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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17th-code-analyzer
by SeventeenthEarthUse this skill when user asks about code quality, duplications, dead code, or optimization opportunities. Analyzes staged changes AND surrounding code using GPT-5.3-Codex. Auto-triggers on: "중복 코드", "불필요한 코드", "최적화", "code quality", "duplications", "dead code" Appropriate for: code hygiene, pre-PR quality check, refactoring opportunities. Not appropriate for: running tests, writing code, code review for correctness.
17th-dart-patterns
by SeventeenthEarthFlutter/Dart project development patterns and conventions. Covers Clean Architecture, Cubit/BLoC, Result type, Testing, DI (GetIt), gRPC, Repository patterns. Reference this skill before writing code to maintain consistent patterns.
17th-deep-discussion
by SeventeenthEarthEnable structured Claude-GPT debate for deep problem analysis. Both models bring unique perspectives to synthesize optimal solutions. GPT can read code (sandbox=read-only), Claude handles web search on request. Appropriate for: complex architectural decisions, trade-off analysis, design reviews. Not appropriate for: simple questions, implementation tasks, quick lookups.
17th-doc-checker
by SeventeenthEarthAnalyze TASK.md and code changes to identify documentation updates needed. Reviews docs/ directory for outdated or missing information. Claude performs analysis and applies updates directly.
17th-go-patterns
by SeventeenthEarthGo project development patterns and conventions. Covers Clean Architecture, Error Handling, Testing, DI (Wire), gRPC/Connect, Repository patterns. Reference this skill before writing code to maintain consistent patterns.
17th-make-triager
by SeventeenthEarthALWAYS use this skill when user requests any make command: make test, make lint, make build, make clean, make test-unit, make test-integration, make test-prepare, or any other Makefile target. This skill delegates make execution to GLM worker which runs the command, analyzes output, and creates a report file. Do NOT use for: hooks, automated checks, file-change triggered linting. Requires glm-worker MCP to be configured.
17th-pr-handler
by SeventeenthEarthUse this skill when user wants to analyze GitHub PR feedback or unresolved conversations. Analyzes validity of reviewer comments using GPT-5.3-Codex via GitHub MCP. Auto-triggers on: "PR 피드백", "unresolved", "리뷰 코멘트", "피드백 확인" Appropriate for: PR review handling, feedback validity check, reviewer response preparation. Not appropriate for: creating PRs, merging PRs, writing code directly.
17th-pr-summary
by SeventeenthEarthGenerate PR title and summary based on staged/committed changes. Analyzes git diff and commit history to produce consistent PR descriptions. Tag derived from branch name (uppercase), or [INT] for main branch.
17th-task-architect
by SeventeenthEarthAI orchestration workflow for TASK specification design. GPT-5.3-Codex xhigh leads the design, GLM-5 performs parallel codebase research. User interviews resolve ambiguities, producing final TASK.md.
17th-task-inspector
by SeventeenthEarthUse this skill for deep inspection of implementation against TASK.md using GPT-5.3-Codex. Provides thorough cross-model verification with detailed pass/fail analysis. Appropriate for: final verification, critical features, before claiming "done". Not appropriate for: quick checks (use task-validator), style review, test execution.
17th-task-validator
by SeventeenthEarthUse this skill when the user wants to validate their staged changes against a task/plan file. Analyzes git staged changes and compares them to requirements/checklist in TASK.md or specified file. Appropriate for: pre-commit review, progress checking, requirement verification. Not appropriate for: code review (style/quality), test execution, unstaged changes.
17th-test-analyzer
by SeventeenthEarthUse this skill when user asks about test coverage, test sufficiency, or whether tests cover requirements. Analyzes staged changes against task spec using GPT-5.3-Codex to find gaps in happy cases and edge cases. Auto-triggers on: "테스트 충분해?", "test coverage", "edge case", "테스트 분석" Appropriate for: test gap analysis, coverage check, pre-PR test review. Not appropriate for: running tests, writing tests, code review.
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.