Kalshi prediction markets API documentation. Use when building integrations with Kalshi's REST API or WebSocket streams for trading, market data, or portfolio management.
Review a plan or implementation for model integrity, service boundaries, and canonical architecture direction. Use when a change touches service boundaries, data models, or trust boundaries.
学术论文出图引擎——覆盖所有常见图表类型，执行五层质量框架。 触发条件: (1) 用户说"画图"/"出图"/"generate figure"/"重新画", (2) 需要生成 KM曲线/森林图/热图/箱线图/散点图/雷达图/Table等, (3) manuscript-reviewer 或用户指出图表质量问题, (4) paper-pipeline 在分析完成后自动调用。 核心能力: 从原始数据生成达到期刊投稿标准的图表， 每个数字溯源验证，不通过对账不出图。
抖音一站式运营助手。基于 Python CDP 浏览器自动化引擎。 核心能力：账号认证、内容发布、视频上传、搜索发现、评论互动、数据分析、AI短漫剧生成。 触发场景：发布抖音、抖音视频、抖音运营、抖音搜索、回复评论、生成抖音内容、抖音账号管理、AI短漫剧生成、小说转视频。 使用此技能当用户需要：运营抖音账号、发布视频到抖音、搜索抖音内容、回复抖音评论、分析抖音数据、做抖音账号定位、生成AI短漫剧。
ZIRAN is an open-source security testing framework for AI agents. It discovers dangerous tool chain compositions via knowledge graph analysis, detects execution-level side effects (not just text output), and runs multi-phase trust exploitation campaigns that model real attacker behavior.
Visual documentation generator. Creates Mermaid diagrams from code: architecture, sequence, ER, flow, component, class, and state diagrams. Use when user says 'diagram', 'draw', 'visualize', 'show me the flow', 'architecture diagram', 'sequence diagram', 'ER diagram', 'data model diagram'.
Search and analyze cryptocurrency market data using CoinPaprika. Activates when users search for coins, ask about prices, request market overviews, or look up tokens by contract address.
Run IronBee session analysis with semantic interpretation of verification metrics, issues, and fixes
Iterate and improve any skill using eval-grounded autoresearch. Combines v2.0 design audit, Hamel's Three Gulfs eval methodology, and Karpathy-style mutation optimization. Use when you want to assess skill quality, build evals from scratch, run error analysis, or optimize a skill through experiments.
This skill should be used when automating browser interactions via Vercel's agent-browser CLI. It handles web page navigation, form filling, screenshots, and data scraping using ref-based element selection.
Automate Chrome for web scraping, SEO analysis, and extracting data from JavaScript-heavy sites. Use for multi-page scraping, authenticated content, screenshots, or when WebFetch isn't enough.
Use when researching what the vault knows about a topic — performs graph + semantic exploration with gap analysis to map existing knowledge and find missing connections. Primarily used via the vault-explorer agent. Not for routine session-start context loading (hooks handle that).
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:
