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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qa-report
by pedronauckPlans real-user QA deliverables: personas, journey maps, exploratory charters, persona/journey/tour/CFR test cases, regression suites, Figma validation checks, automation intent, and user-impact bug reports. Writes artifacts under <qa-output-path>/qa/ for qa-execution to consume. Use when planning QA before execution, documenting journey-driven test strategy, marking flows that need E2E follow-up, or filing structured bug reports. Do not use for live execution, AI implementation audits, CI gate ownership, or technical integration/security/performance suites; use qa-execution or agent-output-audit instead.
qa-execution
by pedronauckExecutes real-user QA sessions through public interfaces using personas, journeys, exploratory charters, test tours, edge-case probes, CFR checks, and browser evidence. Reads qa-report artifacts from <qa-output-path>/qa/ when present, captures issues/screenshots/reports under the same output tree, and classifies bugs by user impact. Use when validating a release candidate, migration, refactor, or user-facing change against production-like behavior. Do not use for AI implementation audits, task-status reconciliation, CI gate runs, integration/security/performance templates, or flaky-test triage; use agent-output-audit for those.
yc-apply
by pedronauckGuides a founder through the full Y Combinator batch application end-to-end. A 10-phase workflow that captures the live YC form, profiles the founders, stress-tests the idea via an embedded grill loop, runs a mandatory 5-agent parallel external research pass on the startup, drafts every form field with anti-pattern and accepted-example checks, produces founder-video bullet notes (no script), runs a final adversarial gate, generates paste-ready submission answers, unlocks an interview-prep simulator after invite, and supports reapplicant delta tracking and post-decision post-mortems. Writes a documented markdown trail under a user-chosen workspace. Use when a founder wants to prepare a YC batch application, build their founder video, drill mock YC interview questions, or reapply with delta evidence. Don't use for pitch-deck design unrelated to YC, generic startup advice without applying, or post-funding work.
xstate
by pedronauckComprehensive guide for XState v5 ecosystem including state machines, actors, @xstate/store, and TanStack Query integration. Use when implementing state machines, event-driven stores, client state management, or integrating XState with React and TanStack Query for data fetching orchestration.
electron-dev
by pedronauckExpert guide for Electron development with Electron Vite and Electron Builder. Use when developing Electron applications, working with main/renderer processes, IPC communication, preload scripts, security configuration, native module handling, or build/distribution setup.
electron-builder
by pedronauckComprehensive guide for electron-builder (v26.x) packaging, code signing, auto-updates, and release workflows. Use when: (1) configuring electron-builder builds (electron-builder.yml or config.js/ts), (2) setting up macOS/Windows code signing or notarization, (3) implementing auto-updates with electron-updater, (4) publishing to GitHub Releases, S3, or generic servers, (5) configuring platform targets (NSIS, DMG, AppImage, Snap, PKG, MSI), (6) working with build hooks (beforePack, afterSign, afterAllArtifactBuild), or (7) using the programmatic API. Triggers on: electron-builder, electron-updater, code signing, notarize, NSIS, DMG, AppImage, auto-update, publish releases, build hooks, electron packaging, electron distribution.
electron-release
by pedronauckExpert guide for Electron production builds, code signing, notarization, auto-updates, and release workflows. Use when building, packaging, or releasing Electron applications, configuring electron-builder, setting up CI/CD pipelines for desktop app distribution, or implementing auto-update mechanisms.
argocd-expert
by pedronauckExpert-level ArgoCD GitOps deployment, application management, sync strategies, and production operations
centrifugo
by pedronauckCentrifugo real-time messaging server expert for WebSocket PUB/SUB, channel management, JWT authentication, event proxying, and horizontal scaling with Redis/NATS. Use when: centrifugo, centrifugal, real-time messaging, websocket pubsub, channel subscriptions, real-time notifications, live updates, presence, history recovery, server-sent events integration, real-time transport layer. Do not use for: general WebSocket programming without Centrifugo, Socket.IO, Pusher SDK, or other real-time frameworks.
devops-engineer
by pedronauckCreates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
es-toolkit
by pedronauckUse when implementing utility functions, array operations, object manipulation, or string operations. ALWAYS use es-toolkit instead of custom implementations or native methods.
evolution-api
by pedronauckEvolution API integration for WhatsApp messaging, instance management, webhooks, and chatbot orchestration. Use when: (1) Creating or managing WhatsApp instances via Evolution API, (2) Sending messages (text, media, audio, lists, buttons, reactions), (3) Configuring webhooks or event listeners, (4) Managing groups or contacts, (5) Integrating with Typebot, Chatwoot, Dify, or OpenAI through Evolution API. Triggers on: evolution-api, evolution api, whatsapp api, baileys, whatsapp integration, send whatsapp, whatsapp webhook.
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.