marshal payload: %w", err) + return + } + + var req *http.Request + if req, err = http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonPayload)); err != nil { + err = fmt.Errorf("failed to create request: %w", err) + return + } + + req.Header.Set("Content-Type", "application/json") + + var resp *http.Response + if resp, err = c.HttpClient.Do(req); err != nil { + err = fmt.Errorf("failed to send request: %w", err) + return + } + defer resp.Body.Close() + + if resp.StatusCode != http.StatusOK { + err = fmt.Errorf("unexpected status code: %d", resp.StatusCode) + return + } + + var result map[string]interface{} + if err = json.NewDecoder(resp.Body).Decode(&result); err != nil { + err = fmt.Errorf("failed to decode response: %w", err) + return + } + + var choices []interface{} + var ok bool + if choices, ok = result["choices"].([]interface{}); !ok || len(choices) == 0 { + err = fmt.Errorf("invalid response format: missing or empty choices") + return + } + + var message map[string]interface{} + if message, ok = choices[0].(map[string]interface{})["message"].(map[string]interface{}); !ok { + err = fmt.Errorf("invalid response format: missing message in first choice") + return + } + + if content, ok = message["content"].(string); !ok { + err = fmt.Errorf("invalid response format: missing or non-string content in message") + return + } + + return +} + +func (c *Client) Complete(ctx context.Context, prompt string, opts *common.ChatOptions) (text string, err error) { + url := fmt.Sprintf("%s/completions", c.ApiUrl.Value) + + payload := map[string]interface{}{ + "prompt": prompt, + "model": opts.Model, + // Add other options from opts if supported by LM Studio + } + + var jsonPayload []byte + if jsonPayload, err = json.Marshal(payload); err != nil { + err = fmt.Errorf("failed to marshal payload: %w", err) + return + } + + var req *http.Request + if req, err = http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonPayload)); err != nil { + err = fmt.Errorf("failed to create request: %w", err) + return + } + + req.Header.Set("Content-Type", "application/json") + + var resp *http.Response + if resp, err = c.HttpClient.Do(req); err != nil { + err = fmt.Errorf("failed to send request: %w", err) + return + } + defer resp.Body.Close() + + if resp.StatusCode != http.StatusOK { + err = fmt.Errorf("unexpected status code: %d", resp.StatusCode) + return + } + + var result map[string]interface{} + if err = json.NewDecoder(resp.Body).Decode(&result); err != nil { + err = fmt.Errorf("failed to decode response: %w", err) + return + } + + var choices []interface{} + var ok bool + if choices, ok = result["choices"].([]interface{}); !ok || len(choices) == 0 { + err = fmt.Errorf("invalid response format: missing or empty choices") + return + } + + if text, ok = choices[0].(map[string]interface{})["text"].(string); !ok { + err = fmt.Errorf("invalid response format: missing or non-string text in first choice") + return + } + + return +} + +func (c *Client) GetEmbeddings(ctx context.Context, input string, opts *common.ChatOptions) (embeddings []float64, err error) { + url := fmt.Sprintf("%s/embeddings", c.ApiUrl.Value) + + payload := map[string]interface{}{ + "input": input, + "model": opts.Model, + // Add other options from opts if supported by LM Studio + } + + var jsonPayload []byte + if jsonPayload, err = json.Marshal(payload); err != nil { + err = fmt.Errorf("failed to marshal payload: %w", err) + return + } + + var req *http.Request + if req, err = http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonPayload)); err != nil { + err = fmt.Errorf("failed to create request: %w", err) + return + } + + req.Header.Set("Content-Type", "application/json") + + var resp *http.Response + if resp, err = c.HttpClient.Do(req); err != nil { + err = fmt.Errorf("failed to send request: %w", err) + return + } + defer resp.Body.Close() + + if resp.StatusCode != http.StatusOK { + err = fmt.Errorf("unexpected status code: %d", resp.StatusCode) + return + } + + var result struct { + Data []struct { + Embedding []float64 `json:"embedding"` + } `json:"data"` + } + + if err = json.NewDecoder(resp.Body).Decode(&result); err != nil { + err = fmt.Errorf("failed to decode response: %w", err) + return + } + + if len(result.Data) == 0 { + err = fmt.Errorf("no embeddings returned") + return + } + + embeddings = result.Data[0].Embedding + return +} diff --git a/plugins/ai/ollama/ollama.go b/plugins/ai/ollama/ollama.go index e0646bf8..89a417a1 100644 --- a/plugins/ai/ollama/ollama.go +++ b/plugins/ai/ollama/ollama.go @@ -26,7 +26,7 @@ func NewClient() (ret *Client) { } ret.ApiUrl = ret.AddSetupQuestionCustom("API URL", true, - "Enter your Ollama URL (as a reminder, it is usually http://localhost:11434)") + "Enter your Ollama URL (as a reminder, it is usually http://localhost:1234/v1')") return } diff --git a/plugins/ai/openai/openai.go b/plugins/ai/openai/openai.go index 10ddd5a8..9d26112c 100644 --- a/plugins/ai/openai/openai.go +++ b/plugins/ai/openai/openai.go @@ -4,10 +4,11 @@ import ( "context" "errors" "fmt" - "github.com/danielmiessler/fabric/plugins" "io" "log/slog" + "github.com/danielmiessler/fabric/plugins" + "github.com/danielmiessler/fabric/common" "github.com/samber/lo" "github.com/sashabaranov/go-openai" @@ -18,6 +19,16 @@ func NewClient() (ret *Client) { } func NewClientCompatible(vendorName string, defaultBaseUrl string, configureCustom func() error) (ret *Client) { + ret = NewClientCompatibleNoSetupQuestions(vendorName, configureCustom) + + ret.ApiKey = ret.AddSetupQuestion("API Key", true) + ret.ApiBaseURL = ret.AddSetupQuestion("API Base URL", false) + ret.ApiBaseURL.Value = defaultBaseUrl + + return +} + +func NewClientCompatibleNoSetupQuestions(vendorName string, configureCustom func() error) (ret *Client) { ret = &Client{} if configureCustom == nil { @@ -30,10 +41,6 @@ func NewClientCompatible(vendorName string, defaultBaseUrl string, configureCust ConfigureCustom: configureCustom, } - ret.ApiKey = ret.AddSetupQuestion("API Key", true) - ret.ApiBaseURL = ret.AddSetupQuestion("API Base URL", false) - ret.ApiBaseURL.Value = defaultBaseUrl - return } diff --git a/plugins/ai/openrouter/openrouter_test.go b/plugins/ai/openrouter/openrouter_test.go new file mode 100644 index 00000000..aea0a076 --- /dev/null +++ b/plugins/ai/openrouter/openrouter_test.go @@ -0,0 +1 @@ +package openrouter diff --git a/plugins/ai/vendor.go b/plugins/ai/vendor.go index c1ba9b0a..2c0245c0 100644 --- a/plugins/ai/vendor.go +++ b/plugins/ai/vendor.go @@ -2,6 +2,7 @@ package ai import ( "context" + "github.com/danielmiessler/fabric/plugins" goopenai "github.com/sashabaranov/go-openai" diff --git a/plugins/ai/vendors.go b/plugins/ai/vendors.go index ad2fca97..c8ad70b1 100644 --- a/plugins/ai/vendors.go +++ b/plugins/ai/vendors.go @@ -4,8 +4,9 @@ import ( "bytes" "context" "fmt" - "github.com/danielmiessler/fabric/plugins" "sync" + + "github.com/danielmiessler/fabric/plugins" ) func NewVendorsManager() *VendorsManager { @@ -93,7 +94,6 @@ func (o *VendorsManager) readModels() (err error) { for result := range resultsChan { if result.err != nil { fmt.Println(result.vendorName, result.err) - cancel() // Cancel remaining goroutines if needed } else { o.Models.AddGroupItems(result.vendorName, result.models...) } diff --git a/plugins/db/fsdb/db.go b/plugins/db/fsdb/db.go index 4e458ded..6ee8534f 100644 --- a/plugins/db/fsdb/db.go +++ b/plugins/db/fsdb/db.go @@ -2,10 +2,11 @@ package fsdb import ( "fmt" - "github.com/joho/godotenv" "os" "path/filepath" "time" + + "github.com/joho/godotenv" ) func NewDb(dir string) (db *Db) { diff --git a/plugins/db/fsdb/patterns.go b/plugins/db/fsdb/patterns.go index ac727b69..c31c2427 100644 --- a/plugins/db/fsdb/patterns.go +++ b/plugins/db/fsdb/patterns.go @@ -107,7 +107,7 @@ func (o *PatternsEntity) getFromDB(name string) (ret *Pattern, err error) { func (o *PatternsEntity) PrintLatestPatterns(latestNumber int) (err error) { var contents []byte if contents, err = os.ReadFile(o.UniquePatternsFilePath); err != nil { - err = fmt.Errorf("could not read unique patterns file. Pleas run --updatepatterns (%s)", err) + err = fmt.Errorf("could not read unique patterns file. Please run --updatepatterns (%s)", err) return } uniquePatterns := strings.Split(string(contents), "\n") diff --git a/plugins/db/fsdb/sessions.go b/plugins/db/fsdb/sessions.go index 2f278744..54d5b961 100644 --- a/plugins/db/fsdb/sessions.go +++ b/plugins/db/fsdb/sessions.go @@ -2,6 +2,7 @@ package fsdb import ( "fmt" + "github.com/danielmiessler/fabric/common" goopenai "github.com/sashabaranov/go-openai" ) diff --git a/plugins/db/fsdb/sessions_test.go b/plugins/db/fsdb/sessions_test.go index 82c99fe0..9477ce14 100644 --- a/plugins/db/fsdb/sessions_test.go +++ b/plugins/db/fsdb/sessions_test.go @@ -1,8 +1,9 @@ package fsdb import ( - goopenai "github.com/sashabaranov/go-openai" "testing" + + goopenai "github.com/sashabaranov/go-openai" ) func TestSessions_GetOrCreateSession(t *testing.T) { diff --git a/plugins/strategy/strategy.go b/plugins/strategy/strategy.go new file mode 100644 index 00000000..6a8319fd --- /dev/null +++ b/plugins/strategy/strategy.go @@ -0,0 +1,231 @@ +package strategy + +import ( + "encoding/json" + "fmt" + "io/fs" + "os" + "path/filepath" + "sort" + "strings" + + "github.com/danielmiessler/fabric/plugins" + "github.com/danielmiessler/fabric/plugins/tools/githelper" +) + +const DefaultStrategiesGitRepoUrl = "https://github.com/danielmiessler/fabric.git" +const DefaultStrategiesGitRepoFolder = "strategies" + +func NewStrategiesManager() (sm *StrategiesManager) { + label := "Prompt Strategies" + strategies, err := LoadAllFiles() + if err != nil { + strategies = make(map[string]Strategy) // empty map + } + sm = &StrategiesManager{ + Strategies: strategies, + } + sm.PluginBase = &plugins.PluginBase{ + Name: label, + SetupDescription: "Strategies - Downloads Prompting Strategies (like chain of thought) [required]", + EnvNamePrefix: plugins.BuildEnvVariablePrefix(label), + ConfigureCustom: sm.configure, + } + + sm.DefaultGitRepoUrl = sm.AddSetupQuestionCustom("Git Repo Url", true, + "Enter the default Git repository URL for the strategies") + sm.DefaultGitRepoUrl.Value = DefaultStrategiesGitRepoUrl + + sm.DefaultFolder = sm.AddSetupQuestionCustom("Git Repo Strategies Folder", true, + "Enter the default folder in the Git repository where strategies are stored") + sm.DefaultFolder.Value = DefaultStrategiesGitRepoFolder + + return +} + +type StrategiesManager struct { + *plugins.PluginBase + Strategies map[string]Strategy + + DefaultGitRepoUrl *plugins.SetupQuestion + DefaultFolder *plugins.SetupQuestion +} + +type Strategy struct { + Name string `json:"name"` + Description string `json:"description"` + Prompt string `json:"prompt"` +} + +func LoadAllFiles() (strategies map[string]Strategy, err error) { + strategies = make(map[string]Strategy) + strategyDir, err := getStrategyDir() + if err != nil { + return + } + filepath.WalkDir(strategyDir, func(path string, d fs.DirEntry, err error) error { + if err != nil { + return err + } + + if d.IsDir() && path != strategyDir { + return filepath.SkipDir + } + + if filepath.Ext(path) == ".json" { + strategyName := strings.TrimSuffix(filepath.Base(path), ".json") + strategy, err := LoadStrategy(strategyName) + if err != nil { + return err + } + strategies[strategy.Name] = *strategy + } + return nil + }) + return + +} + +func (sm *StrategiesManager) IsConfigured() (ret bool) { + ret = sm.PluginBase.IsConfigured() + if ret { + if len(sm.Strategies) == 0 { + ret = false + } + } + return +} + +func (sm *StrategiesManager) Setup() (err error) { + if err = sm.PluginBase.Setup(); err != nil { + return + } + if err = sm.PopulateDB(); err != nil { + return + } + return +} + +// PopulateDB downloads strategies from the internet and populates the strategies folder +func (sm *StrategiesManager) PopulateDB() (err error) { + stageDir, _ := getStrategyDir() + fmt.Printf("Downloading strategies and Populating %s...\n", stageDir) + fmt.Println() + if err = sm.gitCloneAndCopy(); err != nil { + return + } + return +} + +func (sm *StrategiesManager) gitCloneAndCopy() (err error) { + homeDir, err := os.UserHomeDir() + if err != nil { + err = fmt.Errorf("could not get home directory: %v", err) + return + } + strategyDir := filepath.Join(homeDir, ".config", "fabric", "strategies") + + // Create the directory if it doesn't exist + if err = os.MkdirAll(strategyDir, os.ModePerm); err != nil { + return fmt.Errorf("failed to create strategies directory: %w", err) + } + + // Use the helper to fetch files + err = githelper.FetchFilesFromRepo(githelper.FetchOptions{ + RepoURL: sm.DefaultGitRepoUrl.Value, + PathPrefix: sm.DefaultFolder.Value, + DestDir: strategyDir, + SingleDirectory: true, + }) + if err != nil { + return fmt.Errorf("failed to download strategies: %w", err) + } + + return nil +} + +func (sm *StrategiesManager) configure() (err error) { + sm.Strategies, err = LoadAllFiles() + return +} + +// getStrategyDir returns the path to the strategies directory +func getStrategyDir() (ret string, err error) { + homeDir, err := os.UserHomeDir() + if err != nil { + err = fmt.Errorf("could not get home directory: %v, using current directory instead", err) + ret = filepath.Join(".", "strategies") + return + } + return filepath.Join(homeDir, ".config", "fabric", "strategies"), nil +} + +// LoadStrategy loads a strategy from the given name +func LoadStrategy(filename string) (*Strategy, error) { + if filename == "" { + return nil, nil + } + + // Get the strategy directory path + strategyDir, err := getStrategyDir() + if err != nil { + return nil, err + } + + // First try with .json extension + strategyPath := filepath.Join(strategyDir, filename+".json") + if _, err := os.Stat(strategyPath); os.IsNotExist(err) { + // Try without extension + strategyPath = filepath.Join(strategyDir, filename) + if _, err := os.Stat(strategyPath); os.IsNotExist(err) { + return nil, fmt.Errorf("strategy %s not found. Please run 'fabric --liststrategies' for list", filename) + } + } + + data, err := os.ReadFile(strategyPath) + if err != nil { + return nil, err + } + + var strategy Strategy + if err := json.Unmarshal(data, &strategy); err != nil { + return nil, err + } + strategy.Name = strings.TrimSuffix(filepath.Base(strategyPath), ".json") + + return &strategy, nil +} + +// ListStrategies prints available strategies +func (sm *StrategiesManager) ListStrategies() error { + if len(sm.Strategies) == 0 { + return fmt.Errorf("no strategies found. Please run 'fabric --setup' to download strategies") + } + fmt.Print("Available Strategies:\n\n") + + // Get all strategy names for sorting + names := []string{} + for name := range sm.Strategies { + names = append(names, name) + } + + // Sort the strategy names alphabetically + sort.Strings(names) + + // Find the longest name to align descriptions + maxNameLength := 0 + for _, name := range names { + if len(name) > maxNameLength { + maxNameLength = len(name) + } + } + + // Print each strategy with its description aligned + formatString := "%-" + fmt.Sprintf("%d", maxNameLength+2) + "s %s\n" + for _, name := range names { + strategy := sm.Strategies[name] + fmt.Printf(formatString, strategy.Name, strategy.Description) + } + + return nil +} diff --git a/plugins/tools/converter/html_readability.go b/plugins/tools/converter/html_readability.go index debbe1a6..001e274e 100644 --- a/plugins/tools/converter/html_readability.go +++ b/plugins/tools/converter/html_readability.go @@ -2,6 +2,7 @@ package converter import ( "bytes" + "github.com/go-shiori/go-readability" ) diff --git a/plugins/tools/githelper/githelper.go b/plugins/tools/githelper/githelper.go new file mode 100644 index 00000000..d95b0206 --- /dev/null +++ b/plugins/tools/githelper/githelper.go @@ -0,0 +1,111 @@ +package githelper + +import ( + "fmt" + "io" + "os" + "path/filepath" + "strings" + + "github.com/go-git/go-git/v5" + "github.com/go-git/go-git/v5/plumbing/object" + "github.com/go-git/go-git/v5/storage/memory" +) + +// FetchOptions defines options for fetching files from a git repo +type FetchOptions struct { + // RepoURL is the URL of the git repository + RepoURL string + + // PathPrefix is the folder within the repo to extract (e.g. "patterns/") + PathPrefix string + + // DestDir is where the files will be saved locally + DestDir string + + // SingleDirectory if true, only fetch files directly in the specified directory + // without recursing into subdirectories + SingleDirectory bool +} + +// FetchFilesFromRepo clones a git repo and extracts files from a specific folder +func FetchFilesFromRepo(opts FetchOptions) error { + // Ensure path prefix ends with slash + if !strings.HasSuffix(opts.PathPrefix, "/") { + opts.PathPrefix = opts.PathPrefix + "/" + } + + // Clone the repository in memory + r, err := git.Clone(memory.NewStorage(), nil, &git.CloneOptions{ + URL: opts.RepoURL, + Depth: 1, + }) + if err != nil { + return fmt.Errorf("failed to clone repository: %w", err) + } + + // Get HEAD reference + ref, err := r.Head() + if err != nil { + return fmt.Errorf("failed to get repository HEAD: %w", err) + } + + // Get commit object + commit, err := r.CommitObject(ref.Hash()) + if err != nil { + return fmt.Errorf("failed to get commit: %w", err) + } + + // Get the file tree + tree, err := commit.Tree() + if err != nil { + return fmt.Errorf("failed to get tree: %w", err) + } + + // Ensure destination directory exists + if err := os.MkdirAll(opts.DestDir, 0755); err != nil { + return fmt.Errorf("failed to create destination directory: %w", err) + } + + // Extract files from the tree + return tree.Files().ForEach(func(f *object.File) error { + // Only process files in the specified path + if !strings.HasPrefix(f.Name, opts.PathPrefix) { + return nil + } + + // For SingleDirectory mode, skip files in subdirectories + if opts.SingleDirectory { + remainingPath := strings.TrimPrefix(f.Name, opts.PathPrefix) + if strings.Contains(remainingPath, "/") { + return nil + } + } + + // Create local path for the file, removing the prefix + relativePath := strings.TrimPrefix(f.Name, opts.PathPrefix) + localPath := filepath.Join(opts.DestDir, relativePath) + + // Ensure directory structure exists + if err := os.MkdirAll(filepath.Dir(localPath), 0755); err != nil { + return err + } + + // Get file contents + reader, err := f.Reader() + if err != nil { + return err + } + defer reader.Close() + + // Create and write to local file + file, err := os.Create(localPath) + if err != nil { + return err + } + defer file.Close() + + _, err = io.Copy(file, reader) + return err + }) +} diff --git a/plugins/tools/patterns_loader.go b/plugins/tools/patterns_loader.go index 815bbbe7..3d9a1e0f 100644 --- a/plugins/tools/patterns_loader.go +++ b/plugins/tools/patterns_loader.go @@ -2,19 +2,13 @@ package tools import ( "fmt" - "io" "os" "path/filepath" - "sort" - "strings" "github.com/danielmiessler/fabric/plugins" "github.com/danielmiessler/fabric/plugins/db/fsdb" + "github.com/danielmiessler/fabric/plugins/tools/githelper" - "github.com/go-git/go-git/v5" - "github.com/go-git/go-git/v5/plumbing" - "github.com/go-git/go-git/v5/plumbing/object" - "github.com/go-git/go-git/v5/storage/memory" "github.com/otiai10/copy" ) @@ -89,7 +83,7 @@ func (o *PatternsLoader) Setup() (err error) { // PopulateDB downloads patterns from the internet and populates the patterns folder func (o *PatternsLoader) PopulateDB() (err error) { - fmt.Printf("Downloading patterns and Populating %s..\n", o.Patterns.Dir) + fmt.Printf("Downloading patterns and Populating %s...\n", o.Patterns.Dir) fmt.Println() if err = o.gitCloneAndCopy(); err != nil { return @@ -148,156 +142,20 @@ func (o *PatternsLoader) movePatterns() (err error) { } func (o *PatternsLoader) gitCloneAndCopy() (err error) { - // Clones the given repository, creating the remote, the local branches - // and fetching the objects, everything in memory: - var r *git.Repository - if r, err = git.Clone(memory.NewStorage(), nil, &git.CloneOptions{ - URL: o.DefaultGitRepoUrl.Value, - }); err != nil { - fmt.Println(err) - return + // Create temp folder if it doesn't exist + if err = os.MkdirAll(filepath.Dir(o.tempPatternsFolder), os.ModePerm); err != nil { + return fmt.Errorf("failed to create temp directory: %w", err) } - // ... retrieves the branch pointed by HEAD - var ref *plumbing.Reference - if ref, err = r.Head(); err != nil { - fmt.Println(err) - return - } - - // ... retrieves the commit history for /patterns folder - var cIter object.CommitIter - if cIter, err = r.Log(&git.LogOptions{ - From: ref.Hash(), - PathFilter: func(path string) bool { - return path == o.DefaultFolder.Value || strings.HasPrefix(path, o.pathPatternsPrefix) - }, - }); err != nil { - fmt.Println(err) - return err - } - - var changes []fsdb.DirectoryChange - // ... iterates over the commits - if err = cIter.ForEach(func(c *object.Commit) (err error) { - // GetApplyVariables the files changed in this commit by comparing with its parents - parentIter := c.Parents() - if err = parentIter.ForEach(func(parent *object.Commit) (err error) { - var patch *object.Patch - if patch, err = parent.Patch(c); err != nil { - fmt.Println(err) - return - } - - for _, fileStat := range patch.Stats() { - if strings.HasPrefix(fileStat.Name, o.pathPatternsPrefix) { - dir := filepath.Dir(fileStat.Name) - changes = append(changes, fsdb.DirectoryChange{Dir: dir, Timestamp: c.Committer.When}) - } - } - return - }); err != nil { - fmt.Println(err) - return - } - return - }); err != nil { - fmt.Println(err) - return - } - - // Sort changes by timestamp - sort.Slice(changes, func(i, j int) bool { - return changes[i].Timestamp.Before(changes[j].Timestamp) + // Use the helper to fetch files + err = githelper.FetchFilesFromRepo(githelper.FetchOptions{ + RepoURL: o.DefaultGitRepoUrl.Value, + PathPrefix: o.DefaultFolder.Value, + DestDir: o.tempPatternsFolder, }) - - if err = o.makeUniqueList(changes); err != nil { - return + if err != nil { + return fmt.Errorf("failed to download patterns: %w", err) } - var commit *object.Commit - if commit, err = r.CommitObject(ref.Hash()); err != nil { - fmt.Println(err) - return - } - - var tree *object.Tree - if tree, err = commit.Tree(); err != nil { - fmt.Println(err) - return - } - - if err = tree.Files().ForEach(func(f *object.File) (err error) { - if strings.HasPrefix(f.Name, o.pathPatternsPrefix) { - // Create the local file path - localPath := filepath.Join(os.TempDir(), f.Name) - - // Create the directories if they don't exist - if err = os.MkdirAll(filepath.Dir(localPath), os.ModePerm); err != nil { - fmt.Println(err) - return - } - - // Write the file to the local filesystem - var blob *object.Blob - if blob, err = r.BlobObject(f.Hash); err != nil { - fmt.Println(err) - return - } - err = o.writeBlobToFile(blob, localPath) - return - } - - return - }); err != nil { - fmt.Println(err) - } - - return -} - -func (o *PatternsLoader) writeBlobToFile(blob *object.Blob, path string) (err error) { - var reader io.ReadCloser - if reader, err = blob.Reader(); err != nil { - return - } - defer reader.Close() - - // Create the file - var file *os.File - if file, err = os.Create(path); err != nil { - return - } - defer file.Close() - - // Copy the contents of the blob to the file - if _, err = io.Copy(file, reader); err != nil { - return - } - return -} - -func (o *PatternsLoader) makeUniqueList(changes []fsdb.DirectoryChange) (err error) { - uniqueItems := make(map[string]bool) - for _, change := range changes { - if strings.TrimSpace(change.Dir) != "" && !strings.Contains(change.Dir, "=>") { - pattern := strings.ReplaceAll(change.Dir, o.pathPatternsPrefix, "") - pattern = strings.TrimSpace(pattern) - uniqueItems[pattern] = true - } - } - - finalList := make([]string, 0, len(uniqueItems)) - for _, change := range changes { - pattern := strings.ReplaceAll(change.Dir, o.pathPatternsPrefix, "") - pattern = strings.TrimSpace(pattern) - if _, exists := uniqueItems[pattern]; exists { - finalList = append(finalList, pattern) - delete(uniqueItems, pattern) // Remove to avoid duplicates in the final list - } - } - - joined := strings.Join(finalList, "\n") - err = os.WriteFile(o.Patterns.UniquePatternsFilePath, []byte(joined), 0o644) - return + return nil } diff --git a/plugins/tools/to_pdf/to_pdf.go b/plugins/tools/to_pdf/to_pdf.go index 6963227b..0666ec42 100644 --- a/plugins/tools/to_pdf/to_pdf.go +++ b/plugins/tools/to_pdf/to_pdf.go @@ -1,3 +1,23 @@ +// to_pdf +// +// Usage: +// [no args] Read from stdin, write to output.pdf +//