Manage ad campaigns across Google Ads, Meta Ads, LinkedIn Ads, and TikTok Ads. Use when the user wants to analyze campaign performance, research keywords, create campaigns, optimize budgets, or manage ad accounts via the Adspirer MCP server.
End-to-end TypeScript engineering skill for designing, implementing, reviewing, and refactoring production-grade TS codebases. Use when tasks involve TypeScript architecture, advanced typing, strict safety, API/schema typing, React/Node/Nest/Express/Fastify patterns, testing, linting, migration, or code review in .ts/.tsx projects.
Scan, harden, sign, and verify release artifacts with ReleaseGuard — the artifact policy engine for dist/ and release/ outputs.
Update the built-in model configurations for pair-review's AI providers. This skill
Audit and improve web accessibility following WCAG 2.2 guidelines. Use when asked to "improve accessibility", "a11y audit", "WCAG compliance", "screen reader support", "keyboard navigation", or "make accessible".
Perform calculations and math operations. Use for arithmetic, conversions, or formulas.
This skill should be invoked BEFORE presenting implementation plans, architecture recommendations, code review findings, or answers to broad technical questions. Use proactively when about to "recommend", "suggest", "propose", "design", "plan", or answer "how should", "what's the best way", "which approach". MANDATORY for multi-file changes, refactoring proposals, and security-sensitive recommendations.
Prisma CLI commands reference covering all available commands, options, and usage patterns. Use when running Prisma CLI commands, setting up projects, generating client, running migrations, managing databases, or starting Prisma's MCP server. Triggers on "prisma init", "prisma generate", "prisma migrate", "prisma db", "prisma studio", "prisma mcp".
Architecture Decision Records with evaluate and design modes. Document significant technical decisions with context, alternatives, and consequences.
Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summaries. Use this skill when the user asks "how many tokens did this session use", "how many prompts have I sent", "show me the stats for this conversation", "what got compacted", "where are the compaction boundaries", "introspect the session", "do brain surgery on the JSONL", or wants any data point that lives inside the on-disk session log rather than the live context window. Inspired by Tal Raviv's "I wanted to know how compaction works" article.
微信聊天记录深度分析工具。分析1对1单聊的互动习惯、情感走势、性格画像，生成可视化图表和数据导出。当用户提到微信聊天分析、聊天记录分析、感情分析、好感度分析、追求建议、聊天统计、聊天热力图、词云、秒回率、主动性分析、聊天导出等任何与微信聊天数据分析相关的话题时，务必使用此技能。即使用户只是简单提到"分析我和某人的聊天"也要触发。
企业级AI提示词全生命周期开发规范，包含需求设计、开发编写、版本管控、测试验收、安全合规全流程，适用于企业级提示词项目开发
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:
