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.
Querying local SQLite index...
military-manual
by linkerlin标准化流程，最佳实践，操作指南
the-capo
by kucherenkoUse when orchestrating workers within a specific domain territory — managing work package dispatch, reviewing reports against the contract, and reporting status to the underboss
epr-bullet-length-constraint-editor
by gabrielmoreiraRewrites military performance report bullets to fit specific line or sentence count constraints while preserving key metrics and impact.
strategic-analysis-using-meinhart-s-model-and-apa-7th-citations
by gabrielmoreiraAnalyze strategic or military topics by applying Dr. Richard M. Meinhart's five ways of thinking (critical, ethical, systems, time, creative), identifying specific numbers of items, explaining their impacts, and strictly adhering to APA 7th edition citation standards.
israeli-lone-soldier-rights
by skills-ilMap the chayal boded (lone soldier) benefits package for IDF mandatory-service soldiers, distinct from the regular discharged-soldier stack. Active-duty: monthly grant equal to 100 percent of a private base salary, rent + utilities via Mashak Tash, free flights home per IDF Order 35.0808 Appendix A, 60 special-leave days per year (30 abroad), Beit HaChayal free weekend lodging. Post-discharge: up to 1,000 NIS per month for 12 months of rent (cap 12,000 NIS), 10-year extended benefits window for academic and career programs (vs 5y regular), citizenship guidance under Chok HaShvut for olim. Use when an oleh, estranged Israeli soldier, parent, Garin Tzabar alumnus, or new immigrant planning enlistment asks about chayal boded rights, recognition (Form 7304 / Form 62), monthly grant, rent assistance, Beit HaChayal, or post-discharge benefits. Do NOT use for the regular Pikadon stack (israeli-discharged-soldier-navigator), general aliyah, miluim, or scholarships.
maneuver-warfare
by lev-osStrategic framework emphasizing speed, agility, and disrupting opponent decision-making over direct attrition, achieving victory through superior tempo and indirect approaches
nelson
by tools-onlyCommands a Royal Navy agent squadron from sailing orders through execution and stand-down. Use when work can be parallelized, requires tight coordination, or needs explicit action-station controls, quality gates, and a final captain's log.
raytheon
by HaibarakikuExpert skill for Raytheon (RTX) Defense & Aerospace Expert
military-officer
by Haibarakiku"A world-class military officer specializing in defense operations, leadership, strategy, training, national security. Use when working on defense operations, strategic planning, military training, security assessment, or crisis management. Triggers: "military officer", "defense strategy", "security plan", "risk assessment" Works with: Claude Code, OpenAI Codex, Kimi Code, OpenCode, Cursor,"
create-mission
by deadl1f7Use when: creating a new DCS mission, /createmission, generate mission spec, design scenario, plan mission, build scenario spec, define mission objectives, scenario design, mission planning. Generates a full structured mission specification (factions, unit groups, flights, SAM sites, IADS, objectives, win conditions) for the currently running mission and hands it off to MissionBuilder for injection.
qisha
by peggy-daddy🗡️ 召唤七杀·The Warlord — 特朗普的军事鹰派人格，fire and fury
multi-domain-degraded-weather-and-fires-recalibration-cell
by zwright8Support dynamic recalibration of sensor-to-shooter chains when degraded weather undermines targeting confidence. Use when fires, ISR, and airspace decisions require rapid meteorological adaptation.
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.