tylervovan

Agent Docs

知識管理v1.0.0
作者:tylervovan更新於 2026-05-11agent-docs
#Docs#Documentation#Rag

據 AlphaMoat 統計,截至 2026-09,Agent Docs 累計下載 4,061 次、獲 4 個星標,在全部 5,078 個 AI 技能中排名第 2,500 位,在「知識管理」分類(442 個)中排名第 232 位。

累計安裝
130
累計下載
4061
星標
4
全站排名
#2,500
全站前 50% · 「知識管理」前 53%
平台累計安裝累計下載星標評論
ClawHub ↗13040614-

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

該技能介紹如何組織 SKILL.md、README、API 文件和索引,強調分層上下文、按主題分塊、壓縮檢索索引,並前置安全約束。生成的 AGENTS.md、llms.txt、CI 片段或安全規則可能不準確、過時或範圍過寬;採納前應逐項稽核。若採用外部檢索,應使用域名白名單、人工複核並審計請求。

資料釋出於 2026-10-29

可以用來做什麼

  • 需要編寫面向 agent 的專案文件、API 指南或檢索索引的開發者和技術寫作者。

什麼時候用

  • 將文件建議視為草稿;在提交到專案之前核驗命令、路徑、安全規則和來源。外部檢索需限制允許域名並保留稽核。

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

為一個專案規劃 agent 可讀文件的結構,並列出採納前的核驗步驟;不要編寫或修改檔案。

月度資料趨勢

累計下載4061
3380374641112026-06 · 累計下載 34302026-09 · 累計下載 406126-0626-09
星標4
3452026-06 · 星標 42026-09 · 星標 426-0626-09

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份累計下載環比星標
2026-094061—4
2026-063430—4

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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
展開全部(共 169 行)

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

「知識管理」分類下的熱門技能

資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。