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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update-api-console-components
by mulesoftComplete release workflow for api-console v6 and anypoint-api-console wrapper
secure-agent
by mulesoftSecure an agent by applying a policy from the catalog. Handles multiple starting points: from an existing API Manager instance, from an agent asset in Exchange, or from scratch by publishing the agent first. Use when the user wants to secure an agent, add rate limiting, apply OAuth2, enforce IP allowlisting, or protect any agent with a policy — regardless of where they are in the setup process.
run-agent-scan-and-view-results
by mulesoftExecutes an agent scanner and views the discovered AI agents. Use when running an agent scan, checking scan status, viewing scan history, reviewing discovered agents from external platforms, or importing agents into Anypoint Exchange.
platform-assistant
by mulesoftDiscover and navigate the Anypoint Platform API developer portal to find APIs, agent skills, and schemas. Use when bootstrapping knowledge of the Anypoint Platform, onboarding to the API ecosystem, finding available APIs, understanding the portal structure, resolving URNs, looking up JTBD skills, understanding x-origin dynamic parameters, or planning multi-API workflows.
update-mule-run-config
by mulesoftCall use_skill as your FIRST and ONLY action when the user asks to EDIT, UPDATE, MODIFY, or CHANGE an EXISTING run configuration for Mule applications. Use this ONLY for modifying configurations that already exist, NOT for creating new ones. Trigger phrases include "edit config", "update config", "modify config", "change config". When you call use_skill, it must be the only tool call in that response.
pdk-unit
by mulesoftWrite and run unit tests for custom Flex Gateway policies built with the Policy Development Kit (PDK) — wire up `src/tests/`, build a first `UnitTestBuilder` test, mock HTTP/gRPC upstreams with closures or `TraceBackend`, factor reusable `TestConfig` helpers, assert on responses / headers / violations, run with `make test` or `cargo test`, troubleshoot init-sleep races, authority mismatches, and feature-gate skew. Use whenever the user mentions "PDK unit test", "pdk-unit", "UnitTestBuilder", "test my policy", "cargo test PDK", "mock backend PDK", "Flex Gateway policy unit testing", `with_http_upstream_from_authority`, `with_entrypoint`, `TraceBackend`, or asks "how do I test a Flex Gateway policy", "how do I mock an upstream in pdk-unit", "why is my policy timer not firing in tests". For full `pdk-unit` API reference see `pdk-templates/templates/unit_testing.md`. For scaffolding / build / publish see `develop-pdk-policy`.
generate-doc-description
by mulesoftCall use_skill as your FIRST and ONLY action when the user asks to document, add descriptions to, annotate, or add doc:description attributes to Mule XML files, flows, components, or connectors. Do not read project files first — this skill provides instructions for when to read files. Covers documenting all Mule elements in src/main/mule including flows, sub-flows, configs, listeners, processors, transforms, error handlers, and connectors. When you call use_skill, it must be the only tool call in that response.
pdk-templates
by mulesoftVetted, compilable Rust templates for common Flex Gateway Policy Development Kit (PDK) features — JWT validation/generation, OAuth2 introspection, header/body manipulation, body streaming, rate limiting, spike control, CORS, IP filtering, JSON/XML validators, HTTP outbound calls, gRPC, DataWeave evaluation, caching, distributed locks, worker variables, request data, control flow, contracts, data storage, timers, logging, metadata, policy violations, stop_iteration, outbound policies, and PDK unit testing. Use whenever the user asks "how do I X in PDK?", "show me a PDK template for Y", "PDK Rust snippet for Z", "JWT template", "rate limit template", "header manipulation example", "PDK gRPC", "PDK DataWeave", or any prompt mapping to one of the 30 template files under templates/. Read the matching file and adapt it into the user's `src/lib.rs` (and companion files for multi-file features). For project scaffolding, build, and publish lifecycle, defer to `develop-pdk-policy`.
pdk-test
by mulesoftWrite and run integration tests for custom Flex Gateway policies using the `pdk-test` framework — Docker-based, real Flex Gateway routing, `#[pdk_test]` macro, `TestComposite` orchestration, `HttpMock` / `GrpcBin` backends, `reqwest` assertions. Use whenever the user mentions "PDK integration test", "pdk-test", "functional test PDK", "#[pdk_test]", "TestComposite", "FlexConfig", "tests/requests.rs", "make test", or asks "how do I test my policy against real Flex", "how do I set up Docker-based tests for PDK", "why does my pdk-test timeout", "how do I mock a backend in integration tests".
setup-agent-scanner
by mulesoftCreates a scanner configuration to discover AI agents from external platforms like AWS Bedrock, Microsoft Copilot, or Google Vertex AI. Use when setting up agent discovery, configuring a new scanner, connecting to cloud AI platforms, or importing agents into Anypoint Exchange.
secure-mcp-server
by mulesoftSecure an MCP server by applying a policy from the catalog. Handles multiple starting points: from an existing API Manager instance, from an MCP server asset in Exchange, or from scratch by publishing the MCP server first. Use when the user wants to secure an MCP server, add rate limiting, apply OAuth2, enforce IP allowlisting, or protect any MCP server with a policy — regardless of where they are in the setup process.
secure-api
by mulesoftSecure an API by applying a policy from the catalog. Handles multiple starting points: from an existing API Manager instance, from an Exchange asset that needs an instance, or from scratch. Use when the user wants to secure an API, add rate limiting, apply OAuth2, enforce IP allowlisting, or protect any API with a policy — regardless of where they are in the setup process.
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.