381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 20 skills
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clinical-research

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When the user wants to design, build, or operate clinical research informatics tools and workflows. Use when the user mentions "clinical trial," "clinical research," "RCT," "observational study," "registry study," "pragmatic trial," "real-world evidence," "RWE," "decentralized clinical trial," "DCT," "EDC," "REDCap," "Medidata Rave," "Veeva Vault EDC," "Castor," "OpenClinica," "CDISC," "CDASH," "SDTM," "ADaM," "Define-XML," "SEND," "eSource," "eCRF," "IRB," "single IRB," "sIRB," "Common Rule," "Belmont Report," "21 CFR Part 50," "21 CFR Part 56," "GCP," "ICH E6," "ICH E2B," "SAE," "SUSAR," "IWRS," "IRT," "randomization," "eConsent," "ePRO," "eCOA," "CTMS," "Veeva CTMS," "Florence eBinders," "ClinicalTrials.gov," or "FDAAA 801." For HIPAA Authorization vs. waiver mechanics, see hipaa-compliance. For 21 CFR Part 11 e-records / e-signatures, see 21-cfr-part-11. For trial-related FHIR integration with EHRs, see fhir-integration.

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xunit-nunit

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When the user wants to design, implement, debug, or optimize unit / integration tests in .NET using xUnit, NUnit, or MSTest. Use when the user mentions "xUnit," "NUnit," "MSTest," "[Fact]," "[Theory]," "InlineData," "[Test]," "[TestCase]," "[TestFixture]," "Moq," "NSubstitute," "FluentAssertions," "Shouldly," "FsCheck," "dotnet test," "Verify," "Snapshooter," or "WebApplicationFactory." For JS/TS see jest-vitest. For Python see pytest. For Java see junit-testng. For Go see go-test.

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ehr-integration

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When the user wants to integrate with a specific EHR vendor's APIs, app program, or sandbox. Also use when the user mentions "Epic," "App Orchard," "Connection Hub," "Cerner," "Oracle Health," "Millennium," "CareAware," "Code Console," "Ignite APIs," "HealtheLife," "Athenahealth," "athenaOne," "MDP," "More Disruption Please," "Meditech," "Greenfield," "eClinicalWorks," "NextGen," "Allscripts," "Veradigm," "OpenEMR," "EHR sandbox," "vendor app review," "Care Everywhere," "Hyperdrive," "Chronicles," "Interconnect," "DocumentReference write-back," or "MDM message." For FHIR-resource-level work, see fhir-integration. For SMART OAuth specifics, see smart-on-fhir. For HL7 v2 messaging, see hl7-v2.

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wiremock

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When the user wants to virtualize HTTP services for testing — mocking downstream dependencies, simulating slow/error responses, recording-and-replay, or building a stub server for development. Use when the user mentions "WireMock," "stubFor," "mappings," "WireMockServer," "WireMock standalone," "request matching," "scenarios in WireMock," "fault injection," "WireMock Cloud," or "wiremock studio." For consumer-driven contracts see pact-contract-testing. For Karate's Netty mocks see karate. For language-native HTTP mocking see pytest-api / supertest.

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ihe-profiles

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When the user wants to design, deploy, or troubleshoot IHE (Integrating the Healthcare Enterprise) integration profiles. Use when the user mentions "IHE," "XDS," "XDS.b," "XDR," "XDM," "XCA," "PIX," "PDQ," "ATNA," "BPPC," "CT (Consistent Time)," "DSUB," "MHD," "PIXm," "PDQm," "IUA," "MHDS," "mCSD," "Cross-Enterprise Document Sharing," "ITI Technical Framework," "PCC profile," "RAD profile (SWF, XDS-I.b)," or "actors and transactions." For FHIR-native exchange, see fhir-integration. For TEFCA-level policy and QHIN structure, see tefca-hie. For audit details, see audit-logging.

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fhir-integration

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When the user wants to design, implement, debug, or review FHIR integrations. Use when the user mentions "FHIR," "FHIR R4," "FHIR R5," "FHIR resource," "FHIR bundle," "FHIR search," "CapabilityStatement," "US Core," "USCDI," "CARIN BB," "Da Vinci," "mCODE," "IPS," "bulk data," "$export," "OperationOutcome," or names specific resources like Patient/Encounter/Observation/Condition/MedicationRequest. For SMART OAuth flows, see smart-on-fhir. For HL7 v2 pipe-delimited messages, see hl7-v2. For terminology services and code systems, see terminology-services. For CDA documents, see cda-ccda.

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hl7-v2

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When the user wants to design, parse, generate, or troubleshoot HL7 v2.x pipe-delimited messages. Use when the user mentions "HL7 v2," "HL7 2.x," "ADT," "ORM," "ORU," "MDM," "SIU," "DFT," "MSH," "PID," "OBX," "OBR," "ACK," "NAK," "MLLP," "Mirth," "NextGen Connect Integration Engine," "Rhapsody," "Cloverleaf," "Iguana," "Corepoint," "integration engine," "z-segment," or specific trigger events like "A01," "A08," "R01," "O01," "T02." For modern REST/JSON exchange, see fhir-integration. For DICOM imaging messages, see dicom-imaging. For CDA clinical documents, see cda-ccda.

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21-cfr-part-11

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When the user is building, validating, or auditing a computer system that creates, modifies, maintains, archives, retrieves, or transmits electronic records or applies electronic signatures in an FDA-regulated (GxP) context. Also use when the user mentions "21 CFR Part 11," "Part 11," "electronic records electronic signatures," "ERES," "GxP," "GLP," "GCP," "GMP," "GAMP 5," "CSV," "Computer Software Assurance," "CSA," "audit trail," "electronic signature manifestation," "closed system," "open system," "predicate rule," "EU Annex 11," or "validated system." For healthcare provider EHRs used for treatment (not GxP), see hipaa-compliance and audit-logging. For HITRUST or HIPAA generally, see hipaa-compliance.

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clinical-documentation

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When the user wants to design clinical documentation workflows, templates, or note ingestion. Also use when the user mentions "clinical note," "progress note," "H&P," "history and physical," "SOAP note," "APSO," "discharge summary," "consult note," "op note," "operative note," "SmartPhrase," "SmartForm," "dot phrase," "structured data capture," "clinical NLP," "cTAKES," "MedSpaCy," "Amazon Comprehend Medical," "HealthLake," "ambient scribe," "DAX," "Nuance DAX," "Suki," "Abridge," "Augmedix," "DeepScribe," or "AI scribe." For underlying FHIR resources, see fhir-integration. For order entry, see cpoe-orders. For HIPAA implications of recording encounters, see hipaa-compliance.

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espresso

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When the user wants to design, implement, debug, or stabilize Espresso tests for native Android UI automation. Use when the user mentions "Espresso," "onView," "ViewMatchers," "withId," "IdlingResource," "ActivityScenario," "ActivityScenarioRule," "AndroidJUnitRunner," "androidx.test," "instrumented tests," "Espresso Intents," "Espresso Web," or "@RunWith(AndroidJUnit4)." For cross-platform mobile see appium. For iOS-native see xcuitest. For React Native see detox. For YAML-driven flows see maestro.

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supertest

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When the user wants to design, implement, debug, or scale supertest-based API tests in Node.js. Use when the user mentions "supertest," "request(app)," "Express test," "Koa test," "Fastify test," "Jest + supertest," "Mocha + supertest," or "in-process HTTP testing." For Java API testing see rest-assured. For Python see pytest-api. For Postman collections see postman-newman. For contract testing see pact-contract-testing.

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test-reporting

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When the user wants to design, integrate, or improve test reporting — JUnit XML aggregation, HTML dashboards, Allure, Cucumber Reports, screenshots / videos / traces, failure triage, and historical analytics. Use when the user mentions "test reports," "JUnit XML," "Allure," "mochawesome," "Cucumber Reports," "Datadog CI Visibility," "Buildkite Test Analytics," "test result aggregation," "trace / artifacts on failure," "test dashboard," or "trend analysis." For CI orchestration see ci-test-orchestration. For flake analytics see flaky-test-management.

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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.