## CHANGES
- Remove duplicate comment about reading response body
- Preallocate slice capacity in extractModelIDs function
- Initialize modelIDs slice with known capacity
## CHANGES
- Add proper bounds checking for response body truncation
- Prevent slice out of bounds errors in error messages
- Add ellipsis indicator when response body is truncated
- Improve error message clarity for debugging purposes
## CHANGES
* Read response body once for efficiency
* Use io.ReadAll for response body
* Unmarshal json from bodyBytes
* Return error with raw response bytes
* Improve error handling for json parsing
## CHANGES
- Increase error response limit from 500 to 1024 bytes
- Add documentation comment for ErrorResponseLimit constant
- Remove unnecessary error return from extractModelIDs function
- Fix return statements in DirectlyGetModels parsing logic
- Add TODO comment for proper context handling
- Simplify model ID extraction without error propagation
## CHANGES
- Add provider name to API base URL error message
- Enhance error context for better debugging experience
- Include GetName() method call in error formatting
- Add `DirectlyGetModels` function to handle non-standard API responses
- Add Together provider configuration to ProviderMap
- Implement fallback to direct model fetching when standard method fails
## CHANGES
- Replace println with fmt.Fprintln to stderr for errors
- Use os.MkdirTemp for secure temporary directory creation
- Remove unused time import from patterns loader
- Add proper error wrapping for file operations
- Handle RemoveAll errors with warning messages
- Improve error messages with context information
- Add explicit error checking for cleanup operations
### CHANGES
- Add early return if registry is nil to prevent panics.
- Introduce early return for non-chat tool operations.
- Update error message to use original input on HTML readability failure.
- Enhance error wrapping for playlist video fetching.
- Modify temp patterns folder name with timestamp for uniqueness.
- Improve error handling for patterns directory access.
### CHANGES
* Extract chat processing logic into separate function
* Create modular command handlers for setup, configuration, listing, management, and extensions
* Improve patterns loader with migration support and better error handling
* Simplify main CLI logic by delegating to specialized handlers
* Enhance code organization and maintainability
* Add tool processing for YouTube and web scraping functionality
### CHANGES
- Modify trigger path to `data/patterns/**`
- Update `git diff` command to new path
- Change zip command to include `data/patterns/` directory
### CHANGES
- Document required Homebrew formula update for new structure.
- Add new `go install` commands for all tools.
- Specify new build path is `./cmd/fabric`.
- Include link to the draft Homebrew PR.
## CHANGES
- Fix static directory path in extract_patterns.py script
- Add apply_ul_tags pattern for content categorization
- Add t_check_dunning_kruger pattern for bias analysis
- Update pattern descriptions with new entries
- Sync web static data with latest patterns
- Include pattern extracts for new functionality
- Support standardized content topic classification
- Enable cognitive bias identification capabilities
## CHANGES
- Mark all 10 migration steps as completed
- Add restructuring completion status section
- Move pattern generation scripts to pattern_descriptions
- Update completion checkmarks throughout migration plan
- Document remaining external packaging verification tasks
- Consolidate pattern description files under new directory
- Confirm all binaries compile and tests pass
- Note standard Go project layout achieved
## CHANGES
- Move domain types from common to domain package
- Move utility functions from common to util package
- Update all import statements across codebase
- Reorganize OAuth storage functionality into util package
- Move file management functions to domain package
- Update test files to use new package structure
- Maintain backward compatibility for existing functionality
### CHANGES
- Introduce `cmd` directory for all main application binaries.
- Move all Go packages into the `internal` directory.
- Rename the `restapi` package to `server` for clarity.
- Consolidate patterns and strategies into a new `data` directory.
- Group all auxiliary scripts into a new `scripts` directory.
- Move all documentation and images into a `docs` directory.
- Update all Go import paths to reflect the new structure.
- Adjust CI/CD workflows and build commands for new layout.
### CHANGES
- Remove redundant `close(responseChan)` in `Send` method
- Update `SendStream` to close `responseChan` properly
- Modify test to reflect channel closure logic
## CHANGES
- Rename `doneChan` variable to `done` for consistency
- Add `streamChunks` field to mock vendor struct
- Implement chunk sending logic in mock SendStream method
- Add comprehensive streaming success aggregation test case
- Verify message aggregation from multiple stream chunks
- Test assistant response role and content validation
- Ensure proper session handling in streaming scenarios
### CHANGES
- Replace for-range loop with a non-blocking select statement.
- Process message and error channels concurrently for better handling.
- Improve the robustness of streaming error detection.
- Exit loop cleanly when the message channel closes.
## CHANGES
- Add IsConfigured check to vendor configuration loop
- Implement IsConfigured method for Anthropic client validation
- Remove conditional API key requirement based on OAuth
- Add automatic OAuth flow when no valid token
- Validate both API key and OAuth token configurations
- Simplify API key setup question logic
- Add token expiration checking with 5-minute buffer
## CHANGES
- Add zero-value check before setting TopP parameter
- Prevent sending TopP when value is zero
- Apply fix to both chat completions method
- Apply fix to response parameters method
- Ensure consistent parameter handling across OpenAI calls
## CHANGES
- Add detailed instructions for bug reproduction steps
- Include operating system dropdown with specific architectures
- Add OS version textarea with command examples
- Create installation method dropdown with all options
- Replace version checkbox with proper version output field
- Improve formatting and organization of form sections
- Add helpful links to installation documentation
## CHANGES
- Add custom patterns directory support via environment variable
- Implement custom patterns plugin with registry integration
- Override main patterns with custom directory patterns
- Expand home directory paths in custom patterns config
- Add comprehensive test coverage for custom patterns functionality
- Integrate custom patterns into plugin setup workflow
- Support pattern precedence with custom over main patterns
## CHANGES
- Remove OAuth transport implementation from main client
- Extract OAuth flow functions to separate module
- Remove unused imports and constants from client
- Replace inline OAuth transport with NewOAuthTransport call
- Update runOAuthFlow to exported RunOAuthFlow function
- Clean up token management and refresh logic
- Simplify client configuration by removing OAuth internals
### CHANGES
- Remove redundant base URL trimming logic
- Append base URL directly without modification
- Eliminate conditional check for API version suffix
- Move golang.org/x/oauth2 from indirect to direct dependency
- Add OAuth login option for Anthropic client
- Implement PKCE OAuth flow with browser integration
- Add custom HTTP transport for OAuth Bearer tokens
- Support both API key and OAuth authentication methods
- Add Claude Code system message for OAuth sessions
- Update REST API to handle OAuth tokens
- Improve environment variable name sanitization with regex
## CHANGES
• Extract hardcoded model lists into shared constant
• Create ImageGenerationSupportedModels variable for reusability
• Update supportsImageGeneration function to use shared constant
• Refactor error messages to reference centralized model list
• Add documentation comment for supported models variable
• Import strings package in test file
• Consolidate duplicate model validation logic across files
### CHANGES
- Add model field to `BuildChatOptions` method
- Implement `supportsImageGeneration` function for model checks
- Validate model supports image generation in `sendResponses`
- Remove `mars-colony.png` from repository
- Add tests for `supportsImageGeneration` function
- Validate image file support in `TestModelValidationLogic`
### CHANGES
- Define `ImageGenerationResponseType` constant for response handling
- Define `ImageGenerationToolType` constant for tool type usage
- Update `addImageGenerationTool` to use defined constants
- Refactor `extractAndSaveImages` to use response type constant
## CHANGES
- Add web search tool for Anthropic and OpenAI models
- Add search location parameter for web search results
- Add image file output option with format support
- Update zsh completion with new search and image flags
- Update bash completion with new option handling logic
- Update fish completion with search and image descriptions
- Support PNG, JPG, JPEG, GIF, BMP image formats
## CHANGES
- Add `--image-file` flag for saving generated images
- Implement image generation tool integration with OpenAI
- Support image editing with attachment input files
- Add comprehensive test coverage for image features
- Update documentation with image generation examples
- Fix HTML formatting issues in README
- Improve PowerShell code block indentation
- Clean up help text formatting and spacing
## CHANGES
- Enable web search tool for OpenAI models
- Add location parameter support for search results
- Extract and format citations from search responses
- Implement citation deduplication to avoid duplicates
- Add comprehensive test coverage for search functionality
- Update CLI flag description to include OpenAI
- Format citations as markdown links with sources
### CHANGES
- Add `sourcesHeader` constant for citation section title.
- Use `strings.Builder` to construct result efficiently.
- Append sources header and citations in result builder.
- Update `ret` to use constructed string from builder.
## CHANGES
- Add --search flag to enable web search
- Add --search-location for timezone-based results
- Pass search options through ChatOptions struct
- Implement web search tool in Anthropic client
- Format search citations with sources section
- Add comprehensive tests for search functionality
- Remove plugin-level web search configuration
### CHANGES
- Add `review_code`, `extract_alpha`, and `extract_mcp_servers` patterns.
- Refactor the pattern extraction script for improved clarity.
- Add docstrings and specific error handling to script.
- Improve formatting in the pattern management README.
- Fix typo in the `analyze_bill_short` pattern description.
### CHANGES
- Enhance user message conversion to support multi-content.
- Add capability to process image URLs in messages.
- Build multi-part messages with both text and images.
## CHANGES
- Extract shared message conversion to convertMessageCommon
- Reuse logic between chat and response APIs
- Maintain existing text-only behavior for chat
- Support multi-content messages in response API
- Reduce code duplication across converters
- Preserve backward compatibility for both APIs
## CHANGES
* Add chat completions API fallback for non-Responses API providers
* Implement `sendChatCompletions` and `sendStreamChatCompletions` methods
* Introduce `buildChatCompletionParams` to construct API request parameters
* Add `ImplementsResponses` flag to track provider API capabilities
* Update provider configurations with Responses API support status
* Enhance `Send` and `SendStream` methods to use appropriate API endpoints
- Replace chat completions with responses API
- Update message conversion to new format
- Refactor streaming to handle event types
- Remove frequency and presence penalty params
- Replace seed parameter with max tokens
- Update test cases for new API
- Add response text extraction method
### CHANGES
- Introduce local `chat` package for message abstraction
- Replace sashabaranov/go-openai with official openai-go SDK
- Update OpenAI, Azure, and Exolab plugins for new client
- Refactor all AI providers to use internal chat types
- Decouple codebase from third-party AI provider structs
- Replace deprecated `ioutil` functions with `os` equivalents
### CHANGES
* Check if a release exists before attempting creation.
* Suppress error output from `gh release view` command.
* Add an informative log when release already exists.
## CHANGES
- Add slices import for array operations
- Define new search preview model names
- Add mini search preview variants
- Include deep research model support
- Add June 2025 dated model versions
- Replace hardcoded check with slices.Contains
- Support both prefix and exact model matching
New pattern to extract mentions of MCP (Model Context Protocol) servers from content. Identifies server names, features, capabilities, and usage examples.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
## CHANGES
- Add new YouTube handler for transcript requests
- Create `/youtube/transcript` POST endpoint route
- Add request/response types for YouTube API
- Support language and timestamp options
- Update frontend to use new endpoint
- Remove chat endpoint dependency for transcripts
- Validate video vs playlist URLs properly
### CHANGES
- Unify assistant and user message formatting logic.
- Use `formatMultiContentMessage` for assistant role messages.
- Improve dryrun support for multi-part message content.
### CHANGES
- Combine user text and attachments into MultiContent.
- Preserve existing non-text parts like images.
- Use standard content field for text-only messages.
### CHANGES
- Handle multi-content messages for the user role.
- Display image URLs from user messages in output.
- Update both `Send` and `SendStream` methods.
- Retain existing behavior for simple text messages.
- Allow user messages and attachments with patterns.
- Append user message to session regardless of pattern.
- Refactor chat request builder for improved clarity.
### CHANGES
- Reformat JSON `tags` array to display on new lines.
- Update `write_essay` pattern description for clarity.
- Apply consistent formatting to both data files.
## CHANGES
- Rename `write_essay` to `write_essay_pg` for Paul Graham style
- Rename `write_essay_by_author` to `write_essay` with author variable
- Update pattern descriptions to reflect naming changes
- Fix duplicate `write_essay_pg` entry in pattern descriptions
### CHANGES
- Add tags and descriptions for five new creative and analytical patterns.
- Introduce `analyze_terraform_plan` for infrastructure review.
- Add `write_essay_by_author` for stylistic writing.
- Include `summarize_board_meeting` for corporate notes.
- Introduce `create_mnemonic_phrases` for memory aids.
- Update and clean pattern description data files.
- Sort the pattern explanations list alphabetically.
# CHANGES
- Add `os` and `strings` packages to imports
- Implement dynamic URL handling with environment variables
- Refactor provider configuration to support URL templates
- Reorder providers for consistent key order in ProviderMap
- Extract and parse template variables from BaseURL
- Use environment variables or default values for templates
- Replace template with actual values in BaseURL
# CHANGES
- Add `os` and `strings` packages to imports
- Implement dynamic URL handling with environment variables
- Refactor provider configuration to support URL templates
- Reorder providers for consistent key order in ProviderMap
- Extract and parse template variables from BaseURL
- Use environment variables or default values for templates
- Replace template with actual values in BaseURL
- Refactor `cleanPatternOutput` to use a dedicated return variable.
- Hoist `processResponse` function for improved stream readability.
- Remove unnecessary whitespace and trailing newlines from file.
## CHANGES
- Create `PatternApplyRequest` struct for request body parsing
- Implement `ApplyPattern` method for POST /patterns/:name/apply
- Register manual routes for pattern operations in `NewPatternsHandler`
- Refactor `Get` method to return raw pattern content
- Merge query parameters with request body variables in `ApplyPattern`
- Use `StorageHandler` for pattern-related storage operations
## CHANGES
- Add Variables field to PromptRequest struct
- Pass pattern variables through chat handler
- Create API variables documentation example
- Add pattern variables UI in web interface
- Create pattern variables store in Svelte
- Include variables in chat service requests
- Add JSON textarea for variable input
## CHANGES
- Add citation extraction from API responses
- Append citations section to response content
- Format citations as numbered markdown list
- Handle citations in streaming responses
- Store last response for citation access
- Add citations after stream completion
- Maintain backward compatibility with responses
### CHANGES
- Add instructions for configuring Perplexity AI with Fabric
- Include example command for querying Perplexity AI
- Retain existing instructions for YouTube transcription changes
## CHANGES
- feat: Add `MaxTokens` field to `ChatOptions` struct for response control
- feat: Integrate Perplexity client into core plugin registry initialization
- build: Add perplexity-go/v2 dependency to enable API interactions
- feat: Implement stream handling in Perpexlty client using sync.WaitGroup
- fix: Correct parameter types for penalty options in API requests
## LINKS
<https://github.com/sgaunet/perlexipty-go> - Client library used
## CHANGES
- Extract shared yt-dlp logic into tryMethodYtDlpInternal helper
- Add processVTTFileFunc parameter for flexible VTT processing
- Implement language matching for 2-char language codes
- Refactor tryMethodYtDlp to use new helper function
- Refactor tryMethodYtDlpWithTimestamps to use helper
- Reduce code duplication between transcript methods
- Maintain existing functionality with cleaner structure
### CHANGES
- Add concurrency control to prevent simultaneous runs
- Pull latest main branch changes before tagging
- Fetch all remote tags before calculating version
## CHANGES
- Add Save method to PatternsEntity struct
- Create pattern directory with proper permissions
- Write pattern content to system pattern file
- Add comprehensive test for Save functionality
- Verify directory creation and file contents
- Handle errors for directory and file operations
Add a new pattern for generating mnemonic phrases from diceware words. This includes two markdown files defining the user guide, and system implementation details.
## CHANGES
- Replace hardcoded `/tmp` with `os.TempDir()` for paths
- Use `filepath.Join()` instead of string concatenation
- Remove Unix `find` command dependency completely
- Add new `findVTTFiles()` method using `filepath.Walk()`
- Make VTT file discovery work on Windows
- Improve error handling for file operations
- Maintain backward compatibility with existing functionality
This is a merge commit the virtual branches in your workspace.
Due to GitButler managing multiple virtual branches, you cannot switch back and
forth between git branches and virtual branches easily.
If you switch to another branch, GitButler will need to be reinitialized.
If you commit on this branch, GitButler will throw it away.
Here are the branches that are currently applied:
- improve-create-prd (refs/gitbutler/improve-create-prd)
For more information about what we're doing here, check out our docs:
https://docs.gitbutler.com/features/virtual-branches/integration-branch
The changes in this commit expand the identity and purpose of the PRD Generator
to provide more clarity on its role and the expected output. The key changes
include:
- Defining the Generator's purpose as transforming product ideas into a
structured PRD that ensures clarity, alignment, and precision in product
planning and execution.
- Outlining the key sections typically found in a PRD that the Generator should
cover, such as Overview, Objectives, Target Audience, Features, User Stories,
Functional and Non-functional Requirements, Success Metrics, and Timeline.
- Providing more detailed instructions on the expected output format, structure,
and content, including the use of Markdown, labeled sections, bullet points,
tables, and highlighting of priorities or MVP features.
- Create new pattern for analyzing Terraform plans
- Add identity defining expert plan analyzer role
- Include focus on security, cost, and compliance
- Define three output sections for summaries
- Specify 20-word sentence summary requirement
- List 10 critical changes with word limits
- Include 5 key takeaways section format
- Add markdown formatting output instructions
- Require numbered lists over bullet points
- Prohibit warnings and duplicate content
## CHANGES
- Add AIML provider configuration
- Set AIML base URL to api.aimlapi.com/v1
- Expand supported OpenAI compatible providers list
- Enable AIML API integration support
### CHANGES
- Add `.browserslistrc` to define target browser versions.
- Upgrade `pdfjs-dist` dependency from v2.16 to v4.2.67.
- Upgrade `nanoid` dependency from v4.0.2 to v5.0.9.
- Introduce `pdf-config.ts` for centralized PDF.js worker setup.
- Refactor `PdfConversionService` to use new PDF worker configuration.
- Add static `pdf.worker.min.mjs` to serve PDF.js worker.
- Update Vite configuration for ESNext build target and PDF.js.
- Create environment config module for URL handling
- Add getFabricBaseUrl() function with server/client support
- Add getFabricApiUrl() helper for API endpoints
- Configure Vite to inject FABRIC_BASE_URL client-side
- Update proxy targets to use environment variable
- Add TypeScript definitions for window config
- Support FABRIC_BASE_URL env var with fallback
## CHANGES
- Add model-specific raw mode detection logic
- Check Ollama llama2/llama3 models for raw mode
- Check OpenAI o1/o3/o4 models for raw mode
- Use model from options or default chatter
- Auto-enable raw mode when vendor requires it
- Import strings package for prefix matching
## CHANGES
- Add NeedsRawMode to Vendor interface
- Implement NeedsRawMode in all AI clients
- Return false for all implementations
- Support model-specific raw mode detection
- Enable future raw mode requirements
CHANGES
- Upgrade `anthropic-sdk-go` dependency to version `v1.2.0`.
- Integrate new Anthropic Claude 4 Opus and Sonnet models.
- Remove deprecated Claude 2.0 and 2.1 models from list.
- Adjust model type casting for `anthropic-sdk-go v1.2.0` compatibility.
- Refresh README: announce Claude 4, update date, fix links.
## CHANGES
- Fix system message handling with patterns in raw mode
- Prevent duplicate inputs when using patterns
- Add conditional logic for pattern vs non-pattern scenarios
- Simplify message construction with clearer variable names
- Improve code comments for better readability
- Improved formatting of the introduction and content summary sections for better flow.
- Consolidated repetitive sentences and enhanced the overall coherence of the text.
- Adjusted bullet points and numbering for consistency and easier comprehension.
- Ensured that key concepts are clearly articulated and visually distinct to aid understanding.
### CHANGES
- Add `getSortedGroupsItems` to centralize sorting logic.
- Sort groups and items alphabetically, case-insensitive.
- Replace inline sorting in `Print` with new method.
- Update `GetGroupAndItemByItemNumber` to use sorted data.
- Ensure original `GroupsItems` remains unmodified.
CHANGES:
- Add shell completion support for three major shells
- Create standardized completion scripts in completions/ directory
- Add --shell-complete-list flag for machine-readable output
- Update Print() methods to support plain output format
- Document installation steps for each shell in README
- Replace old fish completion script with improved version
CHANGES
* Define `getGoVersion` function in `flake.nix`.
* Use `getGoVersion` to set Go version consistently.
* Pass `goVersion` explicitly into `nix/shell.nix`.
* Remove redundant Go version definition from `shell.nix`.
Update Go version across Dockerfile, Nix configurations, and Go modules.
Refresh dependencies and Nix flake inputs.
CHANGES:
* Update Go version to 1.24.2 in Dockerfile.
* Set Go version to 1.24.0 and toolchain to 1.24.2.
* Refresh Go module dependencies and sums (go.mod, go.sum).
* Update Nix flake lock file inputs.
* Configure Nix environment and packages for Go 1.24.
* Update gomod2nix lock file with dependency hashes.
* Use Go 1.24 in Nix development shell environment.
## CHANGES
- refactor BuildSession raw mode to prepend system to user content
- ensure raw mode messages always have User role
- keep existing user message when no systemMessage provided
- append systemMessage separately in non-raw mode sessions
- store original cmd.Env before context-based exec command creation
- recreate exec command with context then restore originalEnv
- add comments clarifying raw vs non-raw handling behavior
## CHANGES
- Upgrade Anthropic SDK from alpha.11 to beta.3
- Update API endpoint from v1 to v2
- Replace anthropic.F() with direct assignment
- Replace anthropic.F() with anthropic.Opt() for optional params
- Simplify event delta handling in streaming
- Change client type from pointer to value type
- Update comment with SDK changelog reference
CHANGES
* Import `sort` and `strings` packages for sorting functionality.
* Sort retrieved AI model names alphabetically, ignoring case.
* Ensure consistent ordering of AI models in lists.
### CHANGES
- Add `Prompt` field to `StrategyMeta` struct.
- Include `strings` package for filename processing.
- Derive strategy name from filename using `strings.TrimSuffix`.
- Store `Prompt` value from JSON data in `StrategyMeta`
### CHANGES
- Import `sort` and `strings` packages for sorting functionality.
- Create a copy of groups for stable sorting.
- Sort groups alphabetically in a case-insensitive manner.
- Create a copy of items within each group for sorting.
- Sort items alphabetically in a case-insensitive manner.
- Iterate over sorted groups and items for display.
### CHANGES
- Introduce `--listvendors` flag to display all AI vendors.
- Refactor OpenAI-compatible providers into a unified configuration.
- Remove individual vendor packages for streamlined management.
- Add sorting for consistent vendor listing output.
- Update documentation to include new `--listvendors` option.
Bumps the go_modules group with 1 update in the / directory: [golang.org/x/net](https://github.com/golang/net).
Updates `golang.org/x/net` from 0.36.0 to 0.38.0
- [Commits](https://github.com/golang/net/compare/v0.36.0...v0.38.0)
---
updated-dependencies:
- dependency-name: golang.org/x/net
dependency-version: 0.38.0
dependency-type: indirect
dependency-group: go_modules
...
Signed-off-by: dependabot[bot] <support@github.com>
2025-04-16 23:25:39 +00:00
549 changed files with 17927 additions and 7991 deletions
Thanks for taking the time to fill out this bug report!
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This document captures the SPQA policy and State for Alma Security, a security startup out of Redwood City, Ca.
This is part of the SPQA context that will be used to answer questions and create artifacts for the company, e.g., company strategy, security strategy, quarterly security reports (QSRs), project plans, recommendations on which projects to undertake, which investments to take and avoid, and other such decisions.
A major aspect of the SPQA system is the definition of the company's mission, goals, KPIs, and challenges. These shape everything within the company and thus should be used to shape the recommendations made when asked.
In addition to the clearly stated goals and other defining characteristics listed above, there will also be a streaming list of updates coming into this system using the Activity document.
Those will be changes, updates, or modifications to the direction of the company. For example, if Goal number 4 is to build a new datacenter in Boise, Idaho, but we see an update in the Activity section that says we've lost the ability to build in Boise, we should consider goal #4 out of the picture for prioritization and other decision purposes. In other words, the streaming activity log into this document should be considered updates to the core content.
## Company History
Alma Security was started by Chris Meyers, who was previously at Sigma Systems as CTO and HPE as a senior security engineer.
He started the company because, "I saw a gap in the authentication market, where companies were only looking at one or two aspects of one's identity to do authentication. They we're looking at the whole picture and turning that into a continuous authentication story."
## Company Mission
The mission of Alma Security is to ensure businesses can continuously authenticate their users using their whole selves.
## Company Goals (G1 means goal 1, G2 is goal 2, etc. Treat each item (goal/kpi/etc) as half as important as the one before it.)
NOTE: Some goals are things like project rollout which serve the higher goals. In that case they shouldn't always be considered so much lower priority because one is serving the other.
## Company Goals
- G1: Achieve 20% market share by January 2025
- G2: Hit 10000 active customers by January 2025
- G3: Hit a customer trust score of 90+% by January 2025
- G4: Get churn below 5% by August 2024
- G5: Launch in Europe by August 2024
- G6: Launch in India by November 2024
- G7: Launch Mood-monitor integration by February 2024
- G8: Launch partnership with Apple Passkeys by June 2024
## Risk Register (The things we're most worried about)
- R1: Our infrastructure security team is understaffed by 50% after 5 key people left
- R2: We are not currently monitoring our external perimeter for attack surface related vulnerabilities like open ports, listening applications, unknown hosts, unknown subdomains pointing to these things, etc. We only do scans once every couple of months and we don't really have anyone to look at the results
- R3: It takes us multiple days to investigate potential malicious behavior on our systems.
- R4: We lack a full list of our assets, including externally facing hosts, S3 buckets, etc., which make up our attack surface
- R5: We have a low public trust score due to the events of 2022.
## Security Team Narrative
### Background
Alma hired a new security team starting in January of 2023 and we have been building out the program since then. The philosophy and approach for the security team is to explicitly articulate what we believe the highest risks are to Alma, to deploy targeted strategies to address those risks, and to use clear, transparent KPIs to show progress towards our goals over time.
### Current Risks
So our risk register looks like this:
1. We are understaffed by 50% after 5 key people left in 2022
2. Our perimeter is not being monitored for attack surface related vulnerabilities
3. It takes us too long to detect and start investigating malicious behavior on our systems
4. We do not have a full list of our assets, which makes it difficult to know what we need to protect
5. We have a low public trust score due to the events of 2022
### Strategies
As such, our strategies are as follows:
1. Hire 5 more A-tier security professionals
2. Purchase and implement an attack surface management solution
3. Invest in our detection and response capabilities
4. Purchase an asset inventory system that integrates with our attack surface management tool
5. Leverage PR to share as much of our progress as possible with the public to rebuild trust
### How We're Doing
We believe being transparent about our progress is key to everything, and for that reason we maintain a limited number of KPIs that we update every quarter. These metrics will not change often. They will remain consistent so that it's easy to track how we're spending our resources and the progress we're making.
Those KPIs are:
1. Time to detect malicious behavior
2. Time to start investigating malicious behavior
3. Time to remediate critical vulnerabilities on crown jewel systems
4. Time to remediate critical vulnerabilities on all systems
5. Our public trust score
As of $DATE$, our KPIs for these are currently:
$GIVE CURRENT KPIs from the Activity section below$
$INSERT GRAPHS OF KPI PROGRESS OVER TIME HERE$
## Security Team Strategies
- STS1: Hire 5 more A-tier security professionals
- STS2: Purchase an attack surface management solution
- STS3: Invest in our detection and response capabilities
- STS4: Purchase an asset inventory system that integrates with our attack surface management tool
## Infrastructure Notes (a basic description of our tech stack and various context around it)
- We currently have no WAF protecting our main web app but we're considering adding one
- We have had some issues with S3 buckets becoming public, or being set up as public, which has lead to some close calls with customer data almost being exposed.
- alma.amazon-domain.com is our primary S3 bucket that contains everything, but it's not public readable or listable
- We have a root account for our AWS account that doesn't yet have 2FA on it, but we're working on fixing that within a few weeks (but it's been open for a few months)
- We also use Postgres for all our databases.
- Developers have root access to the all kubernetes nodes via SSH on port 45,001 using a shared developer key issued during laptop provisioning.
- We're a kubernetes shop and do everything through AWS
- We're logging most stuff to Cloudtrail and we kind of use Amazon Guarduty, but we don't have a 24/7 team to monitor alerts and logs. We should add that to our list of challenges next time we update our overall policy
- We also have a Windows infrastructure because some key personnel came from Microsoft. The DC is hosted in our head office which is in Redwood City, and anyone who works in that office (most of the 300 employees) uses that to log in when they start work. The domain is ALMA.
- There's a domain-joined fileserver running Windows 2012 that most people use to upload new ideas and plans for new products. It uses Windows authentication from the domain.
- We use a Palo Alto Networks firewall with 2fa using windows authenticator tied to SSO.
- The name of the AI system doing all this context creation using SPQA is Alma, which is also the name of the company.
- We use Workday for HR stuff. Slack for realtime communications. Outlook 365 as a service. Sentinel One on the workstations and laptops. Servers in AWS are mostly Amazon Linux 2 with a few Ubuntu boxes that are a few years old.
- We also primarily use Postgres for all of our systems.
## Team
TEAM MEMBER | TEAM ASSIGNED | SKILLS | PAY LEVEL | LOCATION | PROJECTS
Chris Magann | Vulnerability Management | VM (Expert), AWS (Strong), Python (Basic), Postgres (Basic) | $212K | Redwood City
Tigan Wang | Vulnerability Management | VM (Expert), AWS (Strong), Python (Basic), Postgres (Basic) | $217K | Redwood City
## Projects
PROJECT NAME | PROJECT DESCRIPTION | PROJECT PRIORITY | PROJECT MEMBERS | START DATE | END DATE | STATUS | PROJECT COST
WAF Install | Install a WAF in front of our main web app | Critical | Nadia Khan | 2024-01-01 - Ongoing | In Progress | $112K one-time, $9K/month
Multi-Factor Authentication (MFA) Rollout | Implement MFA across all internal and external systems | Critical | Chris Magann | 2024-01-15 | 2024-05-01 | Planned | $80K one-time, $5K/month
Procure and Implement ASM | Implement continuous monitoring for attack surface vulnerabilities | High | Tigan Wang | 2024-02-15 | 2024-06-15 | Not Started | $75K one-time, $6K/month
Data Encryption Upgrade | Upgrade encryption protocols for all sensitive data | Medium | Nadia Khan | 2024-04-01 | 2024-08-01 | Planned | $95K one-time
Incident Response Enhancement | Develop and implement a 24/7 incident response team | High | Nadia Khan | 2024-03-01 | 2024-07-01 | In Progress | $150K one-time, $10K/month
Cloud Security Optimization | Optimize AWS cloud security configurations and practices | Medium | Tigan Wang | 2024-02-01 | 2024-06-01 | In Progress | $100K one-time, $8K/month
S3 Bucket Security | Review and secure all S3 buckets to prevent data breaches | High | Chris Magann | 2024-01-10 | 2024-04-10 | In Progress | $70K one-time, $5K/month
SQL Injection Mitigation | Implement measures to eliminate SQL injection vulnerabilities | High | Tigan Wang | 2024-01-20 | 2024-05-20 | Not Started | $60K one-time
## SECURITY POSTURE (To be referenced for compliance questions and security questionnaires)
July 2019
Admin accounts still not required to use 2FA.
Company laptops distributed to employees, no MDM yet for device management.
AWS IAM roles created for engineers, but root access still frequently used.
Started basic vulnerability scanning using open-source tools.
December 2019
MFA enforced for all Google Workspace accounts after a phishing attempt.
Introduced ClamAV for basic endpoint protection on corporate laptops.
AWS GuardDuty enabled for threat detection, but no formal incident response team.
First incident response plan table-top exercise conducted, but findings not fully documented.
April 2020
Migrated from Google Workspace to Office 365, with MFA enabled for all users.
Rolled out SentinelOne for endpoint protection on 50% of company laptops.
Implemented least-privilege access control for AWS IAM roles.
First formal vendor risk management review completed for major SaaS providers.
August 2020
Completed full deployment of SentinelOne across all endpoints.
Implemented AWS CloudWatch for real-time alerts; however, logs still not monitored 24/7.
Began encrypting all AWS S3 buckets at rest using server-side encryption.
First internal review of data retention policies, started drafting data disposal policy.
January 2021
Rolled out Jamf MDM for centralized management of macOS devices, enforcing encryption (FileVault) on all laptops.
Strengthened Office 365 security by implementing phishing-resistant MFA using authenticator apps.
<h4><code>fabric</code> is an open-source framework for augmenting humans using AI.</h4>
</p>
</div>
[Updates](#updates) •
[What and Why](#what-and-why) •
[Philosophy](#philosophy) •
[Installation](#Installation) •
[Usage](#Usage) •
[Installation](#installation) •
[Usage](#usage) •
[Examples](#examples) •
[Just Use the Patterns](#just-use-the-patterns) •
[Custom Patterns](#custom-patterns) •
[Helper Apps](#helper-apps) •
[Meta](#meta)
</div>
## What and why
Since the start of modern AI in late 2022 we've seen an **_extraordinary_** number of AI applications for accomplishing tasks. There are thousands of websites, chat-bots, mobile apps, and other interfaces for using all the different AI out there.
It's all really exciting and powerful, but _it's not easy to integrate this functionality into our lives._
<div class="align center">
<h4>In other words, AI doesn't have a capabilities problem—it has an <em>integration</em> problem.</h4>
</div>
**Fabric was created to address this by creating and organizing the fundamental units of AI—the prompts themselves!**
Fabric organizes prompts by real-world task, allowing people to create, collect, and organize their most important AI solutions in a single place for use in their favorite tools. And if you're command-line focused, you can use Fabric itself as the interface!
## Intro videos
Keep in mind that many of these were recorded when Fabric was Python-based, so remember to use the current [install instructions](#installation) below.
- [Breaking problems into components](#breaking-problems-into-components)
- [Too many prompts](#too-many-prompts)
@@ -59,12 +83,19 @@ Fabric is graciously supported by…
- [Save your files in markdown using aliases](#save-your-files-in-markdown-using-aliases)
- [Migration](#migration)
- [Upgrading](#upgrading)
- [Shell Completions](#shell-completions)
- [Zsh Completion](#zsh-completion)
- [Bash Completion](#bash-completion)
- [Fish Completion](#fish-completion)
- [Usage](#usage)
- [Our approach to prompting](#our-approach-to-prompting)
- [Examples](#examples)
- [Just use the Patterns](#just-use-the-patterns)
- [Prompt Strategies](#prompt-strategies)
- [Custom Patterns](#custom-patterns)
- [Setting Up Custom Patterns](#setting-up-custom-patterns)
- [Using Custom Patterns](#using-custom-patterns)
- [How It Works](#how-it-works)
- [Helper Apps](#helper-apps)
- [`to_pdf`](#to_pdf)
- [`to_pdf` Installation](#to_pdf-installation)
@@ -76,34 +107,36 @@ Fabric is graciously supported by…
- [Clipboard Support](#clipboard-support)
- [Meta](#meta)
- [Primary contributors](#primary-contributors)
- [Contributors](#contributors)
<br />
## Updates
> [!NOTE]
> April 16, 2025
>
> - Fabric now supports Grok (from XAI)! Update and use `-S` to select it as your default if you want, or just use the shortcut `-m grok-3-beta`. Enjoy!
## What and why
Since the start of 2023 and GenAI we've seen a massive number of AI applications for accomplishing tasks. It's powerful, but _it's not easy to integrate this functionality into our lives._
<div align="center">
<h4>In other words, AI doesn't have a capabilities problem—it has an <em>integration</em> problem.</h4>
</div>
Fabric was created to address this by enabling everyone to granularly apply AI to everyday challenges.
## Intro videos
Keep in mind that many of these were recorded when Fabric was Python-based, so remember to use the current [install instructions](#installation) below.
> - **Web Search**: Fabric now supports web search for Anthropic and OpenAI models using the `--search` and `--search-location` flags. This replaces the previous plugin-based search, so you may want to remove the old `ANTHROPIC_WEB_SEARCH_TOOL_*` variables from your `~/.config/fabric/.env` file.
> - **Image Generation**: Fabric now has powerful image generation capabilities with OpenAI.
> - Generate images from text prompts and save them using `--image-file`.
> - Edit existing images by providing an input image with `--attachment`.
> - Control image `size`, `quality`, `compression`, and `background` with the new `--image-*` flags.
>
>June 17, 2025
>
>- Fabric now supports Perplexity AI. Configure it by using `fabric -S` to add your Perplexity AI API Key,
> and then try:
>
> ```bash
> fabric -m sonar-pro "What is the latest world news?"
> ```
>
>June 11, 2025
>
>- Fabric's YouTube transcription now needs `yt-dlp` to be installed. Make sure to install the latest
> version (2025.06.09 as of this note). The YouTube API key is only needed for comments (the `--comments` flag)
> and metadata extraction (the `--metadata` flag).
## Philosophy
@@ -185,7 +218,7 @@ To install Fabric, [make sure Go is installed](https://go.dev/doc/install), and
```bash
# Install Fabric directly from the repo
go install github.com/danielmiessler/fabric@latest
go install github.com/danielmiessler/fabric/cmd/fabric@latest
```
### Environment Variables
@@ -395,7 +428,7 @@ pipx uninstall fabric
# Clear any old Fabric aliases
(check your .bashrc, .zshrc, etc.)
# Install the Go version
go install github.com/danielmiessler/fabric@latest
go install github.com/danielmiessler/fabric/cmd/fabric@latest
# Run setup for the new version. Important because things have changed
fabric --setup
```
@@ -407,7 +440,49 @@ Then [set your environmental variables](#environment-variables) as shown above.
The great thing about Go is that it's super easy to upgrade. Just run the same command you used to install it in the first place and you'll always get the latest version.
```bash
go install github.com/danielmiessler/fabric@latest
go install github.com/danielmiessler/fabric/cmd/fabric@latest
```
### Shell Completions
Fabric provides shell completion scripts for Zsh, Bash, and Fish
shells, making it easier to use the CLI by providing tab completion
for commands and options.
#### Zsh Completion
To enable Zsh completion:
```bash
# Copy the completion file to a directory in your $fpath
mkdir -p ~/.zsh/completions
cp completions/_fabric ~/.zsh/completions/
# Add the directory to fpath in your .zshrc before compinit
@@ -418,8 +493,7 @@ Once you have it all set up, here's how to use it.
fabric -h
```
```bash
```plaintext
Usage:
fabric [OPTIONS]
@@ -434,7 +508,9 @@ Application Options:
-T, --topp= Set top P (default: 0.9)
-s, --stream Stream
-P, --presencepenalty= Set presence penalty (default: 0.0)
-r, --raw Use the defaults of the model without sending chat options (like temperature etc.) and use the user role instead of the system role for patterns.
-r, --raw Use the defaults of the model without sending chat options (like
temperature etc.) and use the user role instead of the system role for
patterns.
-F, --frequencypenalty= Set frequency penalty (default: 0.0)
-l, --listpatterns List all patterns
-L, --listmodels List all available models
@@ -448,9 +524,12 @@ Application Options:
--output-session Output the entire session (also a temporary one) to the output file
-n, --latest= Number of latest patterns to list (default: 0)
-d, --changeDefaultModel Change default model
-y, --youtube= YouTube video or play list "URL" to grab transcript, comments from it and send to chat or print it put to the console and store it in the output file
-y, --youtube= YouTube video or play list "URL" to grab transcript, comments from it
and send to chat or print it put to the console and store it in the
output file
--playlist Prefer playlist over video if both ids are present in the URL
--transcript Grab transcript from YouTube video and send to chat (it is used per default).
--transcript Grab transcript from YouTube video and send to chat (it is used per
default).
--transcript-with-timestamps Grab transcript from YouTube video with timestamps and send to chat
--comments Grab comments from YouTube video and send to chat
--metadata Output video metadata
@@ -468,6 +547,7 @@ Application Options:
--serve Serve the Fabric Rest API
--serveOllama Serve the Fabric Rest API with ollama endpoints
--address= The address to bind the REST API (default: :8080)
--api-key= API key used to secure server routes
--config= Path to YAML config file
--version Print current version
--listextensions List all registered extensions
@@ -475,6 +555,16 @@ Application Options:
--rmextension= Remove a registered extension by name
--strategy= Choose a strategy from the available strategies
--liststrategies List all strategies
--listvendors List all vendors
--shell-complete-list Output raw list without headers/formatting (for shell completion)
--search Enable web search tool for supported models (Anthropic, OpenAI)
--search-location= Set location for web search results (e.g., 'America/Los_Angeles')
--image-file= Save generated image to specified file path (e.g., 'output.png')
--image-size= Image dimensions: 1024x1024, 1536x1024, 1024x1536, auto (default: auto)
--image-quality= Image quality: low, medium, high, auto (default: auto)
--image-compression= Compression level 0-100 for JPEG/WebP formats (default: not set)
--image-background= Background type: opaque, transparent (default: opaque, only for
PNG/WebP)
Help Options:
-h, --help Show this help message
@@ -538,7 +628,7 @@ Now let's look at some things you can do with Fabric.
<br />
<br />
If you're not looking to do anything fancy, and you just want a lot of great prompts, you can navigate to the [`/patterns`](https://github.com/danielmiessler/fabric/tree/main/patterns) directory and start exploring!
If you're not looking to do anything fancy, and you just want a lot of great prompts, you can navigate to the [`/patterns`](https://github.com/danielmiessler/fabric/tree/main/data/patterns) directory and start exploring!
We hope that if you used nothing else from Fabric, the Patterns by themselves will make the project useful.
@@ -554,7 +644,7 @@ be used in addition to the basic patterns.
See the [Thinking Faster by Writing Less](https://arxiv.org/pdf/2502.18600) paper and
the [Thought Generation section of Learn Prompting](https://learnprompting.org/docs/advanced/thought_generation/introduction) for examples of prompt strategies.
Each strategy is available as a small `json` file in the [`/strategies`](https://github.com/danielmiessler/fabric/tree/main/strategies) directory.
Each strategy is available as a small `json` file in the [`/strategies`](https://github.com/danielmiessler/fabric/tree/main/data/strategies) directory.
The prompt modification of the strategy is applied to the system prompt and passed on to the
LLM in the chat session.
@@ -565,11 +655,48 @@ Use `fabric -S` and select the option to install the strategies in your `~/.conf
You may want to use Fabric to create your own custom Patterns—but not share them with others. No problem!
Just make a directory in `~/.config/custompatterns/` (or wherever) and put your `.md` files in there.
Fabric now supports a dedicated custom patterns directory that keeps your personal patterns separate from the built-in ones. This means your custompatterns won't be overwritten when you update Fabric's built-in patterns.
When you're ready to use them, copy them into `~/.config/fabric/patterns/`
### Setting Up Custom Patterns
You can then use them like any other Patterns, but they won't be public unless you explicitly submit them as Pull Requests to the Fabric project. So don't worry—they're private to you.
1. Run the Fabric setup:
```bash
fabric --setup
```
2. Select the "Custom Patterns" option from the Tools menu and enter your desired directory path (e.g., `~/my-custom-patterns`)
3. Fabric will automatically create the directory if it does not exist.
### Using Custom Patterns
1. Create your custom pattern directory structure:
```bash
mkdir -p ~/my-custom-patterns/my-analyzer
```
2. Create your pattern file
```bash
echo "You are an expert analyzer of ..." > ~/my-custom-patterns/my-analyzer/system.md
```
3. **Use your custom pattern:**
```bash
fabric --pattern my-analyzer "analyze this text"
```
### How It Works
- **Priority System**: Custom patterns take precedence over built-in patterns with the same name
- **Update Safe**: Your custom patterns are never affected by `fabric --updatepatterns`
- **Private by Default**: Custom patterns remain private unless you explicitly share them
Your custom patterns are completely private and won't be affected by Fabric updates!
## Helper Apps
@@ -598,7 +725,7 @@ This will create a PDF file named `output.pdf` in the current directory.
To install `to_pdf`, install it the same way as you install Fabric, just with a different repo name.
```bash
go install github.com/danielmiessler/fabric/plugins/tools/to_pdf@latest
go install github.com/danielmiessler/fabric/cmd/to_pdf@latest
```
Make sure you have a LaTeX distribution (like TeX Live or MiKTeX) installed on your system, as `to_pdf` requires `pdflatex` to be available in your system's PATH.
@@ -609,12 +736,12 @@ Make sure you have a LaTeX distribution (like TeX Live or MiKTeX) installed on y
It generates a `json` representation of a directory of code that can be fed into an AI model
with instructions to create a new feature or edit the code in a specified way.
See [the Create Coding Feature Pattern README](./patterns/create_coding_feature/README.md) for details.
See [the Create Coding Feature Pattern README](./data/patterns/create_coding_feature/README.md) for details.
Install it first using:
```bash
go install github.com/danielmiessler/fabric/plugins/tools/code_helper@latest
go install github.com/danielmiessler/fabric/cmd/code_helper@latest
```
## pbpaste
@@ -693,7 +820,7 @@ The Streamlit UI supports clipboard operations across different platforms:
- **macOS**: Uses `pbcopy` and `pbpaste` (built-in)
- **Windows**: Uses `pyperclip` library (install with `pip install pyperclip`)
- **Linux**: Uses `xclip` (install with `sudo apt-get install xclip` or equivalent for your distro)
- **Linux**: Uses `xclip` (install with `sudo apt-get install xclip` or equivalent for your Linux distribution)
## Meta
@@ -711,15 +838,15 @@ The Streamlit UI supports clipboard operations across different platforms:
This Cummulative PR adds several Web UI and functionality improvements to make pattern selection more intuitive with the addition of pattern descriptions, ability to save favorite patterns, a Pattern TAG system, powerful multilingual capabilities, PDF-to-markdown functionnalities, a help reference section, more robust Youtube processing and a variety of other ui improvements.
## 🎥 Demo Video
https://youtu.be/XMzjgqvdltM
## 🌟 Key Features
### 1. Web UI and Pattern Selection Improvements
- Pattern Descriptions
- Pattern Tags
- Pattern Favourites
- Pattern Search bar
- PDF to markdown (pdf as pattern input)
- Better handling of Youtube url
- Multilingual Support
- Web UI refinements for clearer interaction
- Help section via modal
### 2. Multilingual Support System
- Seamless language switching via UI dropdown
- Persistent language state management
- Pattern processing now use the selected language seamlessly
### 3. YouTube Integration Enhancement
- Robust language handling for YouTube transcript processing
- Chunk-based language maintenance for long transcripts
- Consistent language output throughout transcript analysis
### 4. Enhanced Tag Management Integration
The tag filtering system has been deeply integrated into the Pattern Selection interface through several UI enhancements:
1.**Dual-Position Tag Panel**
- Sliding panel positioned to the right of pattern modal
- Dynamic toggle button that adapts position and text based on panel state
- Smooth transitions for opening/closing animations
2.**Tag Selection Visibility**
- New dedicated tag display section in pattern modal
- Visual separation through subtle background styling
- Immediate feedback showing selected tags with comma separation
- Inline reset capability for quick tag clearing
3.**Improved User Experience**
- Clear visual hierarchy between pattern list and tag filtering
- Multiple ways to manage tags (panel or quick reset)
- Consistent styling with existing design language
- Space-efficient tag brick layout in 3-column grid
4.**Technical Implementation**
- Reactive tag state management
- Efficient tag filtering logic
- Proper event dispatching between components
- Maintained accessibility standards
- Responsive design considerations
5.**PDF to Markdown conversion functionality for the web interface**
- Automatic detection and processing of PDF files in chat
- Conversion to markdown format for LLM processing
- Installation instructions from the pdf-to-markdown repository
The PDF conversion module has been integrated in the svelte web browser interface. Once installed, it will automatically detect pdf files in the chat interface and convert them to markdown
## HOW TO INSTALL PDF-TO-MARKDOWN
If you need to update the web component follow the instructions in "Web Interface MOD Readme Files/WEB V2 Install Guide.md".
Assuming your web install is up to date and web svelte config complete, you can simply follow these steps to add Pdf-to-mardown.
These enhancements create a more intuitive and efficient pattern discovery experience, allowing users to quickly filter and find relevant patterns while maintaining a clean, modern interface.
## 🛠 Technical Implementation
### Language Support Architecture
```typescript
// Language state management
exportconstlanguageStore=writable<string>('');
// Chat input language detection
if(qualifier==='fr'){
languageStore.set('fr');
userInput=userInput.replace(/--fr\s*/,'');
}
// Service layer integration
constlanguage=get(languageStore)||'en';
constlanguageInstruction=language!=='en'
?`. Please use the language '${language}' for the output.`
:'';
```
### YouTube Processing Enhancement
```typescript
// Process stream with language instruction per chunk
awaitchatService.processStream(
stream,
(content: string,response?: StreamResponse)=>{
if(currentLanguage!=='en'){
content=`${content}. Please use the language '${currentLanguage}' for the output.`;
}
// Update messages...
}
);
```
# Pattern Descriptions and Tags Management
This document explains the complete workflow for managing pattern descriptions and tags, including how to process new patterns and maintain metadata.
## System Overview
The pattern system follows this hierarchy:
1.`~/.config/fabric/patterns/` directory: The source of truth for available patterns
2.`pattern_extracts.json`: Contains first 500 words of each pattern for reference
3.`pattern_descriptions.json`: Stores pattern metadata (descriptions and tags)
4.`web/static/data/pattern_descriptions.json`: Web-accessible copy for the interface
## Pattern Processing Workflow
### 1. Adding New Patterns
- Add patterns to `~/.config/fabric/patterns/`
- Run extract_patterns.py to process new additions:
```bash
python extract_patterns.py
The Python Script automatically:
- Creates pattern extracts for reference
- Adds placeholder entries in descriptions file
- Syncs to web interface
### 2. Pattern Extract Creation
The script extracts first 500 words from each pattern's system.md file to:
- Provide context for writing descriptions
- Maintain reference material
- Aid in pattern categorization
### 3. Description and Tag Management
Pattern descriptions and tags are managed in pattern_descriptions.json:
{
"patterns": [
{
"patternName": "pattern_name",
"description": "[Description pending]",
"tags": []
}
]
}
## Completing Pattern Metadata
### Writing Descriptions
1. Check pattern_descriptions.json for "[Description pending]" entries
2. Reference pattern_extracts.json for context
3. How to update Pattern short descriptions (one sentence).
You can update your descriptions in pattern_descriptions.json manually or using LLM assistance (prefered approach).
Tell AI to look for "Description pending" entries in this file and write a short description based on the extract info in the pattern_extracts.json file. You can also ask your LLM to add tags for those newly added patterns, using other patterns tag assignments as example.
### Managing Tags
1. Add appropriate tags to new patterns
2. Update existing tags as needed
3. Tags are stored as arrays: ["TAG1", "TAG2"]
4. Edit pattern_descriptions.json directly to modify tags
5. Make tags your own. You can delete, replace, amend existing tags.
@@ -22,19 +22,20 @@ Take a deep breath and think step by step about how to best accomplish this goal
This must be under the heading "INSIGHTFULNESS SCORE (0 = not very interesting and insightful to 10 = very interesting and insightful)".
- A rating of how emotional the debate was from 0 (very calm) to 5 (very emotional). This must be under the heading "EMOTIONALITY SCORE (0 (very calm) to 5 (very emotional))".
- A list of the participants of the debate and a score of their emotionality from 0 (very calm) to 5 (very emotional). This must be under the heading "PARTICIPANTS".
- A list of arguments attributed to participants with names and quotes. If possible, this should include external references that disprove or back up their claims.
- A list of arguments attributed to participants with names and quotes. Each argument summary must be EXACTLY 16 words. If possible, this should include external references that disprove or back up their claims.
It is IMPORTANT that these references are from trusted and verifiable sources that can be easily accessed. These sources have to BE REAL and NOT MADE UP. This must be under the heading "ARGUMENTS".
If possible, provide an objective assessment of the truth of these arguments. If you assess the truth of the argument, provide some sources that back up your assessment. The material you provide should be from reliable, verifiable, and trustworthy sources. DO NOT MAKE UP SOURCES.
- A list of agreements the participants have reached, attributed with names and quotes. This must be under the heading "AGREEMENTS".
- A list of disagreements the participants were unable to resolve and the reasons why they remained unresolved, attributed with names and quotes. This must be under the heading "DISAGREEMENTS".
- A list of possible misunderstandings and why they may have occurred, attributed with names and quotes. This must be under the heading "POSSIBLE MISUNDERSTANDINGS".
- A list of learnings from the debate. This must be under the heading "LEARNINGS".
- A list of takeaways that highlight ideas to think about, sources to explore, and actionable items. This must be under the heading "TAKEAWAYS".
- A list of agreements the participants have reached. Each agreement summary must be EXACTLY 16 words, followed by names and quotes. This must be under the heading "AGREEMENTS".
- A list of disagreements the participants were unable to resolve. Each disagreement summary must be EXACTLY 16 words, followed by names and quotes explaining why they remained unresolved. This must be under the heading "DISAGREEMENTS".
- A list of possible misunderstandings. Each misunderstanding summary must be EXACTLY 16 words, followed by names and quotes explaining why they may have occurred. This must be under the heading "POSSIBLE MISUNDERSTANDINGS".
- A list of learnings from the debate. Each learning must be EXACTLY 16 words. This must be under the heading "LEARNINGS".
- A list of takeaways that highlight ideas to think about, sources to explore, and actionable items. Each takeaway must be EXACTLY 16 words. This must be under the heading "TAKEAWAYS".
# OUTPUT INSTRUCTIONS
- Output all sections above.
- Use Markdown to structure your output.
- Do not use any markdown formatting (no asterisks, no bullet points, no headers).
- Keep all agreements, arguments, recommendations, learnings, and takeaways to EXACTLY 16 words each.
- When providing quotes, these quotes should clearly express the points you are using them for. If necessary, use multiple quotes.
@@ -8,19 +8,19 @@ Take a deep breath and think step by step about how to best accomplish this goal
- Consume the entire paper and think deeply about it.
- Map out all the claims and implications on a virtual whiteboard in your mind.
- Map out all the claims and implications on a giant virtual whiteboard in your mind.
# OUTPUT
- Extract a summary of the paper and its conclusions into a 25-word sentence called SUMMARY.
- Extract a summary of the paper and its conclusions into a 16-word sentence called SUMMARY.
- Extract the list of authors in a section called AUTHORS.
- Extract the list of organizations the authors are associated, e.g., which university they're at, with in a section called AUTHOR ORGANIZATIONS.
- Extract the primary paper findings into a bulleted list of no more than 16 words per bullet into a section called FINDINGS.
- Extract the most surprising and interesting paper findings into a 10 bullets of no more than 16 words per bullet into a section called FINDINGS.
- Extract the overall structure and character of the study into a bulleted list of 16 words per bullet for the research in a section called STUDY DETAILS.
- Extract the overall structure and character of the study into a bulleted list of 16 words per bullet for the research in a section called STUDY OVERVIEW.
- Extract the study quality by evaluating the following items in a section called STUDY QUALITY that has the following bulleted sub-sections:
@@ -76,7 +76,9 @@ END EXAMPLE CHART
- SUMMARY STATEMENT:
A final 25-word summary of the paper, its findings, and what we should do about it if it's true.
A final 16-word summary of the paper, its findings, and what we should do about it if it's true.
Also add 5 8-word bullets of how you got to that rating and conclusion / summary.
# RATING NOTES
@@ -84,21 +86,23 @@ A final 25-word summary of the paper, its findings, and what we should do about
- An A would be a paper that is novel, rigorous, empirical, and has no conflicts of interest.
- A paper could get an A if it's theoretical but everything else would have to be perfect.
- A paper could get an A if it's theoretical but everything else would have to be VERY good.
- The stronger the claims the stronger the evidence needs to be, as well as the transparency into the methodology. If the paper makes strong claims, but the evidence or transparency is weak, then the RIGOR score should be lowered.
- Remove at least 1 grade (and up to 2) for papers where compelling data is provided but it's not clear what exact tests were run and/or how to reproduce those tests.
- Do not relax this transparency requirement for papers that claim security reasons.
- If a paper does not clearly articulate its methodology in a way that's replicable, lower the RIGOR and overall score significantly.
- Do not relax this transparency requirement for papers that claim security reasons. If they didn't show their work we have to assume the worst given the reproducibility crisis..
- Remove up to 1-3 grades for potential conflicts of interest indicated in the report.
# ANALYSIS INSTRUCTIONS
- Tend towards being more critical. Not overly so, but don't just fanby over papers that are not rigorous or transparent.
# OUTPUT INSTRUCTIONS
- Output all sections above.
- After deeply considering all the sections above and how they interact with each other, output all sections above.
- Ensure the scoring looks closely at the reproducibility and transparency of the methodology, and that it doesn't give a pass to papers that don't provide the data or methodology for safety or other reasons.
@@ -108,7 +112,7 @@ Known [-2--------] Novel
Weak [-------8--] Rigorous
Theoretical [--3-------] Empirical
- For the findings and other analysis sections, write at the 9th-grade reading level. This means using short sentences and simple words/concepts to explain everything.
- For the findings and other analysis sections, and in fact all writing, write in the clear, approachable style of Paul Graham.
- Ensure there's a blank line between each bullet of output.
You are a research paper analysis service focused on determining the primary findings of the paper and analyzing its scientific rigor and quality.
Take a deep breath and think step by step about how to best accomplish this goal using the following steps.
# STEPS
- Consume the entire paper and think deeply about it.
- Map out all the claims and implications on a virtual whiteboard in your mind.
# FACTORS TO CONSIDER
- Extract a summary of the paper and its conclusions into a 25-word sentence called SUMMARY.
- Extract the list of authors in a section called AUTHORS.
- Extract the list of organizations the authors are associated, e.g., which university they're at, with in a section called AUTHOR ORGANIZATIONS.
- Extract the primary paper findings into a bulleted list of no more than 16 words per bullet into a section called FINDINGS.
- Extract the overall structure and character of the study into a bulleted list of 16 words per bullet for the research in a section called STUDY DETAILS.
- Extract the study quality by evaluating the following items in a section called STUDY QUALITY that has the following bulleted sub-sections:
- STUDY DESIGN: (give a 15 word description, including the pertinent data and statistics.)
- SAMPLE SIZE: (give a 15 word description, including the pertinent data and statistics.)
- CONFIDENCE INTERVALS (give a 15 word description, including the pertinent data and statistics.)
- P-VALUE (give a 15 word description, including the pertinent data and statistics.)
- EFFECT SIZE (give a 15 word description, including the pertinent data and statistics.)
- CONSISTENCE OF RESULTS (give a 15 word description, including the pertinent data and statistics.)
- METHODOLOGY TRANSPARENCY (give a 15 word description of the methodology quality and documentation.)
- STUDY REPRODUCIBILITY (give a 15 word description, including how to fully reproduce the study.)
- Data Analysis Method (give a 15 word description, including the pertinent data and statistics.)
- Discuss any Conflicts of Interest in a section called CONFLICTS OF INTEREST. Rate the conflicts of interest as NONE DETECTED, LOW, MEDIUM, HIGH, or CRITICAL.
- Extract the researcher's analysis and interpretation in a section called RESEARCHER'S INTERPRETATION, in a 15-word sentence.
- In a section called PAPER QUALITY output the following sections:
- Novelty: 1 - 10 Rating, followed by a 15 word explanation for the rating.
- Rigor: 1 - 10 Rating, followed by a 15 word explanation for the rating.
- Empiricism: 1 - 10 Rating, followed by a 15 word explanation for the rating.
- Rating Chart: Create a chart like the one below that shows how the paper rates on all these dimensions.
- Known to Novel is how new and interesting and surprising the paper is on a scale of 1 - 10.
- Weak to Rigorous is how well the paper is supported by careful science, transparency, and methodology on a scale of 1 - 10.
- Theoretical to Empirical is how much the paper is based on purely speculative or theoretical ideas or actual data on a scale of 1 - 10. Note: Theoretical papers can still be rigorous and novel and should not be penalized overall for being Theoretical alone.
EXAMPLE CHART for 7, 5, 9 SCORES (fill in the actual scores):
Known [------7---] Novel
Weak [----5-----] Rigorous
Theoretical [--------9-] Empirical
END EXAMPLE CHART
- FINAL SCORE:
- A - F based on the scores above, conflicts of interest, and the overall quality of the paper. On a separate line, give a 15-word explanation for the grade.
- SUMMARY STATEMENT:
A final 25-word summary of the paper, its findings, and what we should do about it if it's true.
# RATING NOTES
- If the paper makes claims and presents stats but doesn't show how it arrived at these stats, then the Methodology Transparency would be low, and the RIGOR score should be lowered as well.
- An A would be a paper that is novel, rigorous, empirical, and has no conflicts of interest.
- A paper could get an A if it's theoretical but everything else would have to be perfect.
- The stronger the claims the stronger the evidence needs to be, as well as the transparency into the methodology. If the paper makes strong claims, but the evidence or transparency is weak, then the RIGOR score should be lowered.
- Remove at least 1 grade (and up to 2) for papers where compelling data is provided but it's not clear what exact tests were run and/or how to reproduce those tests.
- Do not relax this transparency requirement for papers that claim security reasons.
- If a paper does not clearly articulate its methodology in a way that's replicable, lower the RIGOR and overall score significantly.
- Remove up to 1-3 grades for potential conflicts of interest indicated in the report.
- Ensure the scoring looks closely at the reproducibility and transparency of the methodology, and that it doesn't give a pass to papers that don't provide the data or methodology for safety or other reasons.
# OUTPUT INSTRUCTIONS
Output only the following—not all the sections above.
Use Markdown bullets with dashes for the output (no bold or italics (asterisks)).
- The Title of the Paper, starting with the word TITLE:
- A 16-word sentence summarizing the paper's main claim, in the style of Paul Graham, starting with the word SUMMARY: which is not part of the 16 words.
- A 32-word summary of the implications stated or implied by the paper, in the style of Paul Graham, starting with the word IMPLICATIONS: which is not part of the 32 words.
- A 32-word summary of the primary recommendation stated or implied by the paper, in the style of Paul Graham, starting with the word RECOMMENDATION: which is not part of the 32 words.
- A 32-word bullet covering the authors of the paper and where they're out of, in the style of Paul Graham, starting with the word AUTHORS: which is not part of the 32 words.
- A 32-word bullet covering the methodology, including the type of research, how many studies it looked at, how many experiments, the p-value, etc. In other words the various aspects of the research that tell us the amount and type of rigor that went into the paper, in the style of Paul Graham, starting with the word METHODOLOGY: which is not part of the 32 words.
- A 32-word bullet covering any potential conflicts or bias that can logically be inferred by the authors, their affiliations, the methodology, or any other related information in the paper, in the style of Paul Graham, starting with the word CONFLICT/BIAS: which is not part of the 32 words.
- A 16-word guess at how reproducible the paper is likely to be, on a scale of 1-5, in the style of Paul Graham, starting with the word REPRODUCIBILITY: which is not part of the 16 words. Output the score as n/5, not spelled out. Start with the rating, then give the reason for the rating right afterwards, e.g.: "2/5 — The paper ...".
- In the markdown, don't use formatting like bold or italics. Make the output maximally readable in plain text.
- Do not output warnings or notes—just output the requested sections.
You are an expert Terraform plan analyser. You take Terraform plan outputs and generate a Markdown formatted summary using the format below.
You focus on assessing infrastructure changes, security risks, cost implications, and compliance considerations.
## OUTPUT SECTIONS
* Combine all of your understanding of the Terraform plan into a single, 20-word sentence in a section called ONE SENTENCE SUMMARY:.
* Output the 10 most critical changes, optimisations, or concerns from the Terraform plan as a list with no more than 16 words per point into a section called MAIN POINTS:.
* Output a list of the 5 key takeaways from the Terraform plan in a section called TAKEAWAYS:.
## OUTPUT INSTRUCTIONS
* Create the output using the formatting above.
* You only output human-readable Markdown.
* Output numbered lists, not bullets.
* Do not output warnings or notes—just the requested sections.
You are a superintelligent expert on content of all forms, with deep understanding of which topics, categories, themes, and tags apply to any piece of content.
# GOAL
Your goal is to output a JSON object called tags, with the following tags applied if the content is significantly about their topic.
- **future** - Posts about the future, predictions, emerging trends
- **politics** - Political topics, elections, governance, policy
- **tutorial** - Technical or non-technical guides, how-tos
# STEPS
1. Deeply understand the content and its themes and categories and topics.
2. Evaluate the list of tags above.
3. Determine which tags apply to the content.
4. Output the "tags" JSON object.
# NOTES
- It's ok, and quite normal, for multiple tags to apply—which is why this is tags and not categories
- All AI posts should have the technology tag, and that's ok. But not all technology posts are about AI, and therefore the AI tag needs to be evaluated separately. That goes for all potentially nested or conflicted tags.
- Be a bit conservative in applying tags. If a piece of content is only tangentially related to a tag, don't include it.
# OUTPUT INSTRUCTIONS
- Output ONLY the JSON object, and nothing else.
- That means DO NOT OUTPUT the ```json format indicator. ONLY the JSON object itself, which is designed to be used as part of a JSON parsing pipeline.
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