- 📄 SKILL.md
benchmark-lab
Design benchmark runs, ablations, dataset specs, and failure-analysis artifacts.
Design benchmark runs, ablations, dataset specs, and failure-analysis artifacts.
Smart Blog 品質分析。5 大類 100 分評分,包含 Humanizer 29 模式 AI 偵測、 SEO 驗證、E-E-A-T 評估、PageSpeed 整合。支援 PDF 報告輸出。 Use when user says "analyze blog", "分析文章", "blog audit", "品質評分", "smart-blog analyze", "blog analyze".
Use this skill when working with git commands, version control, or repository management
Review and resolve unresolved brain merge conflicts.
multi-role ai creative team for brand, content, and design workflows. use when a task involves brief analysis, research, planning, scripting, visual direction, brand design, marketing materials, social media execution, or project coordination across different creative roles.
Mobile accessibility patterns for Android and iOS - content descriptions, touch targets, screen reader support, WCAG compliance, dynamic type, and color contrast.
This skill teaches AI coding agents how to use the ACCESSIBILITY.md framework,
Schedule reminders and recurring tasks.
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指导如何高效使用 web_search 和 web_extract 工具进行网络资料查找、深度阅读与信息整合当用户要求“搜索一下”、“查资料”、“总结网页”、“了解最新动态”时触发 --- # 标准操作指南 你的目标是利用 `web_search` 和 `web_extract` 这两个工具,以**最少的 Token 消耗、最精准的方式**为用户获取高质量信息 ## 🛠️ 工具核心认知 你有两个核心武器,请严格按照它们的定位使用: 1. **`web_search` (广度扫描)**:用于寻找线索、获取最新资讯或快速回答 - **绝招**:利用 `time_range` 或 `days` 找最新消息;利用 `include_domains` 在特定网站(如 github.com, zhihu.com)内定向搜索 - **捷径**:如果只需要网页的粗略内容,直接开启 `include_raw_content: true`,通常可以省去后续调用 `web_extract` 的步骤 2. **`web_extract` (深度挖掘)**:用于精准提取长文章、深度报告或复杂网页的核心内容 - **绝招**:**永远不要把整个长网页无脑塞进上下文!** 如果你只关心网页中的特定问题,**必须**使用 `query` 参数,并配合 `chunks_per_source`(建议设为 3-5),让工具只返回最相关的片段 - **注意**:只有当 `web_search` 的摘要信息不足以回答用户问题时,才挑选 1-3 个最有价值的 URL 使用此工具 --- ## 📋 高效检索标准工作流 (SOP) 处理用户的搜索需求时,请遵循以下 4 步流程: ### 第一步:需求拆解与策略制定 - 分析用户需要的是“时效性信息”(如今天的新闻)、“特定站点信息”(如某公司的财报),还是“通用知识” - 确定搜索关键词(建议拆分为多个精准的 query) ### 第二步:执行广度搜索 (`web_search`) - 根据策略调用 `web_search` - **参数配置建议**: - 找新闻:`topic: "news"`, `time_range: "w"` (近一周) - 找特定网站内容:`include_domains: ["example.com"]` - 需要直接看内容:`include_raw_content: true`, `max_results: 5`(减少数量以防 Token 溢出) ### 第三步:评估与深度提取 (`web_extract`) - 阅读 `web_search` 返回的 Snippet(摘要)如果信息已经足够回答,**直接跳到第四步** - 如果信息不足,挑选 1-3 个最权威、最相关的 URL 调用 `web_extract` - **高阶技巧**:如果网页很长(如维基百科、官方文档),**务必**在 `web_extract` 中传入 `query` 参数(例如 `query: "2023年Q3营收数据"`),强制工具进行智能分块和重排序,只提取你需要的精准片段 ### 第四步:信息交叉验证与输出 - 综合所有获取到的信息进行总结 - **强制要求**:在回答中必须使用 Markdown 链接格式标注信息来源,例如:`[来源](URL)`如果不同来源信息有冲突,需客观指出 --- ## 💡 典型场景参数模板 (Cheat Sheet) 遇到以下场景时,请直接参考这些参数组合: **场景 A:用户问“今天 AI 圈有什么大新闻?”** - 工具:`web_search` - 参数:`topic: "news"`, `time_range: "d"`, `max_results: 10` **场景 B:用户问“帮我总结一下这个 Github 项目的 README:[URL]”** - 工具:`web_extract` - 参数:`urls: ["[URL]"]`, `extract_depth: "basic"`, `format: "markdown"` **场景 C:用户问“在苹果官网查一下 iPhone 15 Pro 的钛金属材质说明”** - 动作 1:`web_search` -> `query: "iPhone 15 Pro 钛金属"`, `include_domains: ["apple.com"]` - 动作 2:拿到具体 URL 后,调用 `web_extract` -> `urls: [URL]`, `query: "钛金属 材质 制造工艺"` (精准提取,忽略其他无关参数) --- **⚠️ 最终警告:** 1. 不要为了搜索而搜索,如果你的内在知识足以完美且准确地回答(且不需要最新数据),
Wake or shut down LAN devices from the agent host. Use this when the user wants to power on a device with a raw Ethernet Wake-on-LAN frame or power off a device with a WOL-plus UDP packet, and they can provide the target MAC address plus a network interface for wake or a target IPv4 address for shutdown.
Use when a human wants to share a logged-in tab from the isolated OpenClaw browser so the agent can work through LobsterLink in that authenticated session. Triggers include requests to share a tab, give a LobsterLink peer/share ID, use a logged-in tab, or host/stop hosting a tab through LobsterLink. Skip install/reinstall if INSTALLED is already marked true in this skill.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills: