AGENTS.md Schema for LLM-Compiled Knowledge Bases with Learning Layer
A developer has published AGENTS.md v1.0, a schema standard for building LLM-compiled personal knowledge bases using Claude. The approach involves dropping raw sources into a folder and having Claude compile concept articles, backlinks, and index files directly to markdown without RAG or vector databases.
Schema Details
The AGENTS.md file is versioned and contains 14 sections covering directory layout, compilation workflows, query workflows, linting workflows, learning layer, quality rules, and contamination mitigation. When dropped into any directory, Claude reads it at the start of every session to understand how to structure the wiki, name files, lint for contradictions, handle confidence levels, and avoid contaminating the wiki with low-quality agent output.
Learning Layer Addition
Beyond Karpathy's original archive workflow, this implementation adds a learning layer where Claude automatically generates flashcards from every concept article it writes. It maintains a spaced repetition review queue using the FSRS algorithm and tracks knowledge gaps detected during linting.
Claude Code Implementation
The developer used Claude Code to:
- Iterate on the AGENTS.md schema across dozens of sessions until agent behavior was consistent
- Write all 50 repository files including templates, documentation, and a worked example wiki on AI alignment
- Catch schema inconsistencies like frontmatter path convention differences between the spec and example articles
- Compile the worked example wiki (5 concept articles, flashcards, review queue, gap tracker) in a single session
Repository Contents
The GitHub repository includes:
- AGENTS.md v1.0 specification
- Templates for every file type (concept, summary, topic, flashcard, lint report, output report)
- Worked example wiki fully populated with AI alignment topic
- Documentation covering why not RAG, learning layer design, contamination mitigation, and fine-tune path
The project is MIT licensed and available for developers working with AI coding agents to structure and master their personal knowledge bases.
📖 Read the full source: r/ClaudeAI
👀 See Also
Four Claude Code Hooks Enforce Voice and Tone Consistency in AI-Written Copy
A developer built a system using four Claude Code hooks to prevent AI-generated copy from drifting off-brand. The system gates editing of copy files (.tsx, .md) until a reviewer agent validates content against a VOICE-AND-TONE.md guide.
JANG Quantization Method Improves MLX Performance for Large Models
A new quantization method called JANG enables running large models like MiniMax-M2.5 and Qwen 3.5 on Apple's MLX framework with significantly better performance than standard MLX quantization, achieving near-native speeds while maintaining accuracy comparable to higher-bit quantizations.
Librarian MCP: Local AI Server for Persistent Context with Documents
Librarian MCP is an open-source Model Context Protocol server that runs locally and connects to Jan, LM Studio, or Claude Desktop, enabling AI models to search and analyze document collections while maintaining full conversation context and data privacy.
Windows System Tray Monitor for Claude Code Quota
A Windows system tray application that monitors Claude Code usage with a color-coded icon, auto-refreshes quota data every 5 minutes via Anthropic's OAuth API, and provides detailed dashboards showing hourly, daily, weekly, and monthly usage patterns.