fetch-skill Skill
**Description:** 统一 URL 内容抓取器。自动识别 URL 类型，路由到最佳后端，输出干净的 Markdown / JSON / 纯文本。
Want an agent-native computer in the browser? Try HappyCapy.
Cloud sandbox for AI agents · No setup · Run autonomous workflows from your browser
Affiliate link — we may earn a commission at no extra cost to you.
Why use this skill
fetch-skill is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is OpenClaw Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in aresbit/MateBot, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
# fetch-skill **Name:** `fetch-skill` **Description:** 统一 URL 内容抓取器。自动识别 URL 类型，路由到最佳后端，输出干净的 Markdown / JSON / 纯文本。 零依赖核心（普通网页 + 单条推文仅用 Python stdlib），Camofox / wechat-article-exporter 为可选增强。 --- ## 能力矩阵 | URL 类型 | 自动检测 | 后端 | 额外依赖 | |---|---|---|---| | 普通网页 | ✅ | Jina Reader → defuddle.md → markdown.new → Raw | 无 | | X/Twitter 单条推文 | ✅ | FxTwitter API（`api.fxtwitter.com`） | 无（零依赖）| | X/Twitter 回复 | `--replies` | Camofox + Nitter | Camofox（本地 9377）| | X/Twitter 用户时间线 | `--user` | Camofox + Nitter | Camofox | | X Article（长文）| ✅ | Camofox → Jina 兜底 | 推荐 Camofox | | 微信公众号文章 | ✅ | wechat-article-exporter API → Jina → defuddle → Raw | 可选 API | --- ## 快速开始 ```bash SKILL=~/.claude/skills/fetch-skill/scripts/fetch.py # 抓取任意网页（自动选最佳策略） python3 $SKILL https://example.com # 保存到文件 python3 $SKILL https://example.com -o output.md # 静默抓取（不输出进度） python3 $SKILL https://example.com -q # 人类可读的纯文本输出 python3 $SKILL https://example.com -t # 强制跳过 Jina，直接用 defuddle.md python3 $SKILL https://example.com --no-jina ``` ### X / Twitter ```bash # 单条推文（无需登录，无需 API Key） python3 $SKILL https://x.com/OpenAI/status/123456 -t # 推文 JSON 完整数据 python3 $SKILL https://x.com/OpenAI/status/123456 --pretty # 推文 + 回复（需要 Camofox） python3 $SKILL https://x.com/OpenAI/status/123456 --replies -t # 用户时间线，最多 100 条（需要 Camofox） python3 $SKILL https://x.com/elonmusk --user elonmusk --limit 100 -t # 或 python3 $SKILL --user elonmusk --limit 100 ``` ### 微信公众号 ```bash # Jina 兜底（无需额外配置） python3 $SKILL "https://mp.weixin.qq.com/s/xxxx" # 使用本地 wechat-article-exporter 服务 python3 $SKILL "https://mp.weixin.qq.com/s ...