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...
analytics
by getsentryInstrument and discover analytics events in Sentry's frontend UI. Use when adding tracking to buttons, pages, modals, or custom interactions, when defining new analytics events, when searching for existing events, when auditing analytics coverage for a feature, or when answering questions about how users interact with a feature. Trigger on "add analytics", "track event", "instrument analytics", "analytics event", "track click", "track page view", "add tracking", "what events exist for", "audit analytics", "how many people", "how many users", "are people using", "is anyone clicking", "usage of", "who is using".
cell-architecture
by getsentryReference and active migration guide for Sentry's cell architecture. Explains what cells and localities are and why they're different, how requests reach cells via Synapse API routing, ingestion routing, and the control silo gateway, and how to safely query cross-cell data without silently missing results. The migration section covers how to do migration work: draining the URL_NAME_TO_ACTION registry in test_urls.py to zero (with a recipe for each action type), rolling deploy safety and the two-phase pattern required by independent sentry/getsentry deploys, and the region -> cell rename including what not to rename (DB columns, AWS refs, uptime regions, billing address). Also documents known issues with proposed fixes: integration TeamLinkageView routing, Jira cross-cell fan-out, and relocation endpoint routing.
react-component-documentation
by getsentryCreate or update component documentation in Sentry's MDX stories format. Use when asked to "document a component", "add stories", "write component docs", "create an mdx file", "add a stories.mdx", or document a design system component. Generates structured MDX with live demos, accessibility guidance, and auto-generated API docs from TypeScript types.
cmdk-actions
by getsentryGuide for adding new actions to Sentry's Command+K palette. Use when implementing new cmdk actions, registering page-level or global actions, building async resource pickers, or adding contextual actions to a view.
design-system
by getsentryGuide for using Sentry's layout and text primitives. Use when implementing UI components, layouts, or typography. Enforces use of core components over styled components.
django-models
by getsentryDesign Django ORM models for Sentry following architectural conventions for silos, replication, relocation, and foreign keys. Use when adding a new Django model, designing a model for a feature, deciding where data should live, picking a foreign key type, or refactoring an existing model's silo placement. Trigger on "add a Django model", "create a model", "design a model for X", "new database table", "store this data in the DB", "I need to track Y", "model for [feature]". Not for Pydantic models, dataclasses, ML models, or Protobuf — this is specifically for Django ORM models in the Sentry codebase.
lint-fix
by getsentryFix violations of an eslintPluginScraps rule across the codebase. Use when asked to "fix lint violations", "apply a lint rule", "fix scraps rule errors", "roll out a lint rule", "enforce a rule codebase-wide", or "fix design system lint". Covers manual fixes, autofix, batching, and codemod strategies for large-scale rollouts.
generate-migration
by getsentryGenerate Django database migrations for Sentry. Use when creating migrations, adding/removing columns or tables, adding indexes, or resolving migration conflicts.
hybrid-cloud-test-gen
by getsentryGenerate hybrid cloud tests for the Sentry codebase. Use when asked to "generate HC test", "create hybrid cloud test", "write HC test", "add HC test", "write RPC test", "test RPC service", "silo test", "cross-silo test", "outbox test", "API gateway test", or "endpoint silo test". Covers RPC service tests, API gateway tests, outbox pattern tests, and API endpoint tests with silo decorators.
generate-frontend-forms
by getsentryGuide for creating forms using Sentry's new form system. Use when implementing forms, form fields, validation, or auto-save functionality.
generate-snapshot-tests
by getsentryGenerate snapshot test files for Sentry frontend React components. Use when asked to "generate snapshot tests", "add snapshot tests", "create visual snapshots", "write snapshot tests", "add visual regression tests", or "snapshot this component". Accepts an optional component path or name via $ARGUMENTS.
hybrid-cloud-outboxes
by getsentryGuide for creating and maintaining outbox-based eventually consistent operations in Sentry. Most commonly used for cross-silo data replication, but applicable anywhere eventual consistency is needed — including single-silo deferred side effects, audit logging, and event fanout. Use when asked to "add outbox", "add outbox replication", "replicate model to control silo", "replicate model to cell", "add outbox category", "write outbox signal receiver", "debug stuck outboxes", "outbox not processing", "data not replicating", "test outbox", "migrate model to use outboxes", "backfill outbox data", "outbox coalescing", "ReplicatedCellModel", "ReplicatedControlModel", "OutboxCategory", "OutboxScope", or "outbox_runner". Covers model mixins, category registration, signal receivers, testing, backfill, and debugging workflows.
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.