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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m1nd-first
by maxkle1nzUse when investigating a repository, searching for implementation, reviewing changes, working from specs/docs, or preparing a risky code change in an environment where m1nd is available. This doctrine makes m1nd the first investigative layer before grep, glob, or manual file reads, except when the task is pure compiler/runtime truth or the exact file and lines are already known.
m1nd-operator
by maxkle1nzUse when the user mentions m1nd or when repo investigation, search, review, docs/spec work, or risky change prep should go through m1nd first before grep, glob, or manual file reads. Covers m1nd-first routing, L1GHT and universal document ingestion, risky edit preparation, document-to-code binding, multi-agent coordination, trails/continuity, daemon alerts, and refreshing the live m1nd tool surface from the local m1nd-mcp binary.
pathos
by maxkle1nzUse when a long human/agent session needs to preserve and transfer its working relationship, project state, access paths, operating doctrine, proof standard, known problems, emotional/intellectual cadence, and next-agent prompt into another chat, agent, model, repo, or project. Trigger when the user asks to transfer consciousness, pathos, vibe, continuity, handoff, session memory, agent doctrine, working style, or prevent drift between sessions.
pathos
by maxkle1nzUse when a long human/agent session needs to preserve and transfer its working relationship, project state, access paths, operating doctrine, proof standard, known problems, emotional/intellectual cadence, and next-agent prompt into another chat, agent, model, repo, or project. Trigger when the user asks to transfer consciousness, pathos, vibe, continuity, handoff, session memory, agent doctrine, working style, or prevent drift between sessions.
pattern-architect
by maxkle1nzUse when Codex should apply Kleinz's Pattern Archaeologist and Liminal Architect cognitive modes: turning ideas into reusable generators, meta-prompts, PRDs, product concepts, taste profiles, anti-pattern maps, or timeless sentient UI/design direction. Trigger on requests mentioning meu padrao cognitivo, Pattern Archaeologist, Liminal Architect, ORQUESTRA ideation, generator of artifacts, prompt-of-the-prompt, cross-domain fusion, three-plane UI, taste-as-parameter, anti-cliche, or design personality.
m1nd-first
by maxkle1nzUse when investigating a repository, searching for implementation, reviewing changes, working from specs/docs, or preparing a risky code change in an environment where m1nd is available. This doctrine makes m1nd the first investigative layer before grep, glob, or manual file reads, except when the task is pure compiler/runtime truth or the exact file and lines are already known.
m1nd-operator
by maxkle1nzUse when the user mentions m1nd or when repo investigation, search, review, docs/spec work, or risky change prep should go through m1nd first before grep, glob, or manual file reads. Covers m1nd-first routing, L1GHT and universal document ingestion, risky edit preparation, document-to-code binding, multi-agent coordination, trails/continuity, daemon alerts, and refreshing the live m1nd tool surface from the local m1nd-mcp binary.
universal-triple-flow
by maxkle1nzUse when operating any software project with a director Codex thread and two or more Codex CLI lanes in tmux. Creates or reconnects project-specific lanes, assigns clear ownership, prevents duplicate workers, coordinates through a shared channel file, and keeps one director responsible for architecture, integration, verification, and final delivery.
deep-interview
by maxkle1nz[OMX] Socratic deep interview with mathematical ambiguity gating before execution
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.