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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constructive-pnpm
by constructive-ioPNPM workspace management — monorepo configuration, dist-folder publishing with makage/lerna, dependency management, deep nested imports, and anti-patterns to avoid. Use when configuring pnpm workspaces, publishing packages to npm, managing monorepo dependencies, or setting up new TypeScript packages.
graphile-postgis
by constructive-ioHow to expose cross-table PostGIS queries to the ORM/GraphQL layer without shipping GeoJSON to the client. Covers the @spatialRelation smart tag (8 operators, parametric distance), the RelationSpatial blueprint node, and the ORM `where:` shape the generated client consumes.
pgpm
by constructive-ioPostgreSQL Package Manager — deterministic, plan-driven database migrations with dependency management. Use when asked to "deploy database", "run migrations", "manage pgpm modules", "add a table", "create a function", "add a migration", "write database changes", "create a workspace", "set up pgpm", "manage dependencies", "revert a migration", "verify deployments", "tag a release", "start postgres", "run database locally", "set up database environment", "load env vars", "add an extension", "install a module", "publish pgpm module", "test database", "write integration tests", "troubleshoot pgpm", or when working with PostgreSQL package management, .control files, pgpm.plan, or SQL migration scripts.
orm-modules
by constructive-ioORM client for the modules API — provides typed CRUD operations for 56 tables and 12 custom operations
cli-modules
by constructive-ioCLI tool (csdk) for the modules API — provides CRUD commands for 56 tables and 12 custom operations
constructive-realtime
by constructive-ioSubscriptions, notifications, change_log, CursorTracker — real-time data via GraphQL subscriptions, PostGraphile live queries, and change tracking. Use when asked to 'add subscriptions', 'real-time updates', 'live queries', 'notifications', 'change_log', 'CursorTracker', 'websocket', or when working with real-time features in Constructive.
graphile-v5-minimal
by constructive-ioConfigure PostGraphile v5 default plugins and features. Use when asked to "configure default plugins", "customize base schema", "keep id as id", "configure Node/Relay features", or when setting up PostGraphile v5 for your specific needs.
graphile-v5-presets
by constructive-ioCreate and compose PostGraphile v5 presets. Use when asked to "create a preset", "configure PostGraphile", "combine presets", "disable plugins", or when setting up a new PostGraphile v5 server.
graphile-v5-debugging
by constructive-ioDebug PostGraphile v5 schema generation issues. Use when asked to "debug schema", "why is this field generated", "log inflector calls", "detect naming conflicts", "understand schema generation", or when troubleshooting PostGraphile v5 issues.
graphile-v5-plugins
by constructive-ioCreate custom PostGraphile v5 plugins using hooks. Use when asked to "create a plugin", "add custom field", "extend schema", "detect conflicts", "add metadata query", or when you need to extend PostGraphile's functionality.
graphile-v5-connection-filter
by constructive-ioConfigure connection filtering in PostGraphile v5. Use when asked to "add filtering", "configure filters", "filter by column", "disable relation filters", "enable all column filters", or when setting up the connection filter plugin.
graphile-v5-inflection
by constructive-ioCustomize GraphQL naming and inflection in PostGraphile v5. Use when asked to "customize naming", "change field names", "simplify relation names", "remove schema prefix", "fix pluralization", or when GraphQL names don't match your conventions.
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.