| name | agent-docs |
| description | Create documentation optimized for AI agent consumption. Use when writing SKILL.md files, README files, API docs, or any documentation that will be read by LLMs in context windows. Helps structure content for RAG retrieval, token efficiency, and the Hybrid Context Hierarchy. |
Agent Docs
Write documentation that AI agents can efficiently consume. Based on Vercel benchmarks and industry standards (AGENTS.md, llms.txt, CLAUDE.md).
The Hybrid Context Hierarchy
Three-layer architecture for optimal agent performance:
Layer 1: Constitution (Inline)
Always in context. 2,000–4,000 tokens max.
# AGENTS.md
> Context: Next.js 16 | Tailwind | Supabase
## 🚨 CRITICAL
- NO SECRETS in output
- Use `app/` directory ONLY
## 📚 DOCS INDEX (use read_file)
- Auth: `docs/auth/llms.txt`
- DB: `docs/db/schema.md`
Include:
- Security rules, architecture constraints
- Build/test/lint commands (top for primacy bias)
- Documentation map (where to find more)
Layer 2: Reference Library (Local Retrieval)
Fetched on demand. 1K–5K token chunks.
- Framework-specific guides
- Detailed style guides
- API schemas
Layer 3: Research Assistant (External)
Gated by allow-lists. Edge cases only.
- Latest library updates
- Stack Overflow for obscure errors
- Third-party llms.txt
Why This Works
Vercel Benchmark (2026):
| Approach | Pass Rate |
|---|
| Tool-based retrieval | 53% |
| Retrieval + prompting | 79% |
| Inline AGENTS.md | 100% |
Root cause: Meta-cognitive failure. Agents don't know what they don't know—they assume training data is sufficient. Inline docs bypass this entirely.
Core Principles
1. Compressed Index > Full Docs
An 8KB compressed index outperforms a 40KB full dump.
Compress to:
- File paths (where code lives)
- Function signatures (names + types only)
- Negative constraints ("Do NOT use X")
2. Structure for Chunking
RAG systems split at headers. Each section must be self-contained:
## Database Setup ← Chunk boundary
Prerequisites: PostgreSQL 14+
1. Create database...
Rules:
- Front-load key info (chunkers truncate)
- Descriptive headers (agents search by header text)
3. Inline Over Links
Agents can't autonomously browse. Each link = tool call + latency + potential failure.
| Approach | Token Load | Agent Success |
|---|
| Full inline | ~12K | ✅ High |
| Links only | ~2K | ❌ Requires fetching |
| Hybrid | ~4K base | ✅ Best of both |
4. The "Lost in the Middle" Problem
LLMs have U-shaped attention:
- Strong: Start of context (primacy)
- Strong: End of context (recency)
- Weak: Middle of context
Solution: Put critical rules at TOP of AGENTS.md. Governance first, details later.
5. Signal-to-Noise Ratio
Strip everything that isn't essential:
- No "Welcome to..." preambles
- No marketing text
- No changelogs in core docs
Formats like llms.txt and AGENTS.md mechanically increase SNR.
llms.txt Standard
Machine-readable doc index for agents:
# Project Name
> One-line project description.
## Authentication
- [Setup](docs/auth/setup.md): Environment vars and init
- [Server](docs/auth/server.md): Cookie handling
## Database
- [Schema](docs/db/schema.md): Full Prisma schema
Location: /llms.txt at domain root
Companion: /llms-full.txt — full concatenated docs, HTML stripped
Security Considerations
Inline = Trusted
AGENTS.md is part of your codebase. Controlled, version-pinned.
External = Attack Surface
- Indirect prompt injection via hidden text
- SSRF risks if agents can browse freely
- Dependency on external uptime
Mitigation: Domain allow-lists, human-in-the-loop for external retrieval.
Anti-Patterns
- Pasting 50 pages — triggers "Lost in the Middle"
- "See external docs" — agents can't browse autonomously
- Generic advice — "Write clean code" (use specific constraints)
- TOC-only docs — indexes without content
- Trusting retrieval alone — 53% vs 100% pass rate
Advanced Patterns
For detailed guidance on RAG optimization, multi-framework docs, and API templates, see references/advanced-patterns.md.
Validation Checklist
