Build, package, and verify DesktopManager NuGet, PowerShell module, and CLI/MCP artefacts through the repo-standard PowerForge entrypoints. Use when changing Build/Build-Project.ps1, Build/Build-Module.ps1, Build/project.build.json, powerforge.dotnetpublish.json, module packaging, CLI publish outputs, release assets, or build/operator documentation.
Fast headless browser for QA testing and site dogfooding. Navigate pages, interact with elements, verify state, diff before/after, take annotated screenshots, test responsive layouts, forms, uploads, dialogs, and capture bug evidence. Use when asked to open or test a site, verify a deployment, dogfood a user flow, or file a bug with screenshots.
Semantic code search, regex pattern search, and symbol lookup across a local repository. Returns ranked markdown codeblocks with file path, line range, content, and optional symbol info. Use `vera search` for conceptual/behavioral queries (how a feature works, where logic lives, exploring unfamiliar code). Use `vera grep` for exact strings, regex patterns, imports, and TODOs. Use `vera references` to trace callers/callees. Use rg only for bulk find-and-replace or files outside the index.
当用户希望用《毛泽东选集》/教员的方法分析现实问题、把毛选中的方法论转成现代分析框架，或明确提出“用毛选帮我分析”“用教员的方法帮我分析 xxx”“用新青年帮我分析”“按新青年的方法拆一下”“按主要矛盾/阶段判断/统一战线/实践检验的方法分析”时使用。适用于工作推进、复杂协作、关系边界、学习成长、生活决策、团队治理等结构性问题；一旦触发，就默认按复杂结构题处理。澄清是所有任务的硬前提：先锁目标，再持续澄清到关键结构基本清楚，之后才进入分析。澄清题默认使用带 A/B/C/D 和“其他”的选项式追问。正式输出前再确认用户要文字版还是 HTML 版。
Consolidate recent logs, sessions, and existing memory files into durable topic memories, normalize dates, prune stale entries, and keep MEMORY.md short enough for prompt use.
Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.
Recursive maths animator — Manim-based technical animations with optional voiceover (manim-voiceover), git scene versioning, pinned requirements, asset folders, GIF approval previews, and a vision verification loop (frame extract, multimodal review in Cursor/Claude Code, VERIFICATION_FEEDBACK.md, iterate). Brief-first workflow: pitch a digestible animation plan and design options, get user approval, then code; lock theme in DESIGN_THEME.md.
Use this skill when the user asks to "build a W2A sensor", "create a world2agent sensor", "add a sensor for <source>", or otherwise wants to emit signals into the World2Agent protocol from a new data source. Guides discovery → design → scaffold → install recipe → test.
规范化 Excalidraw 图，确保后续可维护编辑、绑定关系安全、编辑器辅助重排和夜间模式安全导出。处理 `.excalidraw` 架构图、拓扑图、流程图或系统图，并且需要修复 container/binding、重排几何布局、清理连接线或导出透明 PNG 到文档时使用。
Build automated fleet monitoring workflows using n8n. Use this skill when asked to create agents, automations, or monitoring systems that connect Geotab to external services like Slack, Discord, email, or other APIs.
Structured B2B software vendor evaluation for buyers. Researches your company, asks domain-expert questions, engages vendor AI agents via the Salespeak Frontdoor API, scores vendors across 7 dimensions, and produces a comparative recommendation with evidence transparency. Use when asked to evaluate, compare, or research B2B software vendors.
The Balanced Coupling model for software design. Use when: designing modular architectures, evaluating coupling between components, reviewing code modularity, deciding whether to split or merge modules/services, assessing integration patterns, classifying coupling as balanced or unbalanced, applying DDD strategic and tactical patterns, reasoning about cohesion vs coupling trade-offs, identifying distributed monolith risks, or explaining why a system is hard to change. Provides the three-dimensional framework (integration strength, distance, volatility) and the balance rule for making coupling decisions.
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:
