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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osiris-component-developer
by keboolaCreate production-ready Osiris ETL components (extractors, writers, processors). Use when building new components, implementing capabilities (discover, streaming, bulkOperations), adding doctor/healthcheck methods, packaging for distribution, validating against 60-rule checklist, or ensuring E2B cloud compatibility. Supports third-party component development in isolated projects.
duckdb-transformation
by keboolaExpert knowledge for writing, optimizing, and migrating DuckDB transformations in Keboola. Covers SQL dialect, block orchestration, dynamic backends, Parquet format, case sensitivity, Snowflake-to-DuckDB migration, type casting patterns, and best practices. Use when the user is writing DuckDB SQL, migrating from Snowflake, debugging DuckDB transformations, or needs guidance on DuckDB-specific syntax and configuration in Keboola.
kbagent
by keboolaUse when working with Keboola Connection projects via kbagent CLI. Covers: exploring and searching configurations (extractors, writers, transformations), browsing job history, analyzing cross-project data lineage, calling MCP tools across multiple projects, managing development branches, debugging SQL in temporary workspaces, bulk-onboarding organizations, syncing project configs as local files (GitOps), git-branching with Keboola dev branch isolation, sharing buckets across projects, linking shared data, encrypting secrets for MCP tool call workflows, uploading/downloading Storage Files with tag management, and syncing storage metadata and job history. Triggers: kbagent, Keboola project, keboola configs, keboola jobs, keboola lineage, keboola transformations, keboola MCP tools, keboola workspace, SQL debugging, keboola branches, keboola organization, keboola sharing, bucket sharing, link bucket, keboola sync, keboola git, keboola gitops, sync pull, sync push, sync diff, branch-link, search configs, find in c
kai-streamlit-integration-guide
by keboolaThis skill should be used when the user asks to "build a streamlit app with Kai", "create a Keboola data app", "integrate Kai with Streamlit", "stream Kai responses in Streamlit", "add Kai chat to Streamlit", "build a Keboola chat UI", "create a data app", or mentions Streamlit with Kai, KaiClient, or Keboola AI Assistant. Provides patterns, gotchas, and working code for building Streamlit apps that integrate with the KaiClient library.
kai-javascript-integration-guide
by keboolaThis skill should be used when the user asks to "build a JS app with Kai", "create a JavaScript data app", "integrate Kai with Express", "stream Kai responses in JavaScript", "add Kai chat to a web app", "build a Keboola chat UI in JS", "create a JS data app", or mentions JavaScript/Express/Node.js with Kai, kai-assistant, or Keboola AI Assistant. Provides patterns, gotchas, and working code for building JavaScript apps that integrate with the Kai API.
kai-cli-usage-guide
by keboolaThis skill should be used when the user asks to "use kai", "run kai command", "chat with Keboola AI", "query Keboola", "list tables", "check kai history", "interact with Keboola assistant", "send message to kai", or mentions Kai CLI, Keboola AI Assistant, or wants to execute Kai commands. Provides comprehensive guidance for using the Kai CLI tool to interact with the Keboola AI Assistant.
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.