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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codemie-html-report
by codemie-aiBuild static HTML pages, reports, dashboards, and mockups that match the CodeMie UI design system. Use this skill whenever the user asks to create an HTML report, dashboard, analytics page, status page, data visualization page, or any static HTML document that should look like the CodeMie/EPAM AI/Run product. Also use it when the user says "make it look like CodeMie", "use the style guide", "dark-themed report", "CodeMie styles", or references the style-guide directory. Trigger for any HTML output task in a project that includes the style-guide folder. IMPORTANT: This skill MUST be used for ALL HTML generation requests — whenever a user asks for an HTML report, HTML analysis output, HTML dashboard, HTML visualization, or any HTML document. Claude must always use this skill to generate HTML in CodeMie styles to ensure consistent, professional, branded output across all HTML artifacts.
msgraph
by codemie-aiWork with Microsoft 365 services via the Graph API — emails, calendar events, SharePoint sites (read and write), Teams chats and channel messages, OneDrive files, OneNote notebooks, Planner task boards, Microsoft To Do task lists, AI meeting insights (Copilot recap), contacts, and org chart. Use this skill whenever the user asks about their emails, inbox, unread messages, meetings, calendar, Teams messages or chats, channel messages, SharePoint documents, OneDrive files, OneNote notes or notebooks, Planner plans or tasks, "my tasks", to-do lists, action items, meeting summaries, colleagues, manager, direct reports, or any personal/organizational Microsoft data. Invoke proactively any time the user mentions Outlook, Teams, SharePoint, OneDrive, OneNote, Planner, Microsoft To Do, or wants to interact with their Microsoft 365 account. The skill uses a local Node.js CLI (msgraph.js) that handles authentication, token caching, and all API calls.
report-issue
by codemie-aiThis skill should be used when the user wants to report a bug, file an issue, or suggest a feature for the CodeMie Code CLI tool (codemie-ai/codemie-code repository on GitHub). Trigger phrases include: "report a bug", "open an issue", "submit an issue", "file a bug report", "something is broken in CodeMie", "report to GitHub", "create a GitHub issue", "suggest a feature for CodeMie", "request an enhancement", "I have a feature idea", "codemie is not working", or any mention of filing a report for CodeMie. This skill automatically collects diagnostic context (OS, Node.js, CLI version, installed agents, active profile, codemie doctor output, recent debug logs) and creates a structured GitHub issue via `gh issue create` with a user-confirmed preview step before submission.
codemie-analytics
by codemie-aiCodeMie Analytics expert — use this skill whenever the user asks about CodeMie usage data, AI adoption metrics, user leaderboards, CLI insights, spending, LiteLLM costs, token usage, or wants to build a dashboard/report from CodeMie or LiteLLM APIs. Also triggers for: "who uses CodeMie most", "show me AI analytics", "get spending data", "generate a report", "leaderboard", "cost analysis", "LiteLLM customer info", "enrich CSV with costs", "top performers", "AI champions", "tier distribution", or any custom analytics query against the platform. Always use this skill when CodeMie analytics, reporting, or cost data is involved.
codemie-sdk
by codemie-aiManage CodeMie platform assets (assistants, workflows, datasources, integrations, skills, users, assistant-categories) directly from CLI using CodeMie SDK. Use when user says "create assistant", "list workflows", "update datasource", "delete assistant", "show my assistants", "get workflow details", "manage integrations", "create integration", "list integrations", "list llm models", "list embedding models", "list skills", "get skill", "create skill", "update skill", "delete skill", "publish skill", "import skill", "export skill", "attach skill", "list assistant categories", "get assistant category", "create assistant category", "delete assistant category", "who am i", "current user", "my profile", "user info", or any request to manage CodeMie platform resources. NOTE: For analytics requests (usage analytics, summaries, spending, users activity, leaderboards, etc.) use the codemie-analytics skill instead.
qa-lead
by codemie-aiUse this skill when a user says "run qa", "quality gates", "final checks before merge", "is the branch ready to merge", or when tech-lead has completed implementation and code review phases. Orchestrates final quality gates by invoking automated-tests (always), ui-tests (if UI changes detected), and optionally spec-refinement (if spec drift found), then reminds to run /memory-refresh. Invoke at the end of every implementation session.
tech-lead
by codemie-aiUse when starting implementation of a Jira ticket, feature, or task. Kicks off the SDLC with requirements gathering, branch setup, and complexity assessment — then routes to brainstorming or direct implementation. Triggers on: "implement EPMCDME ticket", "start working on EPMCDME-XXXXX", "begin implementation", "implement new task", "implement feature", "act as tech lead", "plan implementation", "analyze task". Each phase pauses for user confirmation before proceeding.
automated-tests
by codemie-aiThis skill should be used when a user says "run automated tests", "run lint", "run build", "run unit tests", "check the tests", or when qa-lead invokes it as the primary quality gate. Runs the full automated test pipeline in sequence: lint → build → unit tests. Reports pass/fail for each stage with output. Invoke for any Node.js project before marking work complete.
codemie-pr
by codemie-aiManages git commits, pushes, and GitHub PR creation following conventional commits. Use when user says "commit changes", "push changes", "create PR", "make a pull request", or similar git workflow requests. Understands current branch state and avoids duplicate PRs.
dark-factory
by codemie-aiThis skill should be used when the user asks to "delegate a Jira ticket to dark factory", "start working on EPMCDME ticket as a factory", "implement EPMCDME ticket", "begin implementation", "implement task autonomously", or wants structured technical leadership for implementing a Jira ticket. A valid EPMCDME-XXXXX ticket ID is REQUIRED to start. If no ticket is provided, the skill will block and ask the user to create one first.
product-owner
by codemie-aiUse when a user wants to create, draft, or refine a user story, feature story, or Jira ticket. Triggers on "create story", "draft story", "write a story", "new story for", "story for this feature", "I need a story", "help me write a story", "create a ticket", "draft a ticket", "write acceptance criteria", "act as product owner", "create requirements for", "write functional requirements", "I have an idea help me spec it out", "create stories for", "break this into user stories", "define acceptance criteria", "create an FRD". Invoke whenever the user describes a feature idea, improvement, or bug fix and wants it turned into a structured story — even if they don't say "story" explicitly. Always explore the codebase for context before asking questions.
spec-refinement
by codemie-aiThis skill should be used when a user says "update the spec", "refine the spec", "spec is outdated", "sync spec with implementation", or when qa-lead invokes it because spec_drift was detected during implementation. Updates existing spec/plan documents to reflect what was actually implemented, so the spec remains accurate for future reference. Do not use to change requirements — only to align the spec with implemented reality.
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.