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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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cflx-accept
by tumfPortable Conflux acceptance operation skill. Defines the JSON-primary verdict interface and autonomous acceptance review guidance for any agent runtime. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-accept-with-speca
by tumfPortable Conflux acceptance review with an additional SPECA-style property/proof-attempt lens. Drop-in compatible with cflx-accept and cannot ask questions or request user input.
cflx-analyze
by tumfDependency analysis for Conflux change selection. Evaluates queued changes and selects the next change to process based on dependency order, progress, and completion status. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-apply
by tumfImplement an approved OpenSpec change autonomously with truthful task tracking. Provides apply-specific guidance for Conflux orchestration. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-archive
by tumfArchive deployed OpenSpec changes and update canonical specs. Provides archive-specific guidance for Conflux orchestration. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-proposal
by tumfCreate structured Conflux change proposals through interactive conversation with users. Use when users request "create a proposal", "draft a change", "propose a feature", or similar proposal creation tasks. This skill asks clarifying questions and guides users through the proposal process.
cflx-rejection-guide
by tumfGuide users through handling Conflux/OpenSpec changes that ended in `REJECTED.md`, `Rejected`, `Blocked`, `Rejecting`, or informal states like "rejected gated". Use whenever the user asks what to do with a rejected proposal, how to recover a blocked change, how to interpret rejecting review outcomes, or how to choose between closing, blocking, and resuming a change.
cflx-resolve
by tumfConflict resolution and sequential merge guidance for Conflux parallel execution. Provides fixed rules for merge conflict resolution, pre-sync requirements, and retry continuation. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-workflow
by tumfLegacy compatibility router for Conflux workflow operations. Routes apply/rejecting/cleanup-review/accept/archive to self-contained operation guidance. New orchestrator prompts should use dedicated operation-specific skills (cflx-apply, cflx-accept, etc.) instead. CRITICAL - This skill CANNOT ask questions or request user input.
cflx-run
by tumfRun the standard Conflux development flow for an already-defined and committed OpenSpec change. Use when users want to execute `cflx run`, start Conflux orchestration, or follow the standard proposal-then-run workflow on a clean base branch.
xcom-rs
by tumfInteract with X/Twitter via the xcom-rs CLI (Rust). Use for posting tweets, replies, threads, searching, reading timelines/mentions, liking, retweeting, bookmarks, media upload, and user lookups. Use this skill whenever the user wants to do anything on X/Twitter — posting, reading, searching, monitoring mentions, managing bookmarks, or looking up users and their tweets.
jp-grants
by tumfCollect and answer questions about Japanese subsidies/grants (補助金・助成金) with up-to-date sources. Use when a user asks: which programs they qualify for, eligibility, deadlines, required documents, application steps, or where to find official calls for proposals and past award/adoption examples (e.g. J-Grants, METI/SME Agency, MHLW, prefectures/municipalities). Includes workflows and scripts for web search + structured extraction with citations. This skill is methodology-focused rather than program-specific: it provides reusable ways to discover and verify official information across many programs.
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.