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。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 130 | 4061 | 4 | - |
各平台的計數口徑不同,統計時間不一致。
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
以下是根據技能文件生成的示例,不代表實際執行結果。
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 |
|---|---|---|---|
| 2026-09 | 4061 | — | 4 |
| 2026-06 | 3430 | — | 4 |
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。
--- 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
把長文字、文件與網頁內容壓縮成要點和關鍵詞的摘要工具。
透過 IMA OpenAPI 管理筆記與知識庫內容。
規定如何為網頁請求選擇合適工具的必讀指引。
呼叫百度 AI 搜尋介面做網頁檢索的技能。
用 uvx 直接呼叫 markitdown,把各類檔案轉成 Markdown。
面向 OpenClaw 的預設網頁搜尋,即時檢索多個引擎。
用 DuckDuckGo 介面做網頁、新聞、圖片與影片檢索的技能。
檢索並發現可用 OpenClaw 技能的技能。
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。