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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canonical-pipeline
by EmasoftStandard files, CI/CD, hooks, and release pipeline for Emasoft Claude Code plugins. Use when creating or auditing plugin repos. Used dynamically via the-skills-menu — any CPV agent can invoke at runtime (TRDD-478d9687).
deterministic-codemod
by EmasoftDeterministic codemod CLI — bulk-fix backtick-path to markdown-link, add TOC stubs, dedup blank lines, and other mechanical text transforms (issue 17). Zero LLM cost. Use when mechanical fixes outnumber semantic ones. Used dynamically via the-skills-menu (TRDD-478d9687).
v2-1-80-demo-skill
by EmasoftDemonstrates v2.1.80-plus features end to end - userConfig substitutions, CLAUDE_PLUGIN_OPTION env vars, and the v2.1.98 plugin-skill name field. Use when the user wants to see every Claude Code v2.1.80-plus feature exercised in one place. Trigger with phrases like "show v2.1.80 features", "demo userConfig", or "exercise the demo plugin".
github-elements-tracking
by EmasoftThis skill should be used when the user asks to "track work across sessions", "create an epic", "manage issue waves", "post a checkpoint", "claim an issue", "recover from compaction", "coordinate multiple agents", "update memory bank", "store large documents", or mentions GitHub Issues as persistent memory, multi-session work, context survival, agent collaboration, SERENA MCP memory, or project-level context. Provides complete protocols for using GitHub Issues as permanent memory that survives context exhaustion, with integrated SERENA MCP memory bank for project-level context and large document storage.
amcos-failure-notification-refa
by EmasoftUse when consulting detailed failure notification references. Trigger with failure notification lookups. Loaded by ai-maestro-chief-of-staff-main-agent
huggingface-local-models
by EmasoftSelect GGUF artifacts and quantizations for llama.cpp on CPU, Mac Metal, CUDA, or ROCm runtimes. Covers Q4_K_M vs Q5_K_M vs Q6_K trade-offs, llama-server launch flags, --hf-repo/--hf-file fallback for non-standard naming, and conversion from Transformers weights when no GGUF exists. Use when the user picks llama.cpp / LM Studio / Ollama on non-Apple-Silicon platforms. Loaded by llm-externalizer-setup-agent.
amcos-failure-notification
by EmasoftUse when sending failure notifications after operation errors. Trigger with failure events, error reporting, or incident notification. Loaded by ai-maestro-chief-of-staff-main-agent
janitor-skill-bundle-audit
by EmasoftOn-demand security audit of every agent-context bundle in the current project — SKILL.md / CLAUDE.md / AGENTS.md / .cursorrules / .aider.conf.yml / .claude/* / .mcp.json. Detects multilingual prompt-injection, HTML-comment authority impersonation, base-URL override, cross-skill shadowing, dynamic exec in skill bodies, git-hook install directives, exfil webhook sinks, and known-secret-path references. Trigger with /janitor-skill-bundle-audit, "scan my skills for prompt injection", "audit CLAUDE.md for attacks", or "check .cursorrules for hijack patterns".
janitor-pause
by EmasoftSuppresses ai-maestro-janitor heartbeat output without removing the cron. Use when starting a large refactor, doing focused exploration, or any block of work where drift nudges would be noise. Trigger with /janitor-pause, "pause the janitor", "silence the janitor for 2h", or "quiet the heartbeat for the rest of today".
vmlx-setup
by EmasoftInstall, set up, and configure the vMLX backend — an MLX-native inference server for Apple Silicon (jjang-ai/vmlx) exposing an OpenAI/Anthropic/Ollama compatible API. Use when the user wants MLX-native serving on an Apple Silicon Mac, says "set up vmlx", "install vmlx", "mlx inference server", "run mlx-community models", or the llm-externalizer setup wizard picks vMLX as the macOS backend. Trigger with /vmlx-setup or "set up vmlx". Apple Silicon (M1/M2/M3/M4) ONLY.
janitor-auto-manage-oauth-on
by EmasoftOpts THIS machine into the janitor's unattended multi-account OAuth rotator. Sets an opt-in flag the always-on janitor daemon's 60s oauth-rotator-tick Task reads to rotate the live Claude Code credential to an alternate paid account before a rate-limit 429 stalls an overnight session. Default OFF, macOS only, idempotent; refuses if a pinning env var would defeat rotation. Trigger with /janitor-auto-manage-oauth-on, "turn on oauth rotation", or "enable account rotator".
amcos-post-op-notification-ref
by EmasoftUse when consulting detailed post op notification references. Trigger with post op notification lookups. Loaded by ai-maestro-chief-of-staff-main-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.