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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src-tree-reorganize
by p10ns11yReorganize scattered C/C++ sources into src/ with CMake updates. Use when user asks to tidy layout, move files to src, or clean root-level clutter.
concurrent-cli-agents
by p10ns11yRuns Hermes, OpenClaw, and Grok Build concurrently on isolated git worktrees or cloud sandboxes (Modal, Daytona, E2B, Fly Sprites). Use when orchestrating multiple CLI coding agents, git worktree isolation, subagents in separate branches, or offloading agent runs to remote sandboxes.
gt-flow
by p10ns11yMost sensible Graphite (gt) stacking decision and execution flow. Diagnoses coupling (overlapping file touches across planned slices), honestly recommends "gt is overkill here — use plain branches / split-to-prs / sequential waves / one PR" when gt would add complexity or risk (especially with coupled core files or agent worktree generation). When gt is chosen or the safe path is taken, enforces commit-in-generation-env + explicit two-phase handoff + backup refs + per-slice escape branches + state so generated code is never lost and recovery is trivial. Use on any gt/graphite/stack/submit --stack decision, execute-plan assembly, concurrent agent work, or manual PR planning.
cv-promote-guard
by p10ns11yStrict self-guarded mechanism for reading an external CV source (e.g. cvdata.json in a portfolio repo) for grounding and safely promoting insights back with sidecars, diffs, previews, backups, and explicit user confirmation. Never mutates external profile without multiple gates. Use for CV-related logic in finder-reactor or prep flows. Sidecar-first, auditable, intervention points. Fission for safe edit code; fusion for protecting the public profile while improving future matches.
finder-reactor
by p10ns11yCore autonomous, self-guarded decision loop for opportunity-finder apps. Handles discovery (e.g. X search), analysis (LLM + CV + platform context), prep generation, tracking, and guarded promote with cost/rate/fit/CV mutation guards, human pause points, structured decisions, and logging. Use when designing, implementing, or debugging the agentic heart of a finder platform. Fission for tight loops; fusion for reactor architecture and surplus.
fusion-sage
by p10ns11yFusion-oriented evolution of Context Sage. Uses the original fission engine as containment field, then adds synthesis, surplus generation, and self-amplifying knowledge loops. For AI coding tasks where you want not just efficiency, but compounding intelligence.
tauri-agentic
by p10ns11yPatterns for building agentic, MCP-exposed, self-guarded desktop apps in Tauri (Rust backend + React/TS frontend). Expose finder functions as MCP tools, implement guards/pauses in UI and backend, command palette as agent interface, secure key storage, integration with X resources and CV guard. Use when implementing the Tauri shell, reactor UI, or MCP server. Fission for Rust/TS code; fusion for desktop as agent body.
x-agent-resources
by p10ns11yIntegration and usage of official X Developer Platform agent resources (llms.txt, skill.md, MCP/XMCP, xurl, OpenAPI) for accurate, composable X access in agentic apps. Use when building search, analysis, posting, or any X interaction. Ensures llms/skill ground prompts, MCP exposes tools, xurl provides CLI UX. Fission for implementation; fusion for unifying with finder-reactor and agentic design.
fusion-sage
by p10ns11yFusion-oriented evolution of Context Sage. Uses the original fission engine as containment field, then adds synthesis, surplus generation, and self-amplifying knowledge loops. For AI coding tasks where you want not just efficiency, but compounding intelligence.
ai-optimization
by p10ns11yFission engine for token-efficient coding (JS, TS, Node.js, Rust, Python, ML/AI). Pruning, compression, strict token budgeting. Pair with fusion-sage for synthesis and surplus. Trigger on implementation, debugging, refactoring; hand off architecture to fusion-sage.
agentic-reactor
by p10ns11yOverarching patterns for self-guarded, pause-aware, agent-driven desktop/web platforms. Combines finder-reactor, Tauri shell, X resources, and CV guard into a cohesive autonomous system with intervention only on guards. Use for high-level autonomy design, meta-improvement loops, MCP composability. Fusion for the reactor as living system; fission for specific guards.
ai-optimization
by p10ns11yFission engine for token-efficient coding (JS, TS, Node.js, Rust, Python, ML/AI). Pruning, compression, strict token budgeting. Pair with fusion-sage for synthesis and surplus. Trigger on implementation, debugging, refactoring; hand off architecture to fusion-sage.
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.