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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fabric-report
by qkfangCreate and manage Microsoft Fabric Power BI Reports in PBIR format. Covers report.json, page layouts, visual definitions, themes, and .platform config. USE FOR: create Fabric report, Power BI report, PBIR format, report.json, page layout, visual definition, report theme, report pages, Fabric report CI/CD, chart visual, table visual, card visual. DO NOT USE FOR: deploying reports (use fabric-deployment), semantic model definitions (use fabric-semantic-model), lakehouse schemas (use fabric-lakehouse).
fabric-deployment
by qkfangDeploy Microsoft Fabric workspace items using fabric-cicd library and CI/CD automation. Covers deploy_workspace.py, parameter.yml, variable.json, publish_all_items, environment configuration, service principal auth, and post-deploy notebook execution. USE FOR: deploy Fabric workspace, fabric-cicd, publish items, CI/CD pipeline, parameter.yml, variable.json, deploy script, service principal auth, multi-environment deployment, Fabric REST API, unpublish orphans, force republish, validate repo. DO NOT USE FOR: creating notebooks (use fabric-notebook), creating lakehouses (use fabric-lakehouse), creating semantic models (use fabric-semantic-model), creating reports (use fabric-report).
fabric-lakehouse
by qkfangCreate and manage Microsoft Fabric Lakehouses with Delta tables, OneLake shortcuts, and schema definitions. Covers lakehouse.metadata.json, shortcuts.metadata.json, .platform config, and table schemas. USE FOR: create Fabric lakehouse, Delta Lake tables, OneLake shortcuts, lakehouse schema, lakehouse metadata, table columns, Fabric storage, lakehouse CI/CD, dbo schema, shortcut config. DO NOT USE FOR: deploying lakehouses (use fabric-deployment), querying data (use fabric-notebook), semantic models on lakehouse (use fabric-semantic-model).
fabric-notebook
by qkfangCreate and manage Microsoft Fabric Notebooks (PySpark). Covers notebook structure, .platform metadata, cell layout, Spark session config, SQL connectivity via AAD tokens, and data seeding patterns. USE FOR: create Fabric notebook, PySpark notebook, notebook-content.py, Fabric Spark, notebook cells, notebook metadata, seed data notebook, SQL from notebook, mssparkutils, notebookutils, Fabric notebook CI/CD. DO NOT USE FOR: deploying notebooks (use fabric-deployment), lakehouse schemas (use fabric-lakehouse), semantic models (use fabric-semantic-model).
fabric-semantic-model
by qkfangCreate and manage Microsoft Fabric Semantic Models using TMDL (Tabular Model Definition Language). Covers Direct Lake mode, table definitions, relationships, expressions, measures, and .platform config. USE FOR: create semantic model, TMDL files, Direct Lake, model.tmdl, database.tmdl, relationships.tmdl, expressions.tmdl, table definitions, measures, DAX, Power BI dataset, Fabric semantic model CI/CD. DO NOT USE FOR: deploying models (use fabric-deployment), lakehouse schemas (use fabric-lakehouse), report visuals (use fabric-report).
biztalk-xsd-to-csharp
by qkfangConvert BizTalk XSD schema files to C# model classes with XML serialization attributes. Use when: migrating BizTalk XSD schemas, generating C# models from XSD, converting BizTalk message types, XSD to POCO, create C# classes from schema, BizTalk schema migration to .NET 8.
biztalk-map-to-service
by qkfangConvert BizTalk maps (.btm, .xsl) and Scripting Functoids (inline C# / external assembly) to a C# transform service for Azure Functions. Use when: migrating BizTalk map, converting BTM file, translating XSLT map, porting Scripting Functoid, String Concatenate functoid, String Constant functoid, inline C# functoid, external assembly functoid, BizTalk map migration, transform service C#, ContributionToAllocationMap migration.
biztalk-orchestration-to-function
by qkfangConvert a BizTalk orchestration (.odx) and its binding file to an Azure Functions v4 HTTP-triggered function (.NET 8 isolated worker). Use when: migrating BizTalk orchestration, converting ODX file, porting receive port to HttpTrigger, porting send port to HttpClient service, BizTalk orchestration to Azure Functions, orchestration migration C#, SuperContributionOrchestration migration, BizTalk to Azure Functions C#.
figma-design
by qkfangGuide for interpreting and translating Figma design files into production-ready web code. Use this when working with Figma URLs, design tokens, components, layouts, and assets.
straighten-quotes
by qkfangCleans up HTML text by fixing quotes, apostrophes, em dashes, and double spaces for CMS compatibility.
agent-owasp-compliance
by qkfangCheck any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
create-agentsmd
by qkfangPrompt for generating an AGENTS.md file for a repository
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.