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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b24phpsdk-maintainer
by bitrix24Use this skill whenever working with GitHub issues in the bitrix24/b24phpsdk repository: creating new issues, reading existing ones, planning implementation from an issue, referencing an issue in commits, branches, or CHANGELOG, or discovering unsupported Bitrix24 REST API methods and filing tracking issues. IMPORTANT: this skill MUST be invoked before doing any issue-related work.
b24-ui-nuxt
by bitrix24Build UIs with @bitrix24/b24ui-nuxt v2 — 125+ accessible Vue components with Tailwind CSS theming. Use when creating interfaces, building forms, or composing layouts for Bitrix24 application.
b24jssdk-recipes
by bitrix24End-to-end mini-apps built on the canonical b24jssdk actions.v{2,3}.* surface — CRM analytics, ERP sync, Telegram bot, mass mailing, task automation, AI assistant, web search + LLM, Disk files, webhook handler, error-handling cookbook, event registration, OAuth install handshake. Each recipe is a self-contained TypeScript program using B24Hook on the server side. Load when the user asks for a working example or a starting template.
b24jssdk-rest
by bitrix24Call the Bitrix24 REST API through b24jssdk using the canonical actions.v{2,3}.*.make() surface. Covers call, batch, callList, fetchList, batchByChunk for both API versions, picking between v2 and v3, and the rules for the new AjaxResult shape. The legacy callMethod/callBatch/callListMethod/fetchListMethod surface is @deprecated for 2.0.0 — do not generate code against it.
b24jssdk-vibecode
by bitrix24How to combine the b24jssdk SDK with the VibeCode HTTP API (vibecode.bitrix24.tech). Most teams should NOT mix the two — pick one. This skill explains why, where they overlap, and the only practical pattern when you need both. Load only when the user explicitly mentions VibeCode.
b24jssdk-frame-ui
by bitrix24Use B24Frame UI managers (slider, dialog, parent, placement, options, auth) from a Bitrix24 placement iframe app. Covers opening sliders with mobile fallback, picking users and CRM entities, resizing the parent window, placement setValue, persisting app/user options. Load when generating in-frame UI code.
b24jssdk-helpers
by bitrix24Use B24HelperManager / useB24Helper to preload portal data (profile, app, currencies, options, license, payment), format currency, and subscribe to the Pull (push) client. Designed primarily for in-frame apps; the helpers can also work with B24Hook / B24OAuth for read-only data.
b24jssdk-core
by bitrix24Pick and initialize the right b24jssdk entry point (B24Hook for backends, B24Frame for in-iframe apps, B24OAuth for OAuth-installed apps), wire up logging, handle errors, and tune restriction-manager retry behaviour (hardErrorCodes, softErrorCodes, retryOnNetworkError). Load first when generating any b24jssdk code.
b24jssdk-filtering
by bitrix24Build filter, order, and select parameters for Bitrix24 REST methods called via b24jssdk's actions.v{2,3}.* API. Covers the v2 prefix dialect (>=, <=, !, %, =%), the v3 array-triple dialect ([['field', 'op', value]]), NOT, multi-value (IN), dates via Text.toB24Format, and the order-stripping rule of callList. Load when building filtered queries.
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.