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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cover-letter-writer
by jrhoades1Write targeted cover letters for job applications using scored match data and tailored resumes. Use this skill whenever the user wants a cover letter for a specific role — after resume tailoring, when the user says "write a cover letter," "draft a cover letter for this job," "I need a cover letter," "write the letter," or when proceeding through the application workflow after resume-tailor completes. Also trigger when the user says "next step" or "let's keep going" after a resume has been tailored. This skill reads the application folder (metadata.json, tailored resume, job description) and produces a concise, formal cover letter that addresses gaps and reinforces strengths. Outputs .docx, .pdf, and .md versions. Do NOT trigger for general writing help, thank-you notes, follow-up emails, or resignation letters — those are different workflows.
job-intake
by jrhoades1Parse job descriptions, score candidate fit, and set up application tracking folders. Use this skill whenever the user shares a job posting — pasted into chat, saved as a file, or referenced by URL — and wants to evaluate whether it's a good match. Also trigger when the user says "check this job," "is this a fit," "parse this posting," "new application," "evaluate this role," "score this job," or shares a block of text that looks like a job description (title, company, requirements, responsibilities). This skill feeds into resume-tailor and cover-letter-writer — run it first to create the application folder that downstream skills expect. Do NOT trigger for general career advice, salary research, or interview prep — those are different workflows.
job-tracker
by jrhoades1Update application status, log follow-ups, and maintain the job tracker spreadsheet. Use this skill whenever the user wants to update the status of a job application — "I applied," "I got a rejection," "they scheduled an interview," "mark it as withdrawn," "update the tracker," "log this application," "I submitted it," "any follow-ups due?" Also trigger when the user says "I'm done" or "submitted" after the resume and cover letter are ready, or asks to check on pending applications, review follow-up dates, or get a status overview of all active applications. This is the bookkeeping skill — it keeps metadata.json and tracker.xlsx in sync so nothing falls through the cracks. Do NOT trigger for evaluating new jobs (job-intake), tailoring resumes (resume-tailor), or writing cover letters (cover-letter-writer).
research
by jrhoades1Deep research on any topic — competitors, markets, technologies, people, or companies. Use when user says "research X", "look into X", "what do we know about X", or "competitive analysis on X".
resume-tailor
by jrhoades1Tailor resumes to specific job descriptions using scored application data from job-intake. Use this skill whenever the user wants to customize their resume for a specific role — after a job has been evaluated, when the user says "tailor my resume," "customize my resume for this job," "adjust my resume," "make my resume match this posting," or when the user says "yes" or "let's apply" after a job-intake summary. This skill reads the application folder created by job-intake (metadata.json with match scores, gaps, and keywords) and produces a tailored resume that emphasizes the candidate's strongest matches and addresses gaps through strategic positioning. Outputs .docx, .pdf, and .md versions. Do NOT trigger for writing resumes from scratch (that's a different workflow), general career advice, or cover letters (that's cover-letter-writer).
communication-profiles
by jrhoades1Build and maintain communication profiles for key stakeholders on any project. Use when drafting emails, follow-ups, or any outbound communication to ensure tone matching. Also use when onboarding a new project to capture stakeholder communication styles from existing email chains. Trigger when the user mentions "communication profile," "tone profile," "how does [person] communicate," "draft an email to [person]," "match their tone," or "build a profile for."
llm-council
by jrhoades1Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. 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", "I can't decide", "I'm torn between". 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, and context that suggests they want it pressure-tested from multiple angles.
csr-training
by jrhoades1CSR training and onboarding for American Leak Detection covering call scripts, pricing, scheduling, and service descriptions.
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.