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...
emergency-distress-signal
by ComeOnOliver紧急求救 - 飞船坠毁后与Stella的首次联系,帮助她评估损伤并制定初步生存计划
threat-assessment
by nimbusxrAssess route and position risk by combining hostile positions, terrain, and line-of-sight analysis
start-commander
by deadl1f7Use when: start commander, begin tactical session, activate AI commander, command red forces, command blue forces, tactical AI setup, commander session, start tactical AI, run commander, initialize commander, /commander
structs-combat
by AgentWorkers在结构体（Structs）中执行战斗操作，包括发起攻击、进行突袭、部署防御措施以及进行隐蔽行动。适用于攻击敌方结构体、为获取矿石而突袭行星、部署防御部队、激活隐蔽模式、调动舰队进行突袭，或为即将到来的攻击做准备。突袭行动需要舰队的移动以及后台的计算资源（PoW compute）。
joint-denied-terrain-avalanche-route-and-rescue-cell
by zwright8Manage avalanche-threatened mobility corridors and rescue operations in denied mountain terrain. Use when convoy, patrol, or medevac routes face dynamic avalanche risk and degraded communications.
variable-sword
by HmbownUse Variable Sword when you know something needs to be cut but the attack pattern — narrow precision, wide sweep, or long reach — should be determined by the shape of the target at execution time.
wood-aura
by HmbownUse Wood Aura when the operator can afford to hold ground, slow the exchange slightly, and benefit from a protective layer that regains strength if the field stays stable. Wood Aura is a regenerative defense: it favors disciplined positioning, recoverable processes, and stable footing over speed. Instead of merely eating one hit, it encourages Hermes to root the work in dependable ground, recover after contact, and outlast pressure through steady reconstitution.
vulcan
by HmbownUse Vulcan when the right move is sustained low-friction pressure: a tight burst of repeated actions that can confirm progress, expose resistance, or suppress one stubborn target without committing to a larger restructure.
mission-recovery
by Loner1024Internal skill for recovering mission execution after interruption or failure.
ai-enabled-red-teaming-cell
by zwright8Support adversary-emulation and red-team analysis to stress-test plans and assumptions. Use when evaluating vulnerabilities, deception opportunities, and branch outcomes.
battlefield-drone-forensics-and-attribution-cell
by zwright8Exploit recovered or downed drones for attribution, supply-chain signatures, and rapid countermeasure updates. Use when drone incidents require evidence-grade technical exploitation.
battlefield-forensics-site-exploitation-cell
by zwright8Coordinate tactical site exploitation and battlefield forensic workflows to support targeting, attribution, and prosecution-quality evidence handling.
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.