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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andrew-kane-gem-writer
by jikig-aiThis skill should be used when writing Ruby gems following Andrew Kane's patterns. It applies when creating new gems, refactoring existing gems, or designing gem APIs with clean, minimal, production-ready code.
cro
by jikig-aiOrchestrates the sales domain -- assesses revenue posture, recommends pipeline actions, and delegates to sales specialist agents. Use individual sales agents for focused tasks; use this agent for cross-cutting sales strategy and multi-agent coordination. Use cfo for company-level financial analysis and budgeting.
postmerge
by jikig-aiThis skill should be used when verifying a merged PR deployed correctly and production is healthy.
dhh-rails-reviewer
by jikig-aiUse this agent when you need a brutally honest Rails code review from the perspective of David Heinemeier Hansson. Use kieran-rails-reviewer for strict convention and taste checks; use this agent for opinionated architectural critique.
dhh-rails-style
by jikig-aiThis skill should be used when writing Ruby and Rails code in DHH's 37signals style: REST purity, fat models, thin controllers, Current attributes, Hotwire patterns.
financial-reporter
by jikig-aiUse this agent when you need to generate financial summaries, cash flow statements, periodic financial reports, or investor-ready financial overviews. Use budget-analyst for budget planning; use revenue-analyst for forecasting; use this agent for synthesizing financial data into reports. Use cfo for cross-cutting financial strategy.
ops-advisor
by jikig-aiUse this agent when you need to track operational expenses, manage domain registrations, or get hosting recommendations. Use ops-research for live research and provider comparison; use ops-provisioner for account setup; use cfo for financial analysis and budgeting; use this agent for reading and updating the expense ledger.
fact-checker
by jikig-aiVerifies factual claims, statistics, and attributed quotes in drafts by fetching cited URLs and confirming source support. Use copywriter for marketing copy; use content-writer for blog articles; use this agent for citation verification.
every-style-editor
by jikig-aiThis skill should be used when reviewing or editing copy for adherence to Every's style guide. It provides systematic line-by-line review for grammar, punctuation, mechanics, and style compliance.
ops-provisioner
by jikig-aiUse this agent when you need to set up a new SaaS tool account, purchase a plan, configure the tool, and verify it works. Use ops-research for evaluating alternatives; use ops-advisor for the expense ledger; use coo for cross-cutting operations strategy.
ops-research
by jikig-aiUse this agent when you need to research domains, hosting providers, tools, or find cost optimization opportunities. Use ops-advisor for the expense ledger; use ops-provisioner for account setup; use coo for cross-cutting operations strategy; use this agent for live research and price comparison.
deal-architect
by jikig-aiUse this agent when you need to create proposals, SOWs, competitive battlecards, objection-handling playbooks, or deal negotiation frameworks. Use pricing-strategist for product pricing and tier design; use this agent for deal-level negotiation and sales collateral. Use cro for cross-cutting sales strategy.
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.