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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chip-diagram-generator
by zhaixin244-wqUse when generating chip module diagrams (block diagrams, timing diagrams, FSM diagrams). Triggers on '框图', '时序图', '状态机图', 'diagram', '架构图', '画图', '画一个'. Generates module block diagrams, timing diagrams and state machine diagrams. Primary format: D2→PNG for block/FSM diagrams, Wavedrom→PNG for timing diagrams.
chip-png-interface-gen
by zhaixin244-wqUse when generating interface port PNG diagrams from Verilog module declarations. Triggers on '接口图', '端口图', 'interface gen', 'port diagram', '接口PNG', 'module snapshot'. Generates module_snapshot style port PNGs with inputs on left, outputs on right, signal names with width and direction arrows.
vf-pipeline
by zhaixin244-wqUse this skill to start or resume the VeriFlow RTL hardware design pipeline (architect to synth). Trigger this when the user asks to "run the RTL flow", "design hardware", or "start the pipeline". Pass the project directory path as the argument.
chip-doc-structurer
by zhaixin244-wqUse when designing microarchitecture document structure. Triggers on '文档结构', '章节设计', 'doc structure', '文档模板', '内容权重', '文档组织'. Designs chapter structure and content weight allocation for microarchitecture specifications.
chip-impl-input-triage
by zhaixin244-wqUse when checking RTL implementation input completeness before coding. Triggers on '输入确认', 'input triage', '输入检查', '文档完整性', '输入分类', 'input check'. Checks microarch docs, coding style, CBB list completeness and selects execution path.
chip-impl-module-structure
by zhaixin244-wqUse when planning RTL module structure from microarchitecture docs. Triggers on '模块结构', '端口列表', '子模块划分', '文件清单', 'module structure', 'port list'. Extracts ports, submodule partitioning and file list from microarch docs.
chip-impl-quality-gate
by zhaixin244-wqUse when running lint checks, synthesis, or quality gates on RTL code. Triggers on '综合', 'lint', 'synth', 'quality gate', '质量门禁', '门禁', 'verilator', 'yosys', '自检', 'check'. Executes lint+synth checks with auto-heal loop on failures.
chip-impl-self-check
by zhaixin244-wqUse when performing final self-check on RTL implementation before delivery. Triggers on '自检', 'self-check', 'self check', '实现自检', 'quality checklist', 'IC-01', 'IM-01', '检查清单'. Runs IC-01~39 + IM-01~08 implementation quality checklist.
chip-interface-contractor
by zhaixin244-wqUse when defining module interface contracts. Triggers on '接口契约', 'interface contract', '信号列表', '接口定义', '端口定义', '接口时序'. Defines signal list, timing, protocol behavior and SVA for module interfaces.
chip-png-d2-gen
by zhaixin244-wqUse when generating D2 architecture diagrams, data flow diagrams, or FSM state machine diagrams. Triggers on 'd2', '架构图', '框图', '状态机图', 'diagram', '数据通路图', 'generate d2', '模块连接图'. Compiles .d2 source to PNG using dagre layout.
chip-png-wavedrom-gen
by zhaixin244-wqUse when generating Wavedrom timing diagrams as PNG images. Triggers on '时序图', 'wavedrom', 'timing diagram', '波形图', '时序分析图', 'generate wavedrom'. Compiles Wavedrom JSON to PNG using wavedrom-cli.
chip-ppa-formatter
by zhaixin244-wqUse when formatting PPA specification tables. Triggers on 'PPA表', 'PPA格式', 'ppa formatter', '面积表', '功耗表', '性能表', 'PPA规格'. Outputs structured PPA specs with unit normalization and constraint expressions.
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.