llm-context

Smart context management for LLM development workflows.Share relevant project files instantly through intelligent selection and rule-based filtering.

Getting the right context into LLM conversations is friction-heavy:

- Manually finding and copying relevant files wastes time
- Too much context hits token limits, too little misses important details
- AI requests for additional files require manual fetching
- Hard to track what changed during development sessions

llm-context provides focused, task-specific project context through composable rules.

lc-select # Smart file selection lc-context # Copy formatted context to clipboard # Paste and work - AI can access additional files via MCP

lc-preview tmp-prm-auth # Validate rule selects right files lc-context tmp-prm-auth # Get focused context for sub-agent

- lc_outlines- Generate excerpted context from current rule
- lc_preview- Validate rule effectiveness before use
- lc_missing- Fetch specific files/implementations on demand

Note: This project was developed in collaboration with several Claude Sonnets (3.5, 3.6, 3.7, 4.0) and Groks (3, 4), using LLM Context itself to share code during development. All code is heavily human-curated by @restlessronin.

uv tool install "llm-context>=0.6.0"

If you're an agent setting llm-context up to help curate task contexts, run this once per project:

uv tool install "llm-context>=0.6.0" # installs the lc- commands globally cd <project-root> lc-init # creates .llm-context/, copies the lc-curate-context skill to .claude/skills/

# One-time setup cd your-project lc-init # Daily usage lc-select lc-context # Paste into your LLM chat

{ "mcpServers": { "llm-context": { "command": "uvx", "args": ["--from", "llm-context", "lc-mcp"] } } }

# Agent explores codebase lc-outlines # Agent creates focused rule for specific task # (via Skill or lc-rule-instructions) # Agent validates rule lc-preview tmp-prm-oauth-task # Agent uses context for sub-task lc-context tmp-prm-oauth-task

# Explore codebase structure lc_outlines(root_path, rule_name) # Validate rule effectiveness lc_preview(root_path, rule_name) # Fetch specific files/implementations lc_missing(root_path, param_type, data, timestamp)

--- description: "Debug API authentication" compose: filters: [lc/flt-no-files] excerpters: [lc/exc-base] also-include: full-files: ["/src/auth/", "/tests/auth/"] --- Focus on authentication system and related tests.

--- instructions: [lc/ins-developer, lc/sty-python] compose: filters: [lc/flt-base, project-filters] excerpters: [lc/exc-base] ---

lc-init # Installs skill to ~/.claude/skills/ # Restart Claude Desktop or Claude Code

# 1. Share project context lc-context # Any rule - overview included # 2. Paste into Claude, then ask: # "Create a rule for refactoring authentication to JWT" # "I need a rule to debug the payment processing"

# 1. Load framework lc-set-rule lc/prm-rule-create lc-select lc-context -nt # 2. Paste into any LLM # "I need a rule for adding OAuth integration" # 3. LLM generates focused rule using framework # 4. Use the new rule lc-set-rule tmp-prm-oauth lc-select lc-context

cat > .llm-context/rules/flt-repo-base.md << 'EOF' --- description: "Repository-specific exclusions" compose: filters: [lc/flt-base] gitignores: full-files: [".md", "/tests", "/node_modules"] excerpted-files: ["*.md", "/tests"] --- EOF

cat > .llm-context/rules/prm-code.md << 'EOF' --- description: "Main development rule" instructions: [lc/ins-developer, lc/sty-python] compose: filters: [flt-repo-base] excerpters: [lc/exc-base] --- Additional project-specific guidelines and context. EOF lc-set-rule prm-code

Choose format based on your LLM environment:

- Intelligent Selection: Rules automatically include/exclude appropriate files
- Context Validation: Preview size and selection before generation
- Code Excerpting: Extract structure while reducing tokens (15+ languages)
- MCP Integration: AI accesses additional files without manual intervention
- Composable Rules: Build complex contexts from reusable patterns
- AI-Assisted Creation: Interactive skill or documentation-based approaches
- Agent-Friendly: CLI and MCP interfaces for autonomous operation

lc-set-rule prm-code lc-select lc-context # Paste into chat - AI accesses more files via MCP if needed

# Share project context first lc-context # Then create focused rule: # Via Skill: "Create a rule for [task]" # Via Instructions: lc-set-rule lc/prm-rule-create && lc-context -nt # Validate and use lc-preview tmp-prm-task lc-context tmp-prm-task

# Agent validates rule effectiveness lc-preview tmp-prm-refactor-auth # Agent generates context for sub-agent lc-context tmp-prm-refactor-auth > /tmp/context.md # Sub-agent reads context and executes task

# Agent validates rule preview = lc_preview(root_path="/path/to/project", rule_name="tmp-prm-task") # Agent generates context context = lc_outlines(root_path="/path/to/project") # Agent fetches additional files as needed files = lc_missing(root_path, "f", "['/proj/src/auth.py']", timestamp)

All paths use project-relative format with project name prefix:

/{project-name}/src/module/file.py /{project-name}/tests/test_module.py

This enables multi-project context composition without path conflicts.

In rules, patterns are project-relative without the prefix:

also-include: full-files: - "/src/auth/" # ✓ Correct - "/myproject/src/" # ✗ Wrong - don't include project name

- User Guide- Complete documentation with examples
- Design Philosophy- Why llm-context exists
- Real-world Examples- Using full context effectively

Apache License, Version 2.0. SeeLICENSEfor details.

Analyze large codebases and document collections using high-context models via OpenRouter, OpenAI, or Google AI -- very useful, e.g., with Claude Code