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.