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...
onboard-new-user
by codingthefuturewithaiDrives end-to-end onboarding for the gh-repo-code-intelligence repository — verifies prerequisites, installs and registers the required MCP servers (code-understanding, mermaid_image_generator, optionally Conduit for Confluence), configures permissions, builds the user's first config.json, and hands off the analysis run command. Trigger this skill whenever the user is working inside the gh-repo-code-intelligence repo and says any of "help me get started", "I just cloned this", "first time setting up", "how do I run this", "I want to analyze a repo", "help me onboard", "how do I use this tool", or describes a setup symptom (missing MCP servers, no config.json, blank state.json, "what do I do first"). Also trigger when the user says they want to wire up Confluence publishing for the first time. Do NOT trigger if the user is clearly past first-run setup and is asking about a specific phase like editing config.json for a new run, debugging a failed analysis, or interpreting output — those are different tasks.
teamcraft-glgdstakeholder-update
by codingthefuturewithaiGenerate an audience-appropriate stakeholder update by pulling live GitLab sprint data and Drive artifacts. Produces human-facing communication from AI-native project artifacts — status reports, client updates, executive summaries, engineering updates. Works in Claude Cowork and Claude Code.
teamcraft-jcgstakeholder-update
by codingthefuturewithaiGenerate an audience-appropriate stakeholder update by pulling live Jira sprint data, GitHub PR/pipeline status, and Confluence artifacts. Produces human-facing communication from AI-native project artifacts — status reports, client updates, executive summaries, engineering updates. Works in Claude Cowork and Claude Code.
teamcraft-jcgdefine-roadmap
by codingthefuturewithaiProduce a multi-sprint product roadmap in Confluence — the planning layer between a baselined PRD and individual sprint planning. Organises what gets built in what order across sprints, with priorities, dependencies, and capacity rationale. Feeds directly into plan-sprint.
teamcraft-jcgfast-path
by codingthefuturewithaiCreate, plan, and implement urgent work through a streamlined path that preserves quality gates while removing sprint planning ceremony. For production bugs, critical fixes, and emergency work that cannot wait for the next sprint planning session.
teamcraft-jcglearn-teamcraft
by codingthefuturewithaiLearn the TeamCraft plugin — full overview or role-specific deep dive. Teaches the workflow, the skills available to your role, how artifacts flow between roles, and where you fit in the team's process. No environment access needed. Run this before onboard, or any time you want to understand the plugin better.
teamcraft-jcgonboard
by codingthefuturewithaiOrient a new team member to their environment through the TeamCraft lens. Reads Jira projects, GitHub repos, open issues, and Confluence project artifacts — then presents the current state and explains how TeamCraft applies to their role. Advisory only. Never starts work.
teamcraft-jcgpr-review
by codingthefuturewithaiConduct a pull request review without leaving Claude Code. Access the full PR diff, implementation context, and discussion thread. Leave review comments, respond to threads, approve, and merge — all without opening the GitHub UI.
teamcraft-jcgproject-health
by codingthefuturewithaiOn-demand interpreted view of sprint progress, velocity, quality signals, and defect trends. Available to any role at any time — returns insight, not raw data. Works without codebase access.
teamcraft-jcgteamcraft-setup
by codingthefuturewithaiFirst-time setup for the Teamcraft JCG plugin. Verifies that the Atlassian MCP server (sooperset/mcp-atlassian) and GitHub CLI are configured and working, guides through setup if not, and recommends companion plugins. Run this before any other Teamcraft skill. Works in Claude Code and Claude Cowork.
teamcraft-glgdlearn-teamcraft
by codingthefuturewithaiLearn the TeamCraft plugin — full overview or role-specific deep dive. Teaches the workflow, the skills available to your role, how artifacts flow between roles, and where you fit in the team's process. No environment access needed. Run this before onboard, or any time you want to understand the plugin better.
ctf-claude-code-primitivesctfai-brand
by codingthefuturewithaiApply Coding the Future with AI brand styling. Use this skill when the user asks to create branded content, apply brand colors, style documents with CTFAI branding, create presentations/websites/themes with company branding, or generate branded PDFs with fillable fields.
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.