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Import Skills

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bearcove bearcove
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
tracey when coverage

tracey

Add proper Tracey spec annotations to code, find requirements, and check coverage. Use when working with projects that have Tracey configuration (.config/tracey/config.styx), when adding spec references to code, or when checking requirement coverage.

⬇0 ❤180 21 days ago · Uploaded Detail →
microsoft microsoft
from GitHub Development & Coding
  • 📄 SKILL.md
data add user

add-data-source

Guide the user to add a data source, connection, or API connector to a Canvas App via Power Apps Studio, then verify and continue. USE WHEN the user asks to add a data source, add a connection, add an API, add a connector, connect to SharePoint / Dataverse / SQL / Excel / OneDrive / Teams / Office 365, or any similar request to make new data available to the app. DO NOT USE WHEN the user is asking to list or describe existing data sources — call list_data_sources or list_apis directly instead.

⬇0 ❤180 21 days ago · Uploaded Detail →
WhiteNightShadow WhiteNightShadow
from GitHub Development & Coding
  • 📁 cases/
  • 📁 examples/
  • 📁 references/
  • 📄 .gitignore
  • 📄 CHANGELOG_v2.9.0.md
  • 📄 README.md
python data node.js

hello_js_reverse_skill

Node.js / Python 接口自动化与签名还原工程技能：对自有平台或已授权平台的 Web API 进行签名分析与接口对接， 通过 Camoufox 反检测浏览器动态调试与静态源码分析，定位并还原前端加密/签名逻辑， 使用 Node.js 或 Python 实现算法复现与自动化接口调用。 深度集成 camoufox-reverse MCP v0.8.0（C++ 引擎级指纹伪装，78 个逆向分析工具，域级 Session 档案 + 断言系统）。 擅长 JSVMP 虚拟机保护的双路径攻克：路径 A 算法追踪（Hook / 插桩 / 日志分析 / 源码级插桩四板斧， 通用对RS 5/6、Akamai sensor_data、webmssdk、obfuscator.io）、 路径 B 环境伪装（jsdom/vm 沙箱 + 浏览器环境采集对比 + 全量补丁）。 v2.6.0 新增反爬类型三分法（签名型/行为型/纯混淆）作为顶层决策框架，明确 pre_inject_hooks 与 hook_jsvmp_interpreter(mode="proxy") 对签名型反爬不可用， 引入 mode="transparent" 签名安全备选与 MCP 侧 AST 源码插桩（消除 CDN 依赖）。 v2.9.0 新增域级 Session 档案（跨任务复用反爬判定/指纹基准/Cookie 归因）与断言驱动交付体系， Phase 5 升级为断言驱动结构化交付，新增降级梯度原则防止 AI 过早放弃。

⬇0 ❤175 19 days ago · Uploaded Detail →
microsoft microsoft
from GitHub Data & AI
  • 📁 references/
  • 📄 SKILL.md
data kql patterns

kql

KQL language expertise for writing correct, efficient Kusto queries using the Fabric RTI MCP tools. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Eventhouse, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.

⬇0 ❤109 4 days ago · Uploaded Detail →
tomasz-tomczyk tomasz-tomczyk
from GitHub Tools & Productivity
  • 📄 SKILL.md
crit review comments

crit-cli

Use when working with crit CLI commands, review files, addressing review comments, leaving inline code review comments, sharing reviews via crit share/unpublish, pushing reviews to GitHub PRs, or pulling PR comments locally. Covers crit comment, crit share, crit unpublish, crit pull, crit push, review file format, and resolution workflow.

⬇0 ❤177 21 days ago · Uploaded Detail →
nicobailon nicobailon
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
code generate-more high-fidelity

design-deck

Present visual options for architecture, UI, and code decisions with high-fidelity side-by-side previews. For comparing approaches visually — code diffs, diagrams, UI mockups, images — not for gathering structured input (use interview for that). Supports previewBlocks (code, mermaid, image, html), previewHtml, generate-more loops, and plan/PRD-driven flows.

⬇0 ❤165 16 days ago · Uploaded Detail →
tavily-ai tavily-ai
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 SKILL.md
automation docs ai

tavily-best-practices

Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.

⬇0 ❤172 19 days ago · Uploaded Detail →

Skill File Structure Sample (Reference)

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

SKILL.md Requirements

├─ ⭐ 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

Why SkillWink?

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.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

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.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

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.

2. How do Skills work?

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.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared 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.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up