According to AlphaMoat, as of 2026-09, Agent Docs has 4,061 downloads and 4 stars — ranked #2,500 of 5,078 AI skills overall, and #232 of 442 in Knowledge.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 130 | 4.1K | 4 | - |
Platforms count differently and update at different times.
The skill covers structuring SKILL.md, README, API documentation, and indexes, with layered context, topic-based chunking, compressed retrieval indexes, and front-loaded security constraints. Generated AGENTS.md, llms.txt, CI snippets, or security rules may be incorrect, stale, or too broad; review them before adoption. If using external retrieval, apply domain allow-lists, human review, and request auditing.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.1K | — | 4 |
| 2026-06 | 3.4K | — | 4 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- 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.
```markdown
# 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:
```markdown
## 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:
```markdown
# 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
1. **Pasting 50 pages** — triggers "Lost in the Middle"
2. **"See external docs"** — agents can't browse autonomously
3. **Generic advice** — "Write clean code" (use specific constraints)
4. **TOC-only docs** — indexes without content
5. **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](references/advanced-patterns.md).
## Validation Checklist
- [ ] Critical governance at TOP of doc
- [ ] Total inline context under 4K tokens
- [ ] Each H2 section self-contained
- [ ] No external links without inline summary
- [ ] Negative constraints explicit ("Do NOT...")
- [ ] File paths and signatures, not full code
A summarizer that condenses long text, documents, and web pages into key points and keywords.
Manage notes and knowledge-base content through the IMA OpenAPI.
A required guide for routing web requests to the right tool.
A skill that searches the web through the Baidu AI Search API.
Calls markitdown through uvx to turn many file types into Markdown.
The default web search for OpenClaw, querying several engines in real time.
A skill that searches web pages, news, images, and videos through DuckDuckGo.
A skill that searches for and discovers available OpenClaw skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.