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...
sjv-playwright-visual-capture
by osdevingUse when capturing or regenerating System Journey Viewer visual demos and screenshots with Playwright, including local dev server setup, deterministic showcase loading, presentation/journey playback automation, and media artifact validation for README/docs/help gallery.
sjv-docs-sync
by osdevingUse when syncing System Journey Viewer documentation after feature or workflow changes, covering WORKLOG, AI_STATE, SJV Script spec, help content, README, and naming consistency (especially SJV Script terminology and English UI/docs text).
sjv-export-pipeline-validation
by osdevingUse when changing System Journey Viewer export behavior (SVG, PNG, PDF, GIF, MP4, animated SVG) or presentation/player capture flows, including validation of export tests, capture state restoration, format-specific constraints, and demo-gallery impacts.
sjv-file-atlas-and-header-conventions
by osdevingUse when reorganizing frontend files in apps/web, classifying components vs non-components, applying folder/naming conventions, and adding top-of-file Purpose headers for faster human/AI navigation.
sjv-local-persistence-migrations
by osdevingUse when changing System Journey Viewer local persistence shape or semantics (localStorage UI preferences, layout persistence, snapshots, recents), including backward compatibility, storage key versioning/migration logic, tests, and user-facing behavior safeguards.
sjv-pr-and-merge-gh
by osdevingUse when finalizing a System Journey Viewer task with the repository's tmp/ai branch workflow, validations, GitHub CLI pull request creation, check monitoring, and rebase merge with branch deletion.
sjv-script-change-with-roundtrip-tests
by osdevingUse when changing SJV Script syntax or semantics in System Journey Viewer, including parser, import/export, editor sync, conversion, metadata, drilldown, notes, and documentation/showcase alignment with roundtrip test coverage.
sjv-showcase-tutorial-curation
by osdevingUse when updating the System Journey Viewer showcase or tutorial workspaces, including EN/PT variants, notes, drilldowns, semantic edge/journey naming, demo coverage of features, and corresponding tests/help menu entry behavior.
sjv-theme-and-palette-accessibility
by osdevingUse when tuning System Journey Viewer light/dark themes, node/text color presets, palette defaults, and visual contrast/readability in canvas, inspector, showcase/tutorial examples, or desktop shell UI.
sjv-ui-layout-regression-fix
by osdevingUse when fixing System Journey Viewer frontend layout regressions in apps/web, especially menubar, toolbar rows, topbar sizing, dock/workbench clipping, overflow, wrapping, and viewport-dependent UI shell issues.
livro-manim-trackers
by osdevingAplica tecnicas de ValueTracker e always_redraw para cenas vivas. Use quando a animacao precisar reagir continuamente a um valor, como tempo, posicao, tangente ou area.
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.