llms-keeper

llms-keeper — Project Context for AI Agents

Generate and maintain llms.txt and llms-full.txt files following the llmstxt.org standard to give AI agents quick project understanding.

Core Philosophy

"Find the smallest set of high-signal tokens that maximize the likelihood of desired outcome."

Every sentence must answer: "Will an AI agent produce worse results without this information?" If no — delete without hesitation.

Two Files, Two Purposes

File	Purpose	Audience	Length
llms.txt	Navigation index	AI needing quick orientation	50-100 lines
llms-full.txt	Complete context	AI needing full understanding	200-600 lines

Signal vs Noise

Always Include (High-Signal)

• Project purpose and tech stack
• Architecture: module hierarchy, dependency direction
• Data flow patterns (ASCII diagrams for complex flows)
• Key patterns used across 3+ files
• Configuration approach and entry points
• Common pitfalls with solutions
• Essential development commands
• Testing approach and conventions

Never Include (Noise)

• Step-by-step tutorials or implementation guides
• Temporary workarounds, TODOs, FIXMEs
• Patterns used in only 1 file
• Verbose explanations ("This module is responsible for...")
• Obvious information for experienced developers
• Credentials, API keys, secrets

Workflow

Delegate analysis and generation to the documentation-keeper agent.

The agent handles the full process: framework auto-detection, codebase scanning, pattern extraction, file generation, and quality verification.

For manual or informational use, the workflow is:

1. Scan project structure and detect tech stack from manifest files
2. Read key sources: entry points, core modules, configs, tests, docs
3. Extract high-signal information (architecture, patterns, pitfalls)
4. Generate llms-full.txt first (self-contained content)
5. Derive llms.txt from it (navigation index linking to sections)
6. Verify quality checklist from references/llmstxt-spec.md