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.
Querying local SQLite index...
bb-prd
by 05shygo生成芯片/IP 产品需求文档（PRD），从市场需求和技术约束输出专业 PRD 规范。Generate Product Requirements Document for chip/IP design projects. Trigger: /bb-prd, PRD生成, 产品需求文档, 芯片立项, IP需求, chiplet specification
bb-challenge-code
by 05shygo通用对抗性代码/文档评审，按可选角色 ruthless/linus/balanced 输出犀利质询。供任何 agent 在重大改动前压力测试。触发场景：(1) agent 自评；(2) 用户显式 /bb-challenge-code。
bb-check-cdc
by 05shygo基于 AST 检查 CDC/RDC 违例：对比 MAS clock_domains 找跨域信号，检查是否被 2ff-sync CBB 保护。触发场景：(1) bba-guru-synthesis 综合前；(2) 显式 /bb-check-cdc。
bb-close-issue
by 05shygoBabel internal issue protocol — close a handoff after downstream agent picked it up or user signed off. Moves designs/<name>/.handoff/<label>.md to .handoff/closed/ (using mv per CLAUDE.md), appends to handoff_log.jsonl.
bb-collect-coverage
by 05shygo解析 Verilator coverage.dat + sim log，输出 functional + code coverage 数值，判断是否 100% 达标。触发场景：(1) bb-invoke-verilator 后；(2) 每次回归后；(3) 显式 /bb-collect-coverage。
bb-create-sdc
by 05shygo从 MAS（clock_domains / io_timing / path_exceptions）派生 Synopsys SDC 时序约束文件，OpenSTA 语法校验。触发场景：(1) bba-guru-synthesis 综合前；(2) post-PD timing fail 修订约束；(3) 显式 /bb-create-sdc。
bb-create-verif-plan
by 05shygo把 mas/verif_plan_seed.md 扩展为完整验证计划 markdown（功能检查点、覆盖率目标、边界、随机约束、test case 清单）。触发场景：(1) bba-guru-verification 启动时；(2) 显式 /bb-create-verif-plan。
bb-gate-pd-quality
by 05shygoPD signoff 质量门禁：DRC 0 violation + LVS match + post-PD timing 所有 corner WNS≥0 + GDSII 文件存在。通过即完成 signoff。触发场景：(1) bba-guru-pd 完成 GDS export；(2) 显式 /bb-gate-pd-quality。
bb-gate-test-quality
by 05shygo验证质量门禁（v1.3 加严）：functional 100% + line/branch/toggle 100% + assertions 全 pass。通过才允许 ready-for-synth。触发场景：(1) bba-guru-verification 跑完回归；(2) 显式 /bb-gate-test-quality。
bb-generate-tb
by 05shygo根据 MAS + verification_plan + RTL 接口描述生成 SystemVerilog UVM 或 cocotb 测试平台与 test cases 清单。触发场景：(1) bba-guru-verification 在 plan 完成后；(2) 补充 corner case TB；(3) 显式 /bb-generate-tb。
bb-invoke-abc
by 05shygo**[DEPRECATED in v1.3 — common path uses bb-invoke-yosys' embedded ABC]** 独立调用 ABC（Berkeley Logic Synthesis）做逻辑优化，仅 `mapping_effort=high` 等高级调优场景使用。触发场景：(1) bb-invoke-yosys `chain_to_abc=true` 时；(2) 显式 /bb-invoke-abc 调试。
bb-invoke-netgen
by 05shygo调用 Netgen 1.5.275 做 LVS 对比：综合网表 vs Magic 提取的 SPICE，必须 match 才能 signoff。触发场景：(1) bba-guru-pd extract 后做 LVS；(2) 显式 /bb-invoke-netgen。
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.