GITHUB AGENT SKILLS / 技能收录
给你的 AI 助手
装上专业技能
我们从 GitHub 持续收录、审核并分类优质的 Agent Skills，按职业与使用场景整理，复制一行命令即可安装到 Claude Code、Cursor 等智能体。
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按 GitHub Stars 排序的优秀创作者
Microsoft
@microsoft
openclaw
@openclaw
OpenAI
@openai
The Apache Software Foundation
@apache
GitHub
@github
Anthropic
@anthropics
AutoGPT
@Significant-Gravitas
Garry Tan
@garrytan全部 Skills 196838
speckit-clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
nutrient-document-processing
Process, convert, OCR, extract, redact, sign, and fill documents using the Nutrient DWS API. Works with PDFs, DOCX, XLSX, PPTX, HTML, and images.
speckit-checklist
Generate a custom checklist for the current feature based on user requirements.
speckit-analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
notion-spec-to-implementation
Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.
epcc
暂无简介
notion-research-documentation
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
notion-meeting-intelligence
Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.
notion-knowledge-capture
Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
netlify-deploy
Use when deploying a web project to Netlify via npx netlify, linking a repo, or creating preview or production Netlify deploys ; do NOT use for Vercel (use vercel-deploy), Render (use render-deploy), or Cloudflare (use cloudflare-deploy) ; returns Netlify deploy URL.
nanoclaw-repl
Operate and extend NanoClaw v2, ECC's zero-dependency session-aware REPL built on claude -p.
mutation-check
Use at the verify boundary to empirically test test-STRENGTH, not just coverage - given a changed Python source file and the command that runs its tests, it applies a small mutation set to the function(s) under test and asserts each mutation makes at least one test go RED. A mutation the suite leaves green ("survived") is a Goodhart-green gap. Use as a gate after coverage; Python-only.
multilingual-search
Reach non-English sources deliberately - query in the target language against a native-index engine (Yandex for RU, Quark/360search for CN, Naver for KR, Seznam for CZ), fuse several engines with Reciprocal Rank Fusion, and cite the original while translating only on output. Use when a research question has regional, non-English, or non-Western coverage that an English-only search will miss, when results feel thin or one-sided, when a topic is primarily discussed in Russian/Chinese/Korean/Japanese/Arabic sources, or when a claim needs corroboration from an index Google and Bing do not dominate. Also use when merging results from several search providers into one ranking.
multi-ai-mingle
Use when the user wants seamless local collaboration between Claude Code and OpenAI Codex on the same machine - fanning out parallel Codex coding threads from Claude as native MCP tool calls via the official codex mcp-server, and running a live symmetric two-way chat and file-lease channel between the two agents through mcp_agent_mail. Covers mounting codex-as-MCP at user scope, standing up the shared agent-mail http channel, verifying it before use, and the sequenced adoption waves. Triggers include mingle Claude and Codex, run Codex from Claude in parallel, set up the agent mail channel, replace AgentBridge, or coordinate the two local AI agents. Not for single-agent work or remote or cloud multi-agent orchestration.
multi-agent-quickstart
Use when learning how to reach Codex from this Windows-first setup - the beginner mental model for the codex mcp-server rail (mcp__codex__codex / mcp__codex__codex-reply), how the retired per-mode codex_* tools map onto prompt framings, when to fan out parallel calls instead, and how to choose an execution path after planning.
monitor-launcher
The generalized `mon <name>` launcher for the unified monitoring platform - opens any registered monitor (wfmon fleet cockpit, agentmon per-agent tails) from monitors.toml. Use when asked to launch, open, or wire a monitor, add one to the registry, or reasoning about the `mon`/wfmon/agentmon launch flow and the monitor-autolaunch hook. NOT for driving the tail itself (use run-agent-monitor).
mol-filters
Use when mol filters is needed. Synthetic-accessibility SA_Score plus PAINS/BRENK/NIH structural-alert filtering (RDKit FilterCatalog) for a SMILES or SDF. Use when triaging docking or virtual-screening hits, flagging pan-assay-interference / problematic scaffolds, or scoring how hard a molecule.
mle-workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.