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Docs Feeder

作者 zerone0x · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install docs-feeder
功能描述
Automatically fetches comprehensive project documentation from built-in registries or URLs to assist AI agents in debugging and learning.
使用说明 (SKILL.md)

Docs Feeder

Auto-fetch project documentation and feed it to your AI agent for debugging and learning.

Triggers

  • docs feed \x3Cproject>
  • fetch docs \x3CURL>

How It Works

  1. Registry Lookup — 50+ built-in projects (React, Next.js, Hono, Prisma, Anthropic, etc.)
  2. Fetch Priority:
    • /llms-full.txt → Full LLM-friendly docs
    • /llms.txt → Compact version
    • GitHub README → Fallback
  3. Smart Discovery — Unknown projects try common patterns (docs.xxx.com, xxx.dev)
  4. Size Warning — Alerts when docs exceed 500KB

Usage

# By project name (auto-lookup)
node fetch-docs.js nextjs

# By URL (direct fetch)
node fetch-docs.js https://docs.anthropic.com

# Raw content only (no metadata header)
node fetch-docs.js react --raw

# Save to file
node fetch-docs.js prisma --save

# List all supported projects
node fetch-docs.js --list

Built-in Registry

50+ projects including: React, Next.js, Vue, Svelte, Astro, Hono, Express, Fastify, NestJS, Prisma, Drizzle, tRPC, Zod, Tailwind CSS, shadcn/ui, TypeScript, Vite, Bun, Deno, Playwright, Vitest, Supabase, Stripe, Clerk, Anthropic, OpenAI, LangChain, Docker, Kubernetes, Terraform, Rust, Go, Python, FastAPI, Django, and more.

Edit docs-registry.json to add your own projects.

Registry Format

{
  "myproject": {
    "url": "https://myproject.dev",
    "llms": "/llms-full.txt",
    "github": "org/repo",
    "local": "/path/to/local/docs"
  }
}

Workflow

Fetch docs, then describe your problem:

→ node fetch-docs.js nextjs
→ [docs loaded into context]

"I'm getting a hydration mismatch error with App Router..."
→ [AI gives solution based on complete documentation]

Why This Works

Most modern doc sites ship /llms.txt or /llms-full.txt — a single file with the entire knowledge base formatted for LLMs. Instead of searching + reading + understanding docs manually, dump the whole thing into context and let the AI cross-reference.

Requirements

  • Node.js (no external dependencies)
安全使用建议
This skill will fetch documentation from arbitrary URLs and can read local file paths if they appear in docs-registry.json. Before installing or invoking it: 1) Inspect and remove any 'local' entries in docs-registry.json (for example the included /usr/lib/... path) so the skill cannot read host files you don't expect. 2) Treat it as untrusted network code: avoid letting it run autonomously in environments with access to internal networks or cloud instance metadata (it will try any URL you pass or guess common patterns). 3) If you must use it, require explicit user invocation only and run it in a sandboxed agent executor with restricted network access. 4) Do not pass internal IPs/hostnames or sensitive internal URLs as arguments. If you want to be safer, prefer fetching docs manually (or whitelist specific domains) and avoid the automatic URL-guessing behavior.
功能分析
Type: OpenClaw Skill Name: docs-feeder Version: 1.0.0 The skill is classified as suspicious due to its broad network and file access capabilities, which, while aligned with its stated purpose of fetching documentation, present significant security risks. Specifically, the 'smart discovery' feature in both `fetch-docs.sh` and `fetch-docs.js` allows the agent to construct and fetch from arbitrary URLs (e.g., `https://docs.${input}.com`) based on user input, creating a potential Server-Side Request Forgery (SSRF) vulnerability. Additionally, the skill can read local files from paths specified in `docs-registry.json` (e.g., `/usr/lib/node_modules/clawdbot/docs`), which could be abused for arbitrary file reading if the registry entries were manipulated or pointed to sensitive system files.
能力评估
Purpose & Capability
Name/description (fetch project docs) align with the code's main behavior (fetching /llms*.txt, fallback to GitHub README). However the bundled registry includes a 'local' entry (/usr/lib/node_modules/clawdbot/docs) and the code supports reading arbitrary local paths defined in the registry. Reading local files is not obviously necessary for a general 'docs feeder' unless the user explicitly configures local docs; bundling such a path in the registry is unexpected and broadens the skill's capability.
Instruction Scope
SKILL.md documents registry/local entries and usage, but runtime instructions and the code will: fetch any URL you pass (or guess patterns), follow redirects, and read local filesystem paths listed in docs-registry.json. That means an agent invoking this skill can request internal URLs (e.g., 169.254.169.254 or intranet hosts) or cause the skill to read local files if a registry entry points at them — both are outside the narrow notion of 'public documentation fetching' and can expose sensitive data.
Install Mechanism
This is an instruction-only skill with bundled scripts (no install spec). Nothing is downloaded at install time and no external installers are invoked. The risk surface comes from the scripts themselves, not from install-time downloads.
Credentials
The skill requests no environment variables or credentials (proportionate). However, it can access local paths (via registry.local) and arbitrary network endpoints provided by the user/agent — this is an implicit capability that doesn't require credentials but may access sensitive system metadata or internal services.
Persistence & Privilege
always is false and the skill does not modify other skills or system-wide agent settings. It writes only when the user passes --save (to /tmp) or if registry contains local paths; otherwise it outputs results to stdout. Autonomous invocation is allowed by default (platform default) — factor this in with the other concerns.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install docs-feeder
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /docs-feeder 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of Docs Feeder: auto-fetch documentation for 50+ popular projects. - Supports fetching by project name (from built-in registry) or direct URL. - Prioritizes LLM-optimized doc files (`/llms-full.txt`, `/llms.txt`), with fallback to GitHub README if needed. - Smart discovery of docs URLs for unknown projects. - Warns users if doc content exceeds 500KB. - CLI options include raw output, saving to file, and listing all supported projects. - Easily extend registry with custom projects via `docs-registry.json`.
元数据
Slug docs-feeder
版本 1.0.0
许可证
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Docs Feeder 是什么?

Automatically fetches comprehensive project documentation from built-in registries or URLs to assist AI agents in debugging and learning. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 708 次。

如何安装 Docs Feeder?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install docs-feeder」即可一键安装,无需额外配置。

Docs Feeder 是免费的吗?

是的,Docs Feeder 完全免费(开源免费),可自由下载、安装和使用。

Docs Feeder 支持哪些平台?

Docs Feeder 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Docs Feeder?

由 zerone0x(@zerone0x)开发并维护,当前版本 v1.0.0。

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