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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fp-go-lens
by IBMUse this skill when working with lenses and optics in Go using the fp-go library (github.com/IBM/fp-go/v2/optics/lens). Trigger on mentions of lenses, optics, MakeLens, MakeLensRef, MakeLensStrict, lens composition, immutable updates to nested structs, accessing nested data structures, Compose, ComposeRef, ComposeOption, FromNillable, FromNillableRef, Modify, getter/setter patterns, or functional updates to Go structs. Also trigger when the user needs to update deeply nested fields immutably or work with optional fields in struct hierarchies. Also trigger for `// fp-go:Lens` annotation or go generate for lens code generation.
fp-go-logging
by IBMUse this skill when working with logging in fp-go functional pipelines (github.com/IBM/fp-go/v2/logging). Trigger on mentions of logging in monadic contexts, ChainFirstIOK, ChainFirst with logging, side-effect logging, structured logging (SLog), context-embedded loggers, LoggingCallbacks, TapSLog, LogEntryExit, Printf/Logf in IO/ReaderIO/ReaderIOResult, or when the user needs to add logging to functional pipelines without breaking the data flow.
fp-go-pipe-flow
by IBMGuides writing, refactoring, and reviewing fp-go v2 code that uses functional composition via Pipe and Flow. Apply this skill whenever the user asks to write new fp-go code, refactor imperative Go into functional style, explain a Pipe/Flow pipeline, add do-notation (Do/Bind/ApS), use the reader monad, create lenses for struct fields, or generate unit tests for functional pipelines. Trigger on any mention of: Pipe, Flow, reader monad, kleisli, do-notation, Bind, ApS, fp-go pipeline, point-free style, lens composition in Go.
sop-builder
by IBMExpert guidance for building a Standard Operating Procedure (SOP) from a workflow diagram, Langflow JSON, n8n JSON, BPMN model or workflow description. Focuses on business problem, data requirements, and business logic in plain language.
wxo-analyzer
by IBMAnalyzes watsonx Orchestrate (wxO) projects and generates a 3-report documentation set covering the overall solution, agents, and tools/connections/other components with Mermaid diagrams.
wxo-builder
by IBMExpert guidance for generating watsonx Orchestrate native solutions from SOPs or simple prompts. Implements agents, flows, tools, and knowledge bases based on business requirements.
solution-architect
by IBMExpert guidance for creating high-level solution architecture documents from business requirements, use cases, or problem statements. Produces three focused documents - business overview, technical architecture, and implementation plan - with sufficient detail for elaboration into detailed SOPs.
customercare-mcp-builder
by IBMBuild MCP servers for customer care agents following Watson Orchestrate specifications. Guides agents through tool creation, transaction patterns, authentication, widgets, and context management with strict adherence to reference specifications. Use when creating customer care MCP servers.
travel-recommendations
by IBMGet personalized travel destination recommendations based on preferences
currency-converter
by IBMConvert amounts between different currencies with current exchange rates
weather-forecast
by IBMGet current weather and forecast for any location worldwide
examining-ado-project
by IBMBuilds a picture of work in an ado project: activity volume, spaces and operations created over time, experiments and operation configs used etc. Use to create a project/context overview report, summarize what the team has been doing in an ado project, report trends across spaces/operations, or to onboard onto an ado project.
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.