{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm-context","slug":"llm-context","name":"llm-context","type":"mcp","url":"https://github.com/cyberchitta/llm-context.py","page_url":"https://unfragile.ai/llm-context","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llm-context__cap_0","uri":"capability://data.processing.analysis.rule.based.file.selection.with.gitignore.pattern.matching","name":"rule-based file selection with gitignore pattern matching","description":"Intelligently selects files from repositories by applying .gitignore patterns and custom inclusion/exclusion rules defined in YAML frontmatter. The system reads rule files from .llm-context/rules/ directory, parses gitignore-style patterns, and maintains persistent selection state across sessions. Files are categorized as either full-content or outline candidates based on rule configuration, enabling selective context injection without manual file enumeration.","intents":["I want to automatically exclude build artifacts, node_modules, and other irrelevant files without manually listing them","I need different file selection strategies for different LLM tasks (e.g., documentation review vs code refactoring)","I want to reuse file selections across multiple context generation runs without re-specifying patterns"],"best_for":["developers managing large codebases with complex directory structures","teams using LLMs for code review or documentation tasks requiring selective context","projects with existing .gitignore files that can be leveraged for LLM context"],"limitations":["Rule system requires YAML frontmatter knowledge; no GUI for rule creation","File selection state persists locally; no built-in synchronization across team members","Pattern matching limited to gitignore syntax; no regex-based custom patterns"],"requires":["Python 3.9+",".llm-context/rules/ directory initialized in project root","Valid YAML frontmatter in rule files"],"input_types":["gitignore patterns (text)","YAML rule configuration (structured text)","file system paths (text)"],"output_types":["filtered file list (structured data)","selection state (JSON/YAML)"],"categories":["data-processing-analysis","file-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_1","uri":"capability://code.generation.editing.code.structure.outlining.and.definition.extraction","name":"code structure outlining and definition extraction","description":"Parses source code files to extract structural information (function/class definitions, imports, comments) and generates condensed outlines instead of full file content. Supports 40+ languages through language-specific parsers, enabling LLMs to understand codebase architecture without token-heavy full file dumps. Definitions are extracted as key-value pairs mapping symbol names to their locations, allowing LLMs to navigate code semantically.","intents":["I want to give an LLM a high-level overview of a file's structure without sending the entire file content","I need the LLM to understand which functions and classes exist in a module so it can ask for specific definitions","I want to reduce token usage by sending outlines instead of full source code for large files"],"best_for":["developers working with large codebases where full file context exceeds token budgets","teams using LLMs for code navigation and refactoring tasks","polyglot projects requiring multi-language code understanding"],"limitations":["Outline extraction accuracy depends on language-specific parser quality; complex nested structures may be misrepresented","Definition extraction does not resolve cross-file dependencies or type information","Supported languages limited to those with implemented parsers; custom language support requires parser implementation"],"requires":["Python 3.9+","Language-specific parser libraries (tree-sitter or equivalent)","Source files in supported language"],"input_types":["source code files (text)","language identifier (text)"],"output_types":["code outline (structured text)","definition map (JSON/YAML)","highlighted code snippets (text with markers)"],"categories":["code-generation-editing","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_2","uri":"capability://text.generation.language.jinja2.based.template.system.for.context.formatting","name":"jinja2-based template system for context formatting","description":"Formats selected files and extracted code structures into LLM-ready context using Jinja2 templates. The system provides default templates for common scenarios (documentation review, code refactoring) and allows custom templates to be defined in .llm-context/templates/. Templates receive context variables including file lists, outlines, definitions, and project metadata, enabling flexible output formatting for different LLM chat interfaces and prompt engineering strategies.","intents":["I want to format code context in a specific way that matches my LLM's preferred input structure","I need to include project metadata, instructions, and code in a single formatted output","I want to reuse context formatting across different projects without rewriting templates"],"best_for":["developers with specific prompt engineering requirements","teams standardizing on context format across multiple projects","users integrating with custom LLM chat interfaces or APIs"],"limitations":["Template customization requires Jinja2 knowledge; no visual template builder","Template variables are fixed by the system; custom variables require code modification","No built-in template validation; malformed templates produce silent failures or incomplete output"],"requires":["Python 3.9+","Jinja2 library (included in dependencies)","Understanding of Jinja2 syntax for custom templates"],"input_types":["file content (text)","code outlines (structured data)","project metadata (JSON/YAML)","Jinja2 template (text)"],"output_types":["formatted context (text)","markdown-formatted output (text)","structured prompt (text)"],"categories":["text-generation-language","template-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration","name":"model context protocol (mcp) server integration","description":"Exposes llm-context functionality as an MCP server, allowing Claude and other MCP-compatible LLMs to request context generation on-demand through standardized protocol calls. The MCP server implements tools for file selection, context generation, and template rendering, enabling LLMs to interactively refine context without returning to the CLI. This creates a bidirectional integration where LLMs can request specific context based on their analysis needs.","intents":["I want Claude or another MCP-compatible LLM to request code context directly without me manually running CLI commands","I need the LLM to iteratively refine context selection based on its analysis (e.g., request outlines first, then full files)","I want to keep the LLM in a single conversation while it accesses different parts of my codebase"],"best_for":["developers using Claude or other MCP-compatible LLMs for code analysis","teams wanting seamless LLM-to-codebase integration without context switching","projects requiring dynamic context refinement during LLM conversations"],"limitations":["MCP server requires separate process management; no built-in process lifecycle handling","LLM-side MCP support is limited to Claude and select other models; not universally available","Network latency between MCP server and LLM client adds overhead vs local clipboard integration","No built-in authentication or access control; assumes trusted local network"],"requires":["Python 3.9+","MCP-compatible LLM client (Claude, etc.)","MCP server running and accessible to LLM client","Network connectivity between client and server"],"input_types":["MCP tool calls (JSON)","context specification parameters (text/JSON)","file selection rules (YAML)"],"output_types":["formatted context (text)","file lists (JSON)","context metadata (JSON)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_4","uri":"capability://automation.workflow.clipboard.based.context.export.with.format.options","name":"clipboard-based context export with format options","description":"Generates formatted context and copies it directly to the system clipboard, enabling one-click context injection into any LLM chat interface. Supports multiple output formats (markdown, plain text, structured JSON) and integrates with the template system to produce chat-ready context. The clipboard integration bypasses the need for file uploads or API integrations, making it compatible with any LLM interface that accepts pasted text.","intents":["I want to quickly copy my project context to paste into Claude, ChatGPT, or any other LLM chat interface","I need to generate context in a specific format (markdown with code blocks, plain text, etc.) for different LLM interfaces","I want a one-command workflow: select files, format context, copy to clipboard, paste into LLM"],"best_for":["developers using web-based LLM interfaces (ChatGPT, Claude web, etc.)","users without API access or preference for interactive chat over programmatic integration","quick prototyping and ad-hoc LLM interactions"],"limitations":["Clipboard size limits vary by OS; very large contexts may be truncated or fail silently","No feedback on whether context was successfully copied; user must verify manually","Clipboard is shared system resource; concurrent clipboard operations may cause data loss","No built-in context versioning; overwriting clipboard loses previous context"],"requires":["Python 3.9+","System clipboard access (xclip on Linux, pbcopy on macOS, clip on Windows)","LLM chat interface with text input capability"],"input_types":["formatted context (text)","template output (text)"],"output_types":["clipboard content (text)","formatted context ready for pasting (text)"],"categories":["automation-workflow","clipboard-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_5","uri":"capability://memory.knowledge.project.notes.and.user.notes.management","name":"project notes and user notes management","description":"Stores project-level and user-level notes in .llm-context/project-notes.md and .llm-context/user-notes.md respectively, which are automatically included in generated context. These notes provide persistent metadata about the project (architecture decisions, conventions, known issues) and user preferences (preferred coding style, analysis focus areas) that inform LLM understanding without requiring manual re-entry per session. Notes are treated as first-class context components alongside code files.","intents":["I want to document architectural decisions and project conventions that the LLM should know about","I need to provide persistent instructions to the LLM about my coding style and preferences","I want to include project-specific context (known issues, dependencies, constraints) in every LLM interaction"],"best_for":["teams maintaining consistent project context across multiple LLM sessions","projects with complex architecture or non-obvious conventions","developers wanting to establish persistent LLM behavior without per-session prompting"],"limitations":["Notes are static files; no version control or change tracking built-in","No automatic synchronization across team members; requires manual file sharing","Notes are included in every context generation; no per-task filtering","Large notes files increase token usage for every LLM interaction"],"requires":["Python 3.9+",".llm-context/project-notes.md and/or .llm-context/user-notes.md files","Markdown formatting knowledge"],"input_types":["markdown notes (text)","project metadata (text)"],"output_types":["notes content (text)","notes included in formatted context (text)"],"categories":["memory-knowledge","documentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_6","uri":"capability://automation.workflow.execution.environment.with.context.state.persistence","name":"execution environment with context state persistence","description":"Manages the execution context through a ContextSpec object that tracks project configuration, rule selections, and file state across CLI invocations. The system persists state in .llm-context/state.json or equivalent, enabling users to save context configurations and resume them without re-specifying rules or file selections. The execution environment coordinates between file selection, context generation, and output integration, providing a unified interface for context management.","intents":["I want to save a context configuration and reuse it across multiple LLM sessions","I need to track which files were selected for a particular analysis task","I want to modify a previous context selection without starting from scratch"],"best_for":["developers with recurring context needs (e.g., weekly code reviews)","teams standardizing on context configurations per project","projects requiring audit trails of what context was provided to LLMs"],"limitations":["State persistence is local-only; no cloud synchronization or team sharing","State files are JSON/YAML; no built-in migration for schema changes","No conflict resolution if state is modified by multiple processes simultaneously","State includes file paths; may not be portable across different machines or OS"],"requires":["Python 3.9+","Write access to .llm-context/ directory","JSON or YAML parsing capability"],"input_types":["context specification (YAML/JSON)","rule configuration (YAML)","file selection parameters (text)"],"output_types":["context state (JSON/YAML)","persisted configuration (JSON/YAML)"],"categories":["automation-workflow","state-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_7","uri":"capability://code.generation.editing.multi.language.code.parsing.and.highlighting","name":"multi-language code parsing and highlighting","description":"Parses and highlights source code in 40+ languages using language-specific syntax rules, enabling LLMs to understand code structure and semantics beyond plain text. The system applies syntax highlighting markers (markdown code blocks with language identifiers, or inline markers) to code snippets, improving LLM comprehension of language-specific constructs. Language detection is automatic based on file extension, with fallback to user specification.","intents":["I want the LLM to understand code syntax and structure, not just see raw text","I need to highlight specific code sections to draw the LLM's attention to important logic","I want to ensure code is formatted correctly for the LLM's language understanding (e.g., Python indentation, JavaScript async/await)"],"best_for":["polyglot projects with multiple programming languages","developers needing LLM code analysis across language boundaries","teams using LLMs for code review and refactoring"],"limitations":["Language detection relies on file extensions; ambiguous extensions may be misidentified","Syntax highlighting is cosmetic; does not improve LLM semantic understanding","Custom language support requires parser implementation; limited to pre-implemented languages","Highlighting markers add overhead to context size; may increase token usage"],"requires":["Python 3.9+","Language-specific parser libraries","Source files with standard file extensions"],"input_types":["source code files (text)","language identifier (text)","code snippets (text)"],"output_types":["highlighted code (markdown or text with markers)","syntax-aware code blocks (text)"],"categories":["code-generation-editing","syntax-highlighting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_8","uri":"capability://automation.workflow.custom.rule.composition.with.base.rule.inheritance","name":"custom rule composition with base rule inheritance","description":"Enables users to define custom rules that extend system rules (prefixed with 'lc-') through a 'base' property in YAML frontmatter. Rules are stored as markdown files in .llm-context/rules/ with YAML frontmatter defining file selection patterns and LLM instructions in the markdown body. This composition pattern allows rule reuse and specialization without duplication, enabling teams to build rule libraries for common scenarios (documentation review, security audit, performance analysis).","intents":["I want to create a custom rule for a specific analysis task without rewriting file selection patterns","I need to extend a system rule with project-specific instructions for the LLM","I want to build a library of reusable rules for different code review scenarios"],"best_for":["teams with standardized code review processes","projects requiring multiple context configurations for different tasks","organizations building rule libraries for LLM-assisted development"],"limitations":["Rule composition is single-level; no deep inheritance chains","No rule validation; malformed rules fail silently during context generation","Rule conflicts (overlapping patterns) are not detected; last-applied rule wins","No built-in rule documentation or discovery; users must manually track available rules"],"requires":["Python 3.9+","YAML frontmatter knowledge","Markdown file editing capability",".llm-context/rules/ directory"],"input_types":["rule definition (markdown with YAML frontmatter)","base rule name (text)","file selection patterns (gitignore syntax)"],"output_types":["composed rule (internal representation)","file selection patterns (gitignore syntax)","LLM instructions (markdown)"],"categories":["automation-workflow","rule-system"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-context__cap_9","uri":"capability://automation.workflow.cli.command.interface.with.project.setup.and.context.generation.workflows","name":"cli command interface with project setup and context generation workflows","description":"Provides a comprehensive command-line interface organized into logical command groups: project setup (initialize .llm-context directory structure), file selection (apply rules and filter files), context generation (format and prepare output), and clipboard integration (copy to clipboard). Commands are composable, allowing users to chain operations (e.g., select files with rule X, generate context with template Y, copy to clipboard). The CLI supports both interactive and scripted workflows.","intents":["I want to initialize llm-context in my project with a single command","I need to select files using a specific rule and generate context in one workflow","I want to automate context generation as part of my development workflow (e.g., pre-commit hook)"],"best_for":["developers comfortable with command-line tools","teams automating context generation in CI/CD pipelines","projects integrating llm-context into development workflows"],"limitations":["CLI requires Python installation and environment setup; not portable to non-developer machines","No interactive CLI mode; all parameters must be specified as flags or config files","Error messages may be cryptic for users unfamiliar with Python stack traces","Command composition requires shell scripting knowledge for complex workflows"],"requires":["Python 3.9+","pip or uv package manager","Shell environment (bash, zsh, PowerShell, etc.)","Basic CLI knowledge"],"input_types":["command-line arguments (text)","configuration files (YAML)","rule specifications (text)"],"output_types":["formatted context (text)","clipboard content (text)","status messages (text)"],"categories":["automation-workflow","cli-interface"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+",".llm-context/rules/ directory initialized in project root","Valid YAML frontmatter in rule files","Language-specific parser libraries (tree-sitter or equivalent)","Source files in supported language","Jinja2 library (included in dependencies)","Understanding of Jinja2 syntax for custom templates","MCP-compatible LLM client (Claude, etc.)","MCP server running and accessible to LLM client","Network connectivity between client and server"],"failure_modes":["Rule system requires YAML frontmatter knowledge; no GUI for rule creation","File selection state persists locally; no built-in synchronization across team members","Pattern matching limited to gitignore syntax; no regex-based custom patterns","Outline extraction accuracy depends on language-specific parser quality; complex nested structures may be misrepresented","Definition extraction does not resolve cross-file dependencies or type information","Supported languages limited to those with implemented parsers; custom language support requires parser implementation","Template customization requires Jinja2 knowledge; no visual template builder","Template variables are fixed by the system; custom variables require code modification","No built-in template validation; malformed templates produce silent failures or incomplete output","MCP server requires separate process management; no built-in process lifecycle handling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-context","compare_url":"https://unfragile.ai/compare?artifact=llm-context"}},"signature":"fg1y2pCXYya+q7Xc+4xmLoPFUSm9PJaVtUjWkIJrDFEtnWP3hyp6vrRyv07m92KfRjmw71G4bYn376rlZ4YlCA==","signedAt":"2026-06-20T12:09:24.674Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-context","artifact":"https://unfragile.ai/llm-context","verify":"https://unfragile.ai/api/v1/verify?slug=llm-context","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}