Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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brainstorming
by martin98-afkYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
writing-plans
by martin98-afkUse when you have a spec or requirements for a multi-step task, before touching code
node-graph
by martin98-afkUse this skill any time a node-based workflow or graph structure is involved — creating nodes, connecting ports, configuring properties, selecting anchor nodes, or analyzing component capabilities. This includes: building automation flows, data processing pipelines, or logic graphs; querying available components; inspecting node inputs/outputs; linking data streams between nodes; setting runtime parameters; selecting specific nodes to define creation context. Trigger whenever the user mentions 'node', 'component', 'workflow', 'graph', 'connect', 'select', or references building a process flow. If a node needs to be created, linked, configured, or selected, use this skill.
github-ops
by martin98-afkGitHub 仓库操作工具集，支持 Issue 评论回复、PR 创建、Issue/PR 详情查询、代码推送和 PR 创建联动。使用 Python requests 库调用 GitHub API，配合 gh CLI 工具完成分支操作。Use when 用户提到回复 GitHub Issue、创建 PR、推送并创建 PR、查看 issue 状态、GitHub 操作。
grill-with-docs
by martin98-afkGrilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
minimax-image-understanding
by martin98-afk跨平台截图分析工具，基于 MiniMax 多模态 API。支持 macOS (screencapture) 和 Windows (PowerShell) 截图，自动完成截图→Base64编码→API调用全流程。适用于：错误信息分析、代码解读、UI设计分析、文字提取、图表数据解读等场景。
session-summary
by martin98-afkQuery and summarize Drifox software session history. Supports querying sessions within specified date range, auto-parsing conversation content and generating summaries. Trigger scenarios: (1) User asks to query history sessions, (2) Summarize daily conversations, (3) Analyze session records, (4) Export conversation history.
setup-matt-pocock-skills
by martin98-afkSets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.
skill-creator
by martin98-afk根据参考技能和用户需求，自动生成新的 Agent 技能。分析优秀技能的架构模式，提取可复用的设计逻辑，产出符合规范的技能代码。使用场景：用户想创建一个能解决特定领域问题的新技能，或需要从参考案例中提取模式生成技能。
to-issues
by martin98-afk自动分析代码问题并提交规范化的 GitHub Issues。遵循统一格式（Summary/Environment/Description/Impact/Steps/Expected/Actual/Proposed Fix），末尾自动添加智能体生成标记。无需用户交互，直接提交。
using-superpowers
by martin98-afkUse when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
agent-canvas-designer
by martin98-afkAI 驱动的智能体编排画布设计器。通过对话理解需求，AI 自动生成画布配置， 通过 Playwright MCP 自动操控浏览器进行可视化验证和迭代。 无需用户手动拖拽编辑。内置 JSON 校验和自动备份防损坏。 Use when 用户说"设计智能体"、"画布设计"、"编排流程"、 "workflow design"、"生成画布JSON"、"生成配置"。
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.