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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qa-engineer
by markuplintA skill for performing code reviews and test quality checks as a QA engineer. Improves code coverage, detects test-faking code, catches swallowed exceptions, flags conditional logic in tests, promotes hardcoded assertions, and checks cross-platform/cross-runtime compatibility (Windows, Deno, Bun). Language- and framework-agnostic — works with any repository. Trigger this skill on keywords: code review, test, coverage, QA, quality check, test generation, refactoring, review, test, coverage, refactor. Always use this skill when the user requests a PR review or test improvement.
sponsors
by markuplintCheck current active GitHub Sponsors and sync README/website listings. Used for periodic checks and listing updates.
bench-triage
by markuplintTriage one nu-only fixture from tests/external/snapshots/diff/nu-only.json by reading the spec, then drive its verdict to match-error, match-clean, or nu-over by either fixing markuplint or recording an excluded-ids.json entry. The core operation of the nu-validator coverage benchmark. Use when reducing the nu-only backlog, when checking a coverage-claim ("markuplint misses X" / "over-detects Y") against the bench, or when classifying a specific fixture. Trigger keywords: nu-only, ml-only, coverage gap, bench triage, verdict, match-error, match-clean, nu-over, excluded-ids, declare nu over-detection, claim audit, audit fixture, reduce nu-only, mark-up valid per spec, spec-cited exclusion.
migrate4-5
by markuplintGuides you through migrating markuplint configuration from v4 to v5. Detects current versions, reviews the migration guide, interactively confirms breaking changes and new rules with the user, updates config files and tests. For Claude Code.
markuplint
by markuplintReference knowledge for Markuplint HTML linter. Covers violation interpretation, CLI usage, config patterns, and documentation URLs. Auto-loaded when working with HTML linting.
bench-rule-enable
by markuplintEnable an existing or newly-added markuplint rule on the nu-validator bench by editing tests/external/bench/config.ts. Covers the flat-rule case (rules: { '<name>': true }) and the severity override case (rules: { '<name>': { severity: 'error' } }) that bench-virtual-rule does not. Use when a rule exists in the registry but the relevant nu fixtures stay nu-only because the rule is not enabled in the bench config, or when a rule's default severity is warning but the spec text it mirrors is a MUST / MUST NOT. Trigger keywords: enable bench rule, bench config flat rule, severity override bench, rule not flagging on bench, warning vs error in bench, escalate severity bench, wai-aria-implicit-props bench, bench coverage missing rule.
bench-setup
by markuplintSet up the nu-validator benchmark environment — initialise the validator submodule, ensure Docker is running, populate the gitignored raw snapshots via yarn bench:update. Also covers Docker / nu-runner troubleshooting. Use when bench commands fail with "no snapshots found", "Docker daemon not reachable", port 28888 conflicts, or healthcheck timeouts; or when refreshing the bench after a long break. Trigger keywords: bench setup, bench Docker, validator submodule, no snapshots found, Docker daemon not reachable, healthcheck times out, port 28888, yarn bench:update first time, bench install, fresh clone bench.
bench-virtual-rule
by markuplintMirror a preset virtual rule (selectors under `nodeRules` in preset.html-standard.jsonc and similar) into bench/config.ts so the benchmark exercises it. Use when a new disallowed-element / similar selector lands in a preset and the matching nu fixtures remain stuck at nu-only despite the new rule existing in markuplint. Trigger keywords: virtual rule, nodeRules, disallowed-element, preset html-standard, bench config mirror, rule does not fire on bench, preset rule not exercised.
bench-xref
by markuplintSync benchmark cross-reference blocks onto GitHub Issue bodies via `yarn bench:xref`, manage `tests/external/bench/issue-xref.config.ts` mappings, and run the pre-release checklist. Use when adding / removing / updating a primary or secondary mapping, when an issue closes upstream, when the xref CLI's output disagrees with what's on the issue body, or before cutting a release that touches a rule the benchmark covers. Trigger keywords: bench-xref, issue-xref, xref CLI, GitHub issue body, primary mapping, secondary mapping, umbrella block, pre-release checklist, bench-xref-audit workflow, marker version.
product-manager
by markuplintAnalyze, review, and generate documentation for any repository from a Product Manager (PdM) perspective. Language- and framework-agnostic — works with any repository. Use this skill whenever you need to: understand repository structure, read and navigate code, analyze dependencies, generate or review READMEs and documentation, summarize the tech stack, evaluate architecture, assess operational impact of new features, or review PRs. Responds to requests like "what's going on in this repo?", "write a README", "review this change", "is this maintainable?", "what do you think of this PR?". If the task involves repository analysis, review, or documentation, use this skill without hesitation.
markuplint-setup
by markuplintSet up Markuplint in a project from scratch. Detects framework, creates config, runs initial lint, and guides the user through rule adoption with Bulk Suppressions support.
markuplint-configure
by markuplintAdd, remove, or adjust Markuplint rules for specific files or elements. Analyzes violations, proposes scope-appropriate configuration changes, and confirms with the user.
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.