tylervovan

Agent Docs

Knowledgev1.0.0
By tylervovanUpdated 2026-05-11agent-docs
#Docs#Documentation#Rag

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.

Installs
130
Downloads
4.1K
Stars
4
Overall Rank
#2,500
Top 50% overall · Top 53% in Knowledge
PlatformInstallsDownloadsStarsComments
ClawHub ↗1304.1K4-

Platforms count differently and update at different times.

What this skill does

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

What you can do with it

  • Developers and technical writers creating agent-oriented project docs, API guides, or retrieval indexes.

When to use it

  • Treat recommendations as drafts. Verify commands, paths, security rules, and sources before adding them to a project. Limit external retrieval to approved domains and review requests.

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Plan the structure of agent-readable documentation for a project and list checks before adoption; do not write or modify files.

Monthly Trend

Downloads4.1K
3.4K3.7K4.1K2026-06 · Downloads 3.4K2026-09 · Downloads 4.1K26-0626-09
Stars4
3452026-06 · Stars 42026-09 · Stars 426-0626-09

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.

MonthDownloadsMoMStars
2026-094.1K—4
2026-063.4K—4

SKILL.md (original)

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.

4.5 KB · ClawHub v1.0.0
---
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
Show all (169 lines)

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

Popular in Knowledge

Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.