技能目录

为 AI Agent 发现可复用技能。

按任务搜索真实的 GitHub 技能,并在使用前查看 Stars、信任、审计、分类和安装路径。

每个推荐都保留与其仓库、审计和安装路径的明确关联。

搜索结果: nature-of-code

英文目录

A tool that converts codebases, SQL schemas, and other files into queryable knowledge graphs for AI coding assistants.

92K
Stars
80/100
信任
分类: development审计

🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman

97K
Stars
85/100
信任
分类: development审计

Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.

171K
Stars
81/100
信任
分类: design-creative审计

🎨 Local-first, open-source Claude Design alternative. 🖥️ Native desktop app. ⚡ 259+ Skills · ✨ 142+ Design Systems 🖼️ Web · desktop · mobile prototypes · slides · images · videos · HyperFrames 📦 Sandboxed preview · HTML/PDF/PPTX/MP4 export 🤖 Claude Code / OpenClaw / Codex / Cursor / OpenCode / Qwen / Copilot / Hermes / Kimi & 17+ CLIs.

88K
Stars
88/100
信任
分类: agent-skills审计

Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.

54K
Stars
83/100
信任
分类: research审计

Review a branch or diff against repository standards and the originating spec in two independent analysis passes.

169K
Stars
86/100
信任
分类: coding-agents审计

Turn the current conversation and codebase context into a structured implementation spec, then publish it to the configured project issue tracker.

165K
Stars
81/100
信任
分类: coding-agents审计

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

17K
Stars
87/100
信任
分类: design-creative审计

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

162K
Stars
87/100
信任
分类: ml-automation审计