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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Microsoft
@microsoftopenclaw
@openclawOpenAI
@openaiThe Apache Software Foundation
@apacheGitHub
@githubAnthropic
@anthropicsAutoGPT
@Significant-GravitasGarry Tan
@garrytan全部 Skills 196852
conductor-validator
Validates Conductor project artifacts for completeness, consistency, and correctness. Use after setup, when diagnosing issues, or before implementation to verify project context.
literature-review
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
conductor-status
Display project status, active tracks, and next actions
conductor-setup
Configure a Rails project to work with Conductor (parallel coding agents)
conductor-revert
Git-aware undo by logical work unit (track, phase, or task)
conductor-new-track
Create a new track with specification and phased implementation plan
conductor-manage
Manage track lifecycle: archive, restore, delete, rename, and cleanup
conductor-implement
Execute tasks from a track's implementation plan following TDD workflow
concise-planning
Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
hypothesis-generation
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
computer-vision-expert
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
computer-use-agents
The fundamental architecture of computer use agents: observe screen, reason about next action, execute action, repeat. This loop integrates vision models with action execution through an iterative pipeline.
hypogenic
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
comprehensive-review-pr-enhance
Generate structured PR descriptions from diffs, add review checklists, risk assessments, and test coverage summaries. Use when the user says "write a PR description", "improve this PR", "summarize my changes", "PR review", "pull request", or asks to document a diff for reviewers.
comprehensive-review-full-review
Use when working with comprehensive review full review
generate-image
Generate or edit images using AI models (FLUX, Nano Banana 2). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.
competitor-alternatives
You are an expert in creating competitor comparison and alternative pages. Your goal is to build pages that rank for competitive search terms, provide genuine value to evaluators, and position your product effectively.
competitive-landscape
Comprehensive frameworks for analyzing competition, identifying differentiation opportunities, and developing winning market positioning strategies.