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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agentic-qa-onboard
by upex-galaxyWalks new users through this repo's QA flow — Playwright + KATA + Allure + Xray stack, Jira QA workflow (Backlog → Shift-Left QA → Estimation → Ready For Dev → Ready For QA → In Testing → Tested → Closed), /shift-left-testing for pre-sprint AC refinement on backlog Stories, /sprint-testing for in-sprint manual QA, /test-documentation for TMS test cases, /test-automation for KATA-compliant E2E/API tests, /regression-testing for CI suite execution, /framework-development for boilerplate evolution, MCPs available (Context7, Tavily, Atlassian, Playwright, DBHub, OpenAPI, Postman), critical env vars. ALSO the front desk for anyone who is lost or wants to understand how the repo or any workflow skill works — conceptually AND visually: it explains in plain human language (suspending caveman/compressed register) and can open per-skill how-it-works presentations (English + Spanish) in the user's default browser after asking. Triggers on: `onboard me to QA`, `explain this QA repo`, `first time using this`, `primer vez
regression-testing
by upex-galaxyExecute regression test suites via CI/CD, analyze results, classify failures, and produce GO/NO-GO release decisions. Use when running regression, smoke, or sanity suites through GitHub Actions, monitoring workflow runs, downloading Allure or Playwright artifacts, classifying failures (REGRESSION vs FLAKY vs KNOWN vs ENVIRONMENT vs NEW TEST), computing pass-rate and trend metrics, deciding release readiness, generating executive quality reports, or creating regression issues. Triggers on: run regression, trigger test workflow, analyze test results, quality report, GO/NO-GO decision, release readiness, flaky tests, Allure report, smoke suite, pass rate, nightly test failure, stage 6. Do NOT use for writing new regression tests (that belongs to test-automation) or for manual fix verification (that belongs to sprint-testing).
xray-cli
by upex-galaxyXray Cloud test management via `bun xray` CLI: create/list tests, manage test executions and plans, import JUnit/Cucumber/Xray JSON results, update run statuses, back up and restore projects, link defects. Triggers on: create a test in Xray, import test results to Xray, list Xray executions, update run status, backup Xray project, restore Xray tests, link defect to run, sync tests, Xray auth login. Do NOT use for: writing automated tests (test-automation); documenting test cases or ROI analysis (test-documentation); running CI regression suites (regression-testing); browser automation (playwright-cli).
shift-left-testing
by upex-galaxyOrchestrates pre-sprint Shift-Left QA on a batch of backlog Stories. Use when the user wants to refine acceptance criteria, surface ambiguities + gaps, draft an ATP outline, and hand off to PO/Dev BEFORE the Story enters a sprint — so defects are prevented in the requirements, not detected after implementation. Triggers on: shift-left testing, shift-left these stories, groom the backlog, pre-sprint QA, refine these N stories, pre-sprint refinement batch, prepare backlog for sprint planning, run AC refinement on UPEX-100/101/102, run shift-left QA, do early-game testing, pre-sprint test planning. ALSO trigger when the user pastes a comma-separated list of Story IDs sitting in Backlog / Shift-Left QA / Estimation / Ready For Dev and asks any variant of "refine", "groom", "clean these ACs", "shift-left these". Do NOT use for: in-sprint manual QA per ticket (use /sprint-testing — entry status is Ready For QA, this skill's entry status is Backlog/Shift-Left QA), Stage 4 TMS documentation + ROI (test-documentation)
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.