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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reimagine-industry
by Agent-Engineer-MasterProduces a ranked shortlist of venture concepts for disrupting an industry via a 6-phase workflow (Industry Deconstruction → Value Chain Pain Audit → Enabling Conditions Scan + capability seeds → Framework Application → Idea Generation via eight structural moves → Stress Test). Runs two generation lanes: an incumbent-anchored lane (Blue Ocean ERRC, Aggregation, Decoupling, Counter-positioning) and a first-principles lane (Phase 3 capability seeds + Move 8 capability-first + Thiel's Secrets reframed as testable bets, never truth-gated). The incumbent:capability mix is allocated per-industry from Phase 1-3 signals and ratified by the human at Gate 2 (not hardcoded), with a ≥1-per-lane floor. Every concept ships as a bet (load-bearing hypothesis + cheapest validation test); the human gate is test-worthiness, not conviction. Built against a disruption-dataset.yaml from inherited analyze-industry outputs or fresh librarian research. Three approval gates. Reimagination-specific bar test (≥3 non-obvious concepts, ze
morning-brief
by Agent-Engineer-MasterGenerates a structured morning brief and writes it to the daily review file. Pulls from: current priorities (goals summary), active task board, latest competitor digest, and content pipeline status. Fires automatically on weekdays. Output is the morning intention section of your daily review file.
analyze-industry
by Agent-Engineer-MasterProduces a senior-analyst industry-structure brief by orchestrating MBB sub-skills (BCG Strategy Palette, McKinsey G3 sizing, Porter Five Forces with complementors and AI-as-force, Bain profit pools on Porter value chain; Phase 2 adds arenas, S-curve, Helmer 7 Powers, JTBD). Three-layer output to 08-knowledge/world-model/industries/[slug]/. Quick or Deep mode. Three approval gates. Fresh-context bar test. V/C/A/I provenance. Pyramid + SCQA synthesis shipped as a quality-reviewed HTML report (rendered via html-output, audited by analysis-quality-review). Concludes with where-to-play/how-to-win. Triggers on 'analyze the [industry] industry', 'industry brief for [industry]', 'is [industry] structurally attractive', 'industry analysis for [PE deal/market entry]', 'industry-structure analysis'. Do NOT activate for single-company analysis (use build-company-model), DTC category go/no-go (use assess-category), decision pressure-testing (use stress-test), or buyer/investor universe (use building-buyer-shortlists).
size-market
by Agent-Engineer-MasterProduces a granular market sizing for a defined industry/sub-segment using McKinsey G3 granular-growth decomposition + arenas qualification screen, with top-down and bottom-up triangulation. Output: market-sizing.md with TAM/SAM/SOM, ≥3 sub-segment growth-rate decomposition, explicit de-averaging statement, and V/C/A/I provenance tags on every numeric claim. Sized to PE-CDD speed. Triggers on 'size the [industry] market', 'TAM SAM SOM for [industry]', 'how big is the [industry] market', 'market sizing for [industry]', 'granular market sizing'. Sub-skill of analyze-industry but invocable standalone. Do NOT activate for single-company revenue forecasting (use build-company-model), customer-segment demand analysis (use analyze-demand), or competitive share analysis (use map-competitive-arena).
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.