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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charls-reproduce
by xjtulycCHARLS (China Health and Retirement Longitudinal Study) database-specific knowledge for reproducing published papers. Use when reproducing or analyzing papers that use CHARLS data, including variable mapping from harmonized to raw questionnaire items, cognitive function scoring (episodic memory, mental status, TICS), CESD-10 depression screening, social isolation index construction, and chronic disease coding. Also use for any CHARLS data cleaning, variable construction, or cohort selection task.
biomed-dispatch
by xjtulycDispatch biomedical research and data analysis tasks to Claude Code with K-Dense Scientific Skills. Use this skill when the user asks to run any bioinformatics, genomics, drug discovery, clinical data analysis, proteomics, multi-omics, medical imaging, or scientific computation task. Also use for literature search (PubMed, bioRxiv), pathway analysis, protein structure prediction, or scientific writing tasks.
cjk-viz
by xjtulycCJK (中日韩) 字体检测与 matplotlib 配置。任何涉及中文标签、标题、图例的 可视化任务启动前必须先执行本 skill 的字体检测流程，确保不会出现方块乱码。 适用于 matplotlib / seaborn / plotly 静态导出等场景。
feishu-rich-card
by xjtulycSend rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.
paper-reproduce
by xjtulycSystematic methodology for reproducing published academic papers using provided data. Use when the user asks to reproduce, replicate, or verify results from a published paper, including sample selection, descriptive statistics, regression analyses, and generating reproduction reports (Markdown + LaTeX PDF). Covers the full pipeline: data exploration, variable identification/mapping, sample filtering, variable construction, statistical analysis, result comparison, and documentation. Applicable to any observational study, clinical cohort, or survey-based research paper.
svg-ui-templates
by xjtulycGenerate professional SVG UI panels for structured information display. Use when presenting lists, task checklists, pipeline/dependency status diagrams, or rich-text report layouts as SVG images. Covers four templates - list-panel, checklist-panel, pipeline-status, richtext-layout. Style is professional, business-oriented, academic-grade with Material Design color palette.
xarray-netcdf
by xjtulycLabeled multi-dimensional array analysis with xarray: NetCDF/HDF5 I/O, lazy Dask loading, rechunking, Zarr stores, and CF conventions.
clinical-assessment
by xjtulycUse this Skill to score and interpret clinical scales: PHQ-9, GAD-7, PCL-5, reliable change index (RCI), norm comparison, and longitudinal change visualization.
obspy-seismology
by xjtulycSeismological data analysis with ObsPy — FDSN waveform download, response removal, phase picking, moment tensor inversion, and seismicity mapping.
ocean-data
by xjtulycDownload and analyze oceanographic data from Copernicus Marine Service and Argo floats using copernicusmarine, gsw, and xarray.
panel-data
by xjtulycPanel data econometrics with Python linearmodels; covers pooled OLS, fixed/random effects, Hausman test, clustered SE, Arellano-Bond GMM, and regression tables.
cantera-combustion
by xjtulycUse this Skill for combustion simulations with Cantera: mechanism loading, freely propagating flames, ignition delay, reactors, and sensitivity analysis.
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.