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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langfuse-setup
by stevengonsalvezSet up or disable Langfuse observability for Claude Code sessions. Manages hook configuration, credential verification, and connection testing.
ainb-fleet-daemon
by stevengonsalvezLong-running watcher that scans every claude session every 5s and auto-sends `continue` to any session whose recent tmux pane buffer matches a known API-error regex (rate_limited, overloaded_error, internal_server_error, request_timeout, socket_hang_up, fetch_failed, ECONNRESET). Use this when you want unattended recovery from transient API failures across the fleet.
health-check
by stevengonsalvezCheck current session health and get recommendations for session management
swarm-agent-troubleshooting
by stevengonsalvezDiagnose and fix swarm agent spawn failures when agents don't start processing tasks
posthog-replay-analysis
by stevengonsalvezDecode and analyse PostHog session replay recordings programmatically via the API. Use when investigating a user-reported incident (crash, infinite loop, unexpected navigation) where a PostHog session replay exists, and you want to extract the URL sequence, network request timeline, console logs, or DOM state WITHOUT opening the replay UI. Especially valuable when diagnosing iOS Safari/WebKit crashes ("A problem repeatedly occurred"), request storms, or any bug where the replay UI can't be scripted. Bypasses the broken `rrvideo` npm packages with a direct decode-and-chart approach.
corpus
by stevengonsalvezBuild a long-lived, filtered SLICE of the global learnings KB and answer a question-set over it ("ask the auth subsystem", "ask the migration log"). Unlike /reflect:recall (a fresh hybrid query each time), a corpus is a saved filter snapshotted to disk — prime it once, ask many questions, reprime when the KB drifts. Use when the user wants a durable Q&A session against one filtered area of their own code history rather than a one-shot search.
explain-to-me
by stevengonsalvezProduce a self-contained, richly styled HTML explainer for any topic the user asks about. Picks the right template from a bundled set of 22 visual patterns (feature explainer, concept explainer, module map, PR review, ADR, options paper / trade-off analysis, system diagram, flow- chart, status report, slide deck, prototype, editor, etc.), fills it with real content, augments with inline diagrams via sister skills (/fireworks-tech-graph for architecture / flow / sequence diagrams, /graphify for knowledge graphs), applies a Claude-brand polish layer, and publishes to here.now at a topic-slug URL so the link is shareable immediately. Local-only output is available with --local. Use when Stevie says "/explain-to-me", "explain-to-me X", "make me an explainer for X", "give me an HTML explainer", "render this as a webpage", "ADR for X", "options paper for X", or asks for a rich visual writeup. The skill picks the template, names the choice up-front, and reaches for diagrams whenever the content shape needs them.
swarm-orchestration
by stevengonsalvezA tmux-based persistent multi-agent swarm system with file-based inter-agent messaging
plan
by stevengonsalvezCreate a detailed implementation plan
agentmail
by stevengonsalvezSpin up disposable email inboxes via myagentinbox.com for tests, signup flows, OTP/magic-link captures, and any task that needs to receive mail from a service without using a real mailbox. Provides a REST-first bash workflow (no MCP required), 24h-lifetime inboxes, polling helpers with timeout, and verification-code/magic-link extractors. Use when the user mentions "throwaway email", "disposable inbox", "test signup", "OTP capture", "magic link", "verify email flow", "/expect-test signup", or any flow where a service emails a code and the agent needs to read it back. Stores inbox metadata in a tool-neutral state file under .agents/agentmail/ so multiple inboxes survive across sessions. MCP is intentionally NOT recommended — direct REST is simpler and avoids the npx mcp-remote shim. mcporter is documented as a fallback for projects already standardised on MCP tooling.
handover
by stevengonsalvezGenerate a handover document for transferring work to another developer or spawning an async agent
resume-formatter
by stevengonsalvezProfessional resume formatting and PDF generation tool. Use this skill when: (1) Creating a new resume tailored to a specific job description (2) Converting an existing resume to the standard HTML template (3) Generating a PDF from an HTML resume (4) Updating resume content while maintaining consistent formatting Produces professional, ATS-friendly resumes with consistent blue-themed styling.
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.