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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ibmi
by ajshedivyCore skill for working with IBM i systems through the ibmi CLI. Covers text-to-SQL methodology, Db2 for i conventions, schema discovery, multi-system configuration, and — critically — agent scripting patterns (automatic JSON-when-piped, semantic exit codes, NDJSON streaming, dry-run planning, watch mode, multi-system workflows). Use this skill as the foundation for ANY IBM i task: running queries, exploring the database, configuring systems, writing bash/agent scripts that target IBM i, or composing pipelines that need structured output and reliable error handling.
agno-tools
by ajshedivyCreate custom tools for Agno agents. Covers function tools, the @tool decorator, toolkit classes, RunContext for state access, and MCP tool integration. Trigger this skill when: writing custom agent tools, importing agno.tools, using @tool decorator, creating toolkit classes, or asking "how do I create tools for Agno agents?"
agno-workflow
by ajshedivyBuild step-based workflows with Agno for sequential agent pipelines. Trigger this skill when: importing agno.workflow, creating Workflow or Step instances, building pipelines, chaining agents, or asking "how do I build a workflow with Agno?"
agno-agentos
by ajshedivyDeploy Agno agents as production APIs using AgentOS. Covers FastAPI integration, database configuration, knowledge registration, and serving. Trigger this skill when: importing agno.os, creating AgentOS instances, deploying agents to production, serving agents via API, or asking "how do I deploy an Agno agent?"
agno-agent
by ajshedivyBuild single Agno agents with tools, structured output, storage, memory, knowledge bases, guardrails, and human-in-the-loop confirmation. Trigger this skill when: importing agno.agent, creating an Agent instance, adding tools to an agent, configuring agent storage/memory, or asking "how do I build an agent with Agno?"
agno-guardrails
by ajshedivyAdd input and output guardrails to Agno agents. Covers built-in guardrails (PII detection, prompt injection), custom guardrails, output validation, and moderation. Trigger this skill when: importing agno.guardrails, creating guardrail classes, adding pre_hooks or post_hooks, or asking "how do I add safety checks to my agent?"
agno-integrations
by ajshedivyIntegrate Agno agents with observability platforms, A2A protocol, and external services. Covers OpenTelemetry, Langfuse, Arize Phoenix, Agent-to-Agent protocol, and Discord bots. Trigger this skill when: adding observability to agents, setting up tracing, integrating with Langfuse or similar platforms, or asking "how do I monitor my agents?"
agno-knowledge
by ajshedivyBuild knowledge bases for Agno agents using vector databases, embedders, and document readers. Covers ChromaDB, PgVector, LanceDB, document loading, chunking strategies, and hybrid search. Trigger this skill when: importing agno.knowledge, creating Knowledge instances, adding documents to agents, configuring vector databases, or asking "how do I add a knowledge base?"
agno-memory
by ajshedivyAdd persistent memory to Agno agents. Covers MemoryManager, agentic memory, shared memory between agents, multi-user sessions, and memory tools. Trigger this skill when: importing agno.memory, configuring MemoryManager, enabling agentic memory, or asking "how do I add memory to an agent?"
agno-models
by ajshedivyConfigure model providers for Agno agents. Covers Anthropic, OpenAI, Google, Groq, Ollama, AWS Bedrock, Azure, and 40+ other providers. Trigger this skill when: switching model providers, configuring model parameters, using model strings, or asking "how do I use a different model with Agno?"
agno-multimodal
by ajshedivyBuild multimodal Agno agents that handle images, audio, and video. Covers image analysis, audio input/output, video captions, and file processing. Trigger this skill when: processing images with agents, handling audio or video, using vision capabilities, or asking "how do I build a multimodal agent?"
agno-reasoning
by ajshedivyAdd reasoning and chain-of-thought capabilities to Agno agents. Covers ReasoningTools, built-in model reasoning, reasoning content streaming, and structured problem solving. Trigger this skill when: importing agno.tools.reasoning, enabling chain-of-thought, building agents that think step-by-step, or asking "how do I add reasoning to my agent?"
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.