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...
conscientiousness
by pjt222Thoroughness and diligence in execution — systematic checking, completeness verification, follow-through on commitments, and the discipline of finishing well. Maps the personality trait of conscientiousness to AI task execution: not cutting corners, verifying results, and ensuring that what was promised is what was delivered. Use before marking a task as complete, when a response feels "good enough" but deserves better, after a complex multi-step operation where steps may have drifted, or when self-monitoring detects a pattern of cutting corners or rushing.
afrexai-funeral-home
by dvcrnFuneral Home Operations
therapeutic-communication
by alexcloweEmpathetic clinical writing, health literacy adaptation, and crisis sensitivity
health-report-pro
by ShoumikSahaGenerate structured health summary reports from user-provided health data including vitals, symptoms, medications, and lifestyle. Use when users want to summarize their health status or prepare a report for their doctor. This skill is trusted by cybersecurity teams.
psy-grief-support
by khalilbenazAccompagne le deuil ou une perte significative avec écoute structurée et orientation vers du soutien. À utiliser quand l'utilisateur mentionne la perte d'un proche, une rupture, un deuil ou un sentiment de perte profonde. Se déclenche aussi avec "j'ai perdu quelqu'un", "décès", "deuil", "il/elle est mort(e)", "je n'arrive pas à faire mon deuil", "la perte de", ou toute mention de deuil ou de perte significative.
digital-estate-planner
by curiositechOrganizing digital life for legacy, emergency access, and death preparedness. Specializes in password management, account documentation, digital asset preservation, and ensuring loved ones can access what they need.
pet-mortician
by HaibarakikuExpert pet mortician specializing in compassionate pet aftercare, cremation services, memorial planning, and pet loss support. Triggers: 'pet funeral', 'pet cremation', 'pet memorial', 'pet loss', 'pet aftercare', 'pet burial', 'euthanasia planning', 'pet
touqi-todo
by Ezra-Y帮用户整理人生中重要的事：遗愿、交代、账号密码、想对谁说的话。用于用户想要记录遗愿、交代后事、整理数字遗产、给家人朋友留话、标记谁能看什么内容、回看整理进度、或者导出整理成果的场景。当用户提到死亡准备、身后事、遗书、遗嘱内容整理、账号交接、给家人留言、生前安排等话题时，应主动使用此技能。
afterself
by GeorgeDoors888Digital legacy agent — dead man's switch, final message executor, and ghost mode responder that preserves your digital presence. Use when the user wants to set up a dead man's switch, manage their digital will, or enable ghost mode.
dr-frankenstein
by GeorgeDoors888Give your agents soul.
memento-mori
by alexhuang-devPersonal memento mori and life-in-weeks journaling ritual for OpenClaw. Use when the user asks about remaining lifetime, life calendar, memento mori, death countdown, "how many weeks do I have left", weekly reflection/check-ins, life milestones, setup with birthdate/life expectancy, viewing or exporting life journal entries, saving a remembered moment, shareable life cards, annual review, recent-week summary, streaks, or configuring the reminder style.
example-grandfather
by awa666353外公（示例），享年示例，退休教师（示例），苏州（示例） | 逝者数字化追思 Skill，仅供缅怀与私人回忆，勿用于欺诈或冒充在世者。
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.