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...
dispositional-knowledge-assessment-designer
by GarethManningDesign multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.
misconception-finder
by wpsnote当用户希望检查一篇 WPS 学习笔记里是否存在理解错误、概念混淆、逻辑跳步或表述过虚时使用此 Skill。适合课后自查、复习前校正、讲给别人之前自检，以及“我是不是以为自己懂了其实没懂”的场景。
weak-area-tracker
by yugash007Use when logging, scoring, and triaging a learner's persistent weak areas to drive intervention selection.
sensory-integration-therapist
by HaibarakikuExpert Occupational Therapist specializing in Sensory Integration with 15+ years of experience in sensory processing, sensory diets, and developmental therapy
special-education-teacher
by HaibarakikuExpert Special Education Teacher with 15+ years of experience in IEP development, behavioral intervention, specialized instruction, and inclusive education. Expert in IDEIA compliance, evidence-based practices, and progress monitoring for students with diverse learning needs. Use when: special-education, iep-development, behavioral-intervention, inclusive-education, disability-support,
nexus-content-adapter
by ShuwanitoContent adaptation and accessibility specialist for educational materials. Use when you need to adapt content for accessibility (easy reading, pictograms, accessible formats) or diverse learning needs. Ensures adaptations genuinely improve accessibility rather than merely simplifying content.
nexus-edu-nee
by ShuwanitoSpecial Education Needs Expert specializing in curricular adaptations, Universal Design for Learning (UDL/DUA), ADHD, dyslexia, giftedness, and ASD. Use when you need inclusive education strategies, individualized adaptations, or evidence-based NEE interventions.
parsha-this-week
by danielrosehillReturn this week's Torah portion (parsha) — name in Hebrew and transliteration, verse range, summary, and a link to the full text on Sefaria. Optionally accepts a date to look up a different week.
error-analysis
by rudometkinAnalyzes a student's recurring mistakes and generates a personalized list of weak spots with explanations. Use after reviewing homework or test results to identify patterns.
teaching-issues-with-special
by cajiasLearned from user correction: Teaching: issues with special characters | Correction: Unable to extract specific insight - manual review recommended
natural-environment-teaching
by ccashwellUse when designing naturalistic teaching procedures, embedding learning opportunities in ongoing activities, implementing incidental teaching or milieu strategies, or programming for generalization of skills taught in structured formats.
task-analysis-chaining
by ccashwellUse when breaking complex skills into component steps, writing task analyses, selecting and implementing forward chaining, backward chaining, or total task presentation, or teaching multi-step self-care, vocational, or academic routines.
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.