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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rd-initiation-review
by patsnap研发项目立项预审与提案审查，用于立项通过/否决决策、公开新颖性边界审查、 创新点评估及有据可查的项目评级。当用户要求进行项目立项预审、立项评审、 提案审查、研发项目评估、提案包审查、新颖性预查、创新点评审、项目评级， 或希望围绕具体项目、提案或研究包材料集进行正式评审时使用—— 即使用户仅提供提案而未明确说明"评审"。
triz-innovation-pro
by patsnapTRIZ innovation solution analysis assistant. Supports innovative solution analysis through TRIZ causal chain methodology. Core capabilities: system component analysis, contact relationship analysis, functional modeling, causal chain analysis, and innovation solution generation.
alloy-composition-search
by patsnapGenerate professional alloy composition search responses by interpreting user queries, retrieving and analyzing relevant alloy data (optionally via MCP tools), and presenting structured composition tables with clear filtering, classification, and insights.
scout-tech-landscape
by patsnapUse for only materials-related queries requiring technology landscape analysis. Focus on any materials relevant topics such as metallic alloys, polymers, ceramics, composites, and related processing technologies. Generate structured insights on material classes, supply chains, key organizations, and R&D trends based on patents, literature, and industrial data.
solve-tech-problems
by patsnapUse for solving materials engineering problems only. Generate solution-oriented responses for materials engineering problems by analyzing material composition, microstructure, processing conditions, and performance behavior. Provide practical solutions such as alloy design, heat treatment adjustments, defect mitigation, and material substitution, with clear evaluation of trade-offs in properties, manufacturability, cost, and operating conditions.
tech-to-product
by patsnapUse for queries only about translating materials or material science technologies into real-world products. Generate application-focused responses that translate materials and engineering technologies into real-world products, with emphasis on material selection, processing methods, performance requirements, and integration into functional systems under practical manufacturing and operational constraints.
understand-technology
by patsnapUse for explaining materials science and engineering concepts, including material properties, structure–property relationships, processing methods, and performance mechanisms. Generate structured explanations of materials and engineering technologies by covering underlying scientific principles, structure–property relationships, processing methods, and performance characteristics, with clear connections between material composition, microstructure, and functional behavior in practical applications. Use for queries such as "What is X?", "How does X work?", or "Explain the science behind X".
precision-oncology-zhcn
by patsnap综合学术文献、流行病学报告、临床与药物指南及临床试验报告，提供关于癌症及其治疗的报告。 基于癌变机制进行详细的分子生物学和组织学分析。 当查询涉及以下内容时加载本技能： - 癌症或肿瘤 - 癌变机制 - 癌症或肿瘤的治疗 典型查询 - 乳腺癌是如何发生的？ - 白血病的一线和二线治疗 - CAR-T 疗法治疗胰腺癌的进展 - 亚洲结直肠癌的发病率和患病率 - 胶质母细胞瘤治疗中有哪些未满足的医疗需求？
pharmaceuticals-exploration-zhcn
by patsnap用于回答药物相关问题。对于早期药物，搜索并汇总相关专利、学术文献、数据库记录、临床试验、专利和授权交易文件来回答问题。 当用户明确提及特定药物时激活，或在调用 disease_investigation_skill 或 target_intelligence_skill 时作为辅助： - 指定输出某药物的特征或其他记录 - 搜索与特定疾病相关的药物 - 搜索靶向特定靶点的药物 典型查询 - 请告诉我靶向 GLP-1R 治疗糖尿病的司美格鲁肽 - 瑞德西韦是什么药？ - 用于治疗乙型肝炎的药物 - ALN-F12
pharmaceuticals-exploration
by patsnapUsed for answering drug-related questions. For early-stage drugs, search and summarize related patents, academic literature, database records, clinical trials, patents, and licensing transaction documents to answer questions. Activate when users explicitly mention specific drugs or when calling disease_investigation_skill or target_intelligence_skill for assistance - Specify output of a drug's characteristics or other records - Search for drugs related to a specific disease - Search for drugs targeting a specific target Typical queries - Please tell me about semaglutide targeting GLP-1R for diabetes treatment - What drug is remdesivir? - Drugs used to treat hepatitis B - ALN-F12
disease-investigation
by patsnapConduct comprehensive disease investigation combining academic literature, epidemiological data, clinical guidelines, pharmaceutical intelligence, and clinical trial reports. Users may inquire about disease pathogenesis, symptoms, pharmaceutical interventions, treatment options, patent landscapes, and business development opportunities. Load the skill when queries involve: - Disease pathology and molecular mechanisms - Regional disease incidence and subtypes - Clinical symptoms and diagnostic indicators - Treatment landscape and drug development pipeline - Patent and IP analysis for therapeutic areas - Business development and deal intelligence Typical queries - Pathogenesis of NSCLC - Treatment options for influenza - Incidence rates of leukemia in China - Clinical manifestations of depression - PD-1/PD-L1 patent landscape - Drug development pipeline for NSCLC
rd-initiation-review
by patsnapR&D project initiation pre-screen and proposal audit for go/no-go decisions, public novelty boundary review, innovation-point assessment, and evidence-backed project rating. Use when the user asks for project initiation pre-screening, initiation review, proposal review, R&D project evaluation, proposal-package review, novelty pre-screening, innovation-point review, project rating, or wants a formal review around a concrete project, proposal, or research-package material set — even if they only provide the proposal and do not explicitly say "review".
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.