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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apache-spark-catalyst-api
by aehrcExpert guidance for working with the Apache Spark Catalyst query optimisation framework. Use this skill when working with Spark SQL internals, creating custom expressions, implementing query optimisations, working with logical/physical plans, or extending Catalyst. Trigger keywords include "catalyst", "spark sql", "expression", "logical plan", "physical plan", "tree node", "query optimisation", "rule executor", "analyzer", "optimizer", "code generation".
wiremock
by aehrcExpert guidance for using WireMock in Java applications for HTTP API mocking and testing. Use this skill when the user asks to mock HTTP APIs, create API stubs, test REST clients, simulate network faults, verify HTTP requests, or integrate WireMock with Spring Boot. Trigger keywords include "wiremock", "mock http", "stub api", "http mock", "api testing", "rest mock", "simulate fault", "verify request", "spring boot wiremock".
fhir-bulk-data
by aehrcExpert guidance for implementing FHIR Bulk Data Access (Flat FHIR) following the HL7 specification. Use this skill when implementing bulk data export from FHIR servers, building bulk data clients, working with the $export operation, handling NDJSON output files, implementing asynchronous polling workflows, or integrating with SMART Backend Services authorisation. Trigger keywords include "bulk data", "bulk export", "$export", "NDJSON", "bulk FHIR", "async export", "flat FHIR", "Patient/$export", "Group/$export", "system export".
fhir-api
by aehrcExpert guidance for implementing FHIR RESTful API servers and clients following the HL7 FHIR specification. Use this skill when implementing a FHIR server with REST endpoints, building a FHIR client, designing FHIR API routes and handlers, implementing FHIR operations (read, create, update, delete, search, history), working with FHIR bundles, batch requests, or transactions, handling FHIR content negotiation, headers, and versioning, or implementing conditional operations. Trigger keywords include "FHIR REST", "FHIR API", "FHIR server", "FHIR client", "FHIR endpoint", "FHIR operations", "RESTful FHIR", "implement FHIR".
fhirpath
by aehrcExpert guidance for writing FHIRPath expressions - a path-based navigation and extraction language for FHIR data. Use this skill when writing FHIRPath expressions, navigating FHIR resource trees, filtering collections, performing date/time arithmetic, using FHIRPath functions, writing FHIR invariants, or understanding FHIRPath operators. Trigger keywords include "FHIRPath", "fhirpath", "path expression", "FHIR navigation", "where()", "select()", "exists()", "ofType()", "resolve()", "FHIR invariant", "collection filtering", "FHIRPath function".
openspec-explore
by aehrcEnter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
jmh-benchmarks
by aehrcExpert guidance for writing Java microbenchmarks using JMH (Java Microbenchmark Harness). Use this skill when writing performance benchmarks, measuring method execution time, comparing algorithm implementations, profiling code performance, or debugging benchmark issues. Trigger keywords include "jmh", "benchmark", "microbenchmark", "performance test", "@Benchmark", "throughput", "warmup", "blackhole", "measure performance".
sql-on-fhir
by aehrcExpert guidance for implementing SQL on FHIR v2 ViewDefinitions and operations to create portable, tabular projections of FHIR data. Use this skill when the user asks to create ViewDefinitions, flatten FHIR resources into tables, write FHIRPath expressions for data extraction, implement forEach/forEachOrNull/repeat patterns for unnesting, create where clauses for filtering, use constants in view definitions, combine data with unionAll, execute ViewDefinitions with $run or $export operations, or implement SQL on FHIR server capabilities. Trigger keywords include "ViewDefinition", "SQL on FHIR", "flatten FHIR", "tabular FHIR", "FHIR to SQL", "FHIR analytics", "FHIRPath columns", "unnest FHIR", "$viewdefinition-run", "$export", "view runner", "repeat", "recursive", "QuestionnaireResponse".
databricks-cli
by aehrcExpert guidance for using the Databricks CLI to manage Databricks workspaces, clusters, jobs, pipelines, Unity Catalog, SQL warehouses, serving endpoints, secrets, bundles, and all other Databricks resources. Use this skill when running databricks commands, managing Databricks infrastructure, deploying bundles, querying serving endpoints, managing Unity Catalog objects, or automating Databricks workflows. Trigger keywords include "databricks", "databricks cli", "dbfs", "unity catalog", "databricks bundle", "databricks jobs", "databricks clusters", "sql warehouse", "serving endpoint", "databricks secrets".
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.