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.
Querying local SQLite index...
auto-provision
by easingthemesCreate all AWS resources for AI automation agents — DynamoDB tables, SQS queue, S3 bucket, SNS topic, IAM role, Lambda functions, and API Gateway. Reads config from .ai/automation/infra.json. Idempotent — skips already-existing resources.
dx-eject
by easingthemesEject all plugin assets into the consumer repo — copies skills, agents, rules, templates, shared files, hooks, and MCP config so the project works without plugins installed. Use when a team wants to own all dx files locally instead of depending on plugins.
dx-council
by easingthemesRun any question, decision, or artifact through a council of 3 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Adapted from Karpathy's LLM Council. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'council the plan', 'council the implementation'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff. DO trigger when the user presents a genuine decision with stakes, multiple options, or an artifact they want pressure-tested from multiple angles.
dx-hub-status
by easingthemesShow status of hub dispatches — which repos are running, done, blocked, or failed. Use to check multi-repo progress. Trigger on "hub status", "dispatch status", "what's running".
dx-simple
by easingthemesApply a small AEM change (a11y label, color, spacing, copy, css-class, icon, focus trap, or other small behavior tweak) by splitting work into authoring (JCR writes) and code (file edits → PR) paths. Reads the ADO story directly — a structured ```simple``` block is recommended but optional. Trigger on "simple change", "small tweak", "apply tweak".
dx-req-tasks
by easingthemesCreate or close child Task work items under an Azure DevOps/Jira User Story. Use to break down a story into FE/BE/Authoring tasks with hour estimates, or use "close" argument to close all child tasks after development is done.
dx-pr-reviews-report
by easingthemesGenerate categorized reports for multiple PR reviews — lists reviewed PRs, generates a report for each, and posts all to ADO Wiki or Confluence. Use when you want to document all recent PR reviews, mentions "report reviews", or wants batch PR review reports.
dx-pr-commit
by easingthemesCommit changes and optionally create an ADO pull request. Handles staging, commit messages with ADO work item IDs, rebasing onto the base branch, and PR creation via ADO MCP tools. Use when the user says "commit", "create PR", "open PR", "push changes", or any variation. This is the ONLY skill for commits and PRs — always use it instead of gh CLI or manual git workflows.
dx-pr-answer
by easingthemesAnswer open comments on your ADO pull requests. Researches codebase context, drafts thoughtful replies, and posts them. Also detects and applies proposed code patches from reviewer comments. Use when someone wants to answer PR comments, respond to review feedback, handle open PR threads, or accept proposed patches.
dx-bug-verify
by easingthemesReproduce a bug using Playwright — navigate to the repro URL, follow repro steps, take screenshots, and confirm whether the bug is reproducible. Supports `before` (default), `after`, and `qa` modes. Works with Azure DevOps/Jira. Use after /dx-bug-triage, after /dx-bug-fix (with `after`), or after PR merge (with `qa`) to verify on QA environment.
dx-adapt
by easingthemesAuto-detect project type, structure, build commands, and AEM values. Updates .ai/config.yaml with project profile and substitutes real values into installed .claude/rules/. Run after /dx-init and /aem-init. Re-run anytime to refresh detected values.
dx-agent-all
by easingthemesFull pipeline from ADO story to executed code. Runs requirements, planning, execution, build, review, commit, and PR in sequence with optional human review checkpoints. Use for end-to-end story implementation.
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.