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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om-approve-merge-pr
by open-mercatoApprove (submit an approving review) and squash-merge a GitHub PR given only its number. Optionally file a follow-up issue at the same time. Use when the user says "approve and merge PR 123", "ship PR 123", "om-approve-merge 123", or gives a PR number with intent to merge.
om-create-ai-agent
by open-mercatoBuild, override, or extend a typed Open Mercato AI agent (chat or structured-object) using the unified AI framework — declare `ai-agents.ts`, register tool packs via `defineAiTool`, patch existing agents with `aiAgentExtensions`, configure agentic loop controls (`loop.stopWhen` / `loop.prepareStep` / `loop.budget` / `executionEngine`), gate mutations through the approval contract, wire ACL features, and embed `<AiChat>` into a backoffice or portal page. Works in both the monorepo (`packages/<x>/src/modules/<module>/`) and standalone projects (`apps/<app>/src/modules/<module>/` or `node_modules/@open-mercato/<package>` consumers). Triggers on "create AI agent", "add AI agent", "build AI assistant", "extend AI agent", "override AI agent", "add tool to existing agent", "wire ai-agents.ts", "add ai-tools.ts", "embed AiChat", "agent for module", "agentic loop", "configure loop budget", "tool-loop-agent".
om-eject-and-customize
by open-mercatoGuide for safely ejecting and customizing core modules. Use when a developer needs to modify a core module's behavior beyond what UMES extensions support, wants to eject a module, or is considering whether to eject vs extend. Triggers on "eject", "customize module", "modify core module", "override module", "fork module", "change built-in", "should I eject".
om-auto-upgrade-0-4-10-to-0-5-0
by open-mercatoMigrate a standalone Open Mercato app from framework 0.4.10 to 0.5.0. This release is the biggest Open Mercato release so far and bundles 250+ post-Hackathon fixes plus several important dependency upgrades, so this skill acts as the executable companion to the 0.5.0 upgrade notes. It mechanically applies the documented codemods for the 0.4.10 → 0.5.0 window — Meilisearch class rename, Stripe API-version typing, lucide-react brand-icon removals and metadata-icon safety fixes, react-markdown className wrap, cron-parser `CronExpressionParser.parse` rename, @simplewebauthn Uint8Array narrowing, react-email CLI rename, plus the Jest ESM allow-list. Runs inside the user's app, detects which patterns are actually in use, edits files in place, typechecks, and reports what was migrated and what still needs a human eye. Use when a user asks to "upgrade my Open Mercato project from 0.4.10 to 0.5.0", "bump open-mercato to 0.5.0", or "apply the 0.5.0 upgrade notes".
om-root-cause
by open-mercatoRead-only root-cause analysis for a GitHub issue. Identifies the bug's location and the minimal change surface so the next agent can implement the fix without re-exploring the repo. Outputs a short summary, the files that need to change, and the proposed approach.
om-review-prs
by open-mercatoReview all currently unreviewed open pull requests, newest first, using the auto-review-pr skill and respecting in-progress locks.
om-integration-tests
by open-mercatoRun and create QA integration tests (Playwright TypeScript), including executing the full suite, converting optional markdown scenarios, and generating new tests from specs or feature descriptions. Use when the user says "run integration tests", "test this feature", "create test for", "convert test case", "run QA tests", or "integration test".
om-integration-builder
by open-mercatoBuild integration provider packages for the Open Mercato Integration Marketplace (payment, shipping, data-sync, webhook). Scaffolds the npm package, adapter, credentials, widget injection, webhook processing, health checks, i18n, tests. Triggers on "build integration", "add provider", "integrate with stripe/paypal/dhl".
om-implement-spec
by open-mercatoImplement a specification (or specific phases) using coordinated subagents with unit tests, integration tests, docs, and code-review compliance. Tracks progress by updating the spec. Triggers on "implement spec", "implement phases", "build from spec", "code the spec".
om-followup-issue-from-pr
by open-mercatoTurn a review comment into a follow-up GitHub issue assigned to the PR author. Paste a link to a PR or a PR comment; the skill extracts the actionable ask from the comment, gathers PR context, and opens a tracking issue. Assignee is the comment's @-mention if present, otherwise the PR author. Use during code review when the user says "make a follow-up issue", "create an issue for this", "om-followup", or pastes a PR/comment link with that intent.
om-fix
by open-mercatoImplements the minimal code change identified by the root-cause step, adds regression tests, and runs the validation gate. Claims the GitHub issue at start (assignee + in-progress label + claim comment) so concurrent automation backs off. Does not commit, push, or open a PR — that is the open-pr step's job.
om-ds-guardian
by open-mercatoDesign System Guardian for Open Mercato. Use for frontend UI work, design-system compliance reviews, semantic token migration, hardcoded color or typography cleanup, DS-compliant page scaffolding, and common DS violations such as arbitrary text sizes, raw color classes, or missing shared states. Prefer this skill whenever you are building or reviewing Open Mercato UI.
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.