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...
diagram-design
by cathrynlaveryCreate technical and product diagrams — architecture, flowchart, sequence, state machine, ER / data model, timeline, swimlane, quadrant, nested, tree, org chart, layer stack, venn, pyramid — as standalone HTML files with inline SVG. Ships with a neutral editorial skin and a first-run gate that prompts users to customize the style guide (colors, fonts) from their own website before generating. Includes annotation-callout primitive and optional sketchy variant.
openclaw-ops
by cathrynlaveryUse when installing, configuring, troubleshooting, securing, or performing a health check on OpenClaw gateway setups — including channel integrations, exec approvals, cron jobs, agent sessions, and operational maintenance.
codex
by cathrynlaveryUse when Claude Code needs a second opinion, verification, or deeper research on technical matters. This includes researching how a library or API works, confirming implementation approaches, verifying technical assumptions, understanding complex code patterns, or getting alternative perspectives on architectural decisions. The agent leverages the Codex CLI to provide independent analysis and validation.
voice-memo-organizer
by cathrynlaveryFind, transcribe, summarize, and organize all Apple Voice Memos into a searchable archive. Batch processes hundreds of recordings locally using whisper.cpp — no API keys needed. Use when the user wants to organize, transcribe, or search their voice memos, mentions untitled recordings, or asks about finding Apple Voice Memos on macOS.
repo-atlas
by cathrynlaveryBuild a persistent context system (atlas) for any repository — generates directory maps with entrypoints, documents architecture and module boundaries, traces critical flows, catalogs external dependencies, and creates agent-ready onboarding guides. Use when asked to create a repo map, generate codebase documentation for LLM agents, set up an atlas, or create onboarding docs. Also use when asked to 'map this repo', 'document this codebase', or 'create context docs'.
spend-optimizer
by cathrynlaveryAudit subscriptions AND optimize credit card rewards. Analyzes bank CSVs to find recurring charges, categorize spend, identify which card each charge should be on, and calculate potential rewards. Use when user says "audit subscriptions", "optimize my cards", "maximize rewards", or "analyze my spending". Outputs interactive HTML with subscription management and card optimization.
problem-solver
by cathrynlaverySystematic analytical frameworks for diagnosing root causes, evaluating options, and solving complex technical or operational problems.
shopify-developer
by cathrynlaveryComprehensive Shopify development skill for theme architecture, Liquid, Admin API, Storefront API, metafields/metaobjects, checkout extensibility, Shopify Functions, webhooks, and platform limits. Use when implementing or debugging Shopify app/theme work, API integrations, checkout customizations, or data modeling. For Hydrogen/headless framework specifics, use Context7 MCP for current framework docs.
tally
by cathrynlaveryTally Forms REST API CLI for form management and automation. Use when creating/updating forms, reading submissions, exporting CSV or JSON, managing webhooks, or browsing workspaces via the Tally API.
wispr-flow
by cathrynlaveryGenerate daily, weekly, or monthly voice dictation recaps from Wispr Flow. Use when user says "what did I do today", "daily recap", "weekly recap", "monthly recap", "wispr recap", "show me my flow stats", "what apps did I use", "how much did I dictate", "what did I work on this week", or "what did I work on this month".
granola-meeting-notes
by cathrynlaveryUse when the user asks about meeting notes, transcripts, action items, project lists from meetings, meeting prep, weekly digests, or mentions Granola. Also use when asked "what did we discuss", "who was in the meeting", "what did I commit to", "what were the decisions", "recap my meetings", "who said what", or wants to search/export meeting history. Triggers on keywords like meeting, discussed, action items, transcript, recap, digest, attendees, commitments.
sendfox
by cathrynlaveryEmail marketing automation with SendFox API via agent-first CLI. Use when managing contacts and lists, viewing campaigns, setting up automated sequences, generating signup forms, or integrating SendFox with landing pages. Triggers on tasks like "add contact to newsletter," "create email list," "set up welcome sequence," "generate signup form," or any SendFox-related email automation.
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.