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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ui5-version-upgrade
by marianfooPlan and execute a SAPUI5/OpenUI5 version upgrade. Use when the user is bumping the UI5 version in manifest.json, ui5.yaml, package.json, or an existing UI5 app and wants to identify deprecations in use, fixes that remove local workarounds, relevant new features, manifest/schema issues, and API replacements. Combines this server's `ui5_version_diff` tool with SAP's `@ui5/mcp-server` tools.
arc1-cursor-regression
by marianfooUse when user asks to generate Cursor MCP config + regression prompts for ARC-1. Adaptive: derive tests from PR diff or chat findings and build targeted setup/prompts for changed features/fixes.
analyze-chat-session
by marianfooAnalyze the current conversation's tool calls, responses, errors, and approach to produce a structured feedback report for improving MCP tool usage with ARC-1. Use when the user asks to "analyze this session", "review my chat", "give feedback on tool usage", "how did I do", or wants a retrospective on a debugging session.
bootstrap-system-context
by marianfooGround the assistant in the target SAP system before any coding work by producing a local system-info.md that captures SID, release, components, detected features, RAP constraints, and ARC-1 lint preset. Use when asked to "set up system context", "bootstrap the SAP system", "create system-info.md", or when starting a session against an unfamiliar SAP system.
convert-ui5-to-fiori-elements
by marianfooGenerate a Fiori Elements V4 LROP app (list report + object page) driven by @UI.* annotations on a V4 RAP service, using the Fiori MCP server's 3-step (list_functionalities → get_functionality_details → execute_functionality) workflow. Use when asked to "build a Fiori Elements app", "generate LROP from this V4 service", "convert to annotation-driven UI", or "scaffold Fiori Elements V4".
explain-abap-code
by marianfooExplain ABAP objects with full dependency context (via SAPContext) and optional ATC code quality analysis — replicates SAP Joule's "Explain Code" capability, including behavior definitions (BDEF — CRUD graph, determinations/validations, bound handler class). Use when asked to "explain this ABAP", "what does ZCL_X do", "walk me through this class/CDS view/behavior definition", or "review this object's quality".
generate-abap-unit-test
by marianfooGenerate ABAP Unit tests for classes with dependency analysis, interface-based test doubles, and method-level surgical insertion. Use when asked to "generate unit tests for this class", "add ABAP Unit tests", "write tests for this method", or "create test doubles for ZCL_X".
generate-analytics-star-schema
by marianfooGenerate a CDS analytical model (star schema — cube + dimension + text views) on top of a RAP business object or a DDIC table. Use when asked to "create a star schema", "generate an analytical cube and dimensions", "make a RAP BO analytical", "build a CDS cube from a table", or "create an analytical model".
generate-cds-analytical-query
by marianfooGenerate an analytical CDS query (transient projection view with PROVIDER CONTRACT ANALYTICAL_QUERY) on top of an existing analytical cube. Use when asked to "create an analytical query", "build a KPI query on a cube", "generate an ANALYTICAL_QUERY view", or "expose a cube for analytics/embedded analytics".
generate-cds-unit-test
by marianfooGenerate an ABAP Unit test class for a CDS entity using the CDS Test Double Framework — replicates SAP Joule's "CDS Unit Test Generation" capability. Use when asked to "generate CDS unit tests", "test this CDS view", "create CDSTDF tests", or "write unit tests for ZI_SALESORDER".
generate-rap-logic
by marianfooGenerate RAP determinations, validations, and custom action implementations for an existing behavior definition by reading the RAP stack and filling empty method stubs in the behavior pool. Use when asked to "implement RAP determinations", "fill in behavior pool methods", "add RAP validation logic", or "generate custom action code".
generate-rap-service-researched
by marianfooGenerate a production-quality RAP OData service through deep system research, best-practice analysis, and iterative planning with user approval before any code is written. Use when asked to "plan a RAP service properly", "research before building RAP", "design a RAP service for production", or for large RAP greenfield work in transportable packages.
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.