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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business-requirement-testcase-generator
by 540415003Generate business-domain manual QA test cases from a user-provided business domain, PRD, TRD, design links, repository knowledge-base content, and manual-case rules. Use when the user asks to create, understand, organize, or write manual test cases for any business requirement, especially prompts like "帮我生成需求xxx的测试用例", "业务域：xxx", "阅读PRD/TRD/设计稿并输出测试用例", or "写入测试用例到Google Sheet".
errorlog-analysis
by 540415003Analyze error logs against source code repositories to determine if errors indicate code bugs. Given a file path, line number, function name, and sample log messages, reads the corresponding source code, performs root cause analysis, classifies bug severity (High/Medium/Low), and provides fix recommendations. Use when analyzing error logs, diagnosing code bugs from log output, or when the user provides error log entries with code location information.
manual-cases-to-autotest
by 540415003Convert manual QA test cases into project-standard automated tests from a required Jira ID and manual-case link, using local workspace code and local automation repositories first. Use when the user asks "根据手工用例编写自动化用例", "针对需求生成自动化用例", "根据手工用例生成自动化测试用例", or provides Jira, code branch, and manual-case link. Supports CCTV, Workforce, Asset, and other codebases; apply business-specific automation repo mapping when the business can be identified.
qa-resource-risk-gantt
by 540415003Update or create QA/resource risk warning Gantt charts for one or more business teams from Google Sheets scheduling documents. Use when the user asks to 更新QA资源风险预警甘特图, 生成业务团队资源预警甘特图, update QA risk Gantt for a team, detect QA resource risks, compare Dev提测时间 and QA测试开始时间, calculate QA Gap, add a new business team's scheduling document, or refresh the shared QA资源预警 spreadsheet.
test-case-completeness-review
by 540415003Review whether manual or automated test cases are missing scenarios. Supports two modes: lightweight review from testcase standards and historical badcases only, or full omission comparison using user-provided PRD, optional TRD, and remote branch diff against release. Use when the user asks to check case completeness, review DMS/manual/automation cases, compare cases against PRD/optional TRD/code diff, find missing test scenarios, or assess coverage before release.
test-case-generation
by 540415003根据 PRD、TRD、设计稿先生成 XMind 检查点，再生成全新测试用例，并在用户提供历史用例时生成历史结合版用例。 适用于需求评审检查点梳理、全新用例生成、历史用例复用更新、Google Sheet 或 Excel 测试用例输出。
knowledge-base-update
by 540415003根据Jira单号自动在ssc-knowledge-base中更新需求维度的知识库。 适用于需求测试完成后的知识库更新。
api-automation-write-case
by 540415003Go API automation (query/write/mixed) from a user-supplied API semantic Markdown (no live service tracing). Optional repo survey via step1 + 梳理报告模版; step2 drives full *_test.go / *_assert.go, go test with timeout retries and fix rounds, then execution summary MD. Use in Codex when the user mentions 接口自动化, 深度断言, 写 API 自动化用例, API语义文档, or asks to generate Go automation from an API semantic report.
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.