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...
jira
by meainManage Jira issues using the jira CLI — lookup, create, edit, transition, assign, comment, link, and search. Triggers: /jira, "look up jira issue", "check jira ticket", "create jira issue", "update jira", "move ticket to", "assign issue", "add comment to", "what's the status of PROJ-123", any mention of a Jira issue key like PROJ-123
web-search
by meainSearch the web using lynx and DuckDuckGo. Performs text-based web searches and can dump specific website content. Only use when the built-in WebSearch tool is not available.
backlog-add
by meainLook up a URL (Teams message, Jira ticket, GitHub PR/issue, incident.io alert) and create a concise backlog entry with a [ref] link. Triggers: /backlog-add, "add this link to backlog", "backlog from link", "look up and add to backlog"
analyze-prs
by meainAnalyze my PRs across tracked repos, collect human reviewer feedback, identify recurring patterns and areas for improvement, and generate a visual HTML report. Accepts an optional time range argument (e.g., "last month", "last 2 weeks", "march") defaulting to last month.
esql
by meainQuery Elasticsearch/Kibana using ES|QL via the Kibana async search API. Requires an initial curl command from the user to extract session credentials. Triggers: /esql, 'query elastic', 'search logs', 'check elastic logs', 'run esql', 'elasticsearch query', 'check kibana'
explain-flow
by meainExplain how code flows with concrete input/output examples, ASCII diagrams, and before/after tables. Works on PRs, functions, modules, or any code path. Triggers: /explain-flow, "explain this PR", "explain this function", "explain this module", "how does this flow", "walk me through", "explain the change in"
firefox
by meainControl Firefox browser on macOS: open URLs, close/switch tabs, get active tab info, focus Firefox, send keystrokes. Use when asked to open a link in Firefox, close a tab, switch to a tab, find what's open in Firefox, or automate any browser action.
interview-report
by meainAnalyze a URL shortener take-home interview submission and generate a detailed report card. Reads all code from a zip file or git repo, performs deep code review (bugs, architecture, testing, security, production readiness), scores across dimensions, identifies issues with actual code examples and suggested fixes, generates follow-up interview questions with good/bad answer signals, and produces an HTML + PDF report card. Triggers: /interview-report, "review this candidate", "analyze take home", "generate report card", "interview report", "assess this submission", "review assignment", "candidate report", "analyze the code", "go through this submission". Always triggered when reviewing code in an interviews/ directory.
michel
by meainRespond as Michel — draft messages, replies, or any written content in the user's voice and personality. Triggers: /michel, "write as me", "draft a reply", "respond as me", "write in my voice"
money-dashboard
by meainAnalyze a Money Manager Android .mmbak backup file and generate an HTML insights dashboard with charts, trends, and actionable financial insights. Triggers: /money-dashboard, "analyze my money", "money dashboard", "analyze mmbak", "financial dashboard"
my-weekly-report
by meainGenerate a concise weekly status update in team format by pulling data from the current Jira sprint (tickets assigned to me), backlog, and recent activity. Outputs bullet points covering "What I worked on" (completed/in-progress this sprint), "What's next" (To Do in current sprint), and standard blockers/leaves fields. When asked for last week's report, covers completed items from last week. Upcoming items are based on the current sprint's To Do tickets. Triggers: /my-weekly-report, "my weekly update", "my weekly status", "generate my weekly", "write my weekly", "my weekly report"
recall
by meainSearch past Claude Code and Codex sessions. Triggers: /recall, "search old conversations", "find a past session", "recall a previous conversation", "search session history", "what did we discuss", "remember when we"
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.