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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material-density-volume-calculation
by SpectrAI-InitiativeCalculate material density and volume from mass and geometric dimensions for materials mechanics analysis.
polymer-property-analysis
by SpectrAI-InitiativePolymer & Material Property Analysis - Analyze polymer properties: composition, symmetry, density, and lattice parameters for material design. Use this skill for polymer science tasks involving MaterialCompositionAnalyzer CalculateSymmetry CalculateDensity MofLattice. Combines 4 tools from 2 SCP server(s).
iron-and-steel-processes
by TibsfoxIndustrial production of iron and steel from ore to finished product — blast furnace, Bessemer converter, open-hearth, basic oxygen, electric arc, continuous casting, rolling, and secondary metallurgy. Covers the chemistry of decarburization, dephosphorization, and desulfurization, the metallurgy of plain carbon steels, the evolution from puddling and rolling to BOF and EAF, and the process-structure-property chain that makes mild steel the default structural material of the industrial era.
materials-characterization
by TibsfoxMeasurement techniques for identifying composition, microstructure, and properties of engineering materials — optical and electron microscopy, x-ray diffraction, EDS and WDS spectroscopy, thermal analysis, mechanical testing, and fractography. Covers when to use which technique, sample-preparation pitfalls, and how to build a characterization plan that answers a specific engineering question rather than accumulating data for its own sake.
materials-selection
by TibsfoxSystematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
nonferrous-alloys
by TibsfoxMetallurgy of the major nonferrous structural and engineering alloys — aluminum, copper, titanium, nickel, magnesium, and their strengthening mechanisms. Covers precipitation (age) hardening in Al-Cu and Al-Zn-Mg systems, solid-solution strengthening in brass and bronze, alpha-beta titanium, nickel superalloys with gamma-prime, and the trade-offs in density, strength, corrosion resistance, and cost that decide when to leave steel for a lighter or more specialized metal.
structural-failure-analysis
by TibsfoxDiagnosis of why structural materials fail — ductile overload, brittle cleavage, fatigue, creep, stress-corrosion cracking, hydrogen embrittlement, and corrosion. Covers Griffith fracture mechanics, the ductile-to-brittle transition, the S-N and Paris fatigue frameworks, fractographic interpretation, and the J.E. Gordon view that strength, stiffness, and toughness are distinct properties that must be understood together to prevent collapse.
comsol-materials
by iammm0COMSOL 材料系统使用要点与经验
select-print-material
by pjt222Choose 3D printing materials based on mechanical, thermal, and chemical requirements. Covers PLA, PETG, ABS, ASA, TPU, Nylon, and resin variants with property comparisons. Use to select material for parts with specific mechanical or thermal needs, choose for outdoor or chemical exposure, evaluate food-safe or biocompatible applications, balance printability vs. performance, or troubleshoot material-related print failures.
select-print-material
by pjt222Choose 3D printing materials based on mechanical, thermal, and chemical requirements. Covers PLA, PETG, ABS, ASA, TPU, Nylon, and resin variants with property comparisons. Use when selecting material for parts with specific mechanical or thermal requirements, choosing for outdoor or chemical exposure, evaluating food-safe or biocompatible applications, balancing printability vs. performance, or troubleshooting material-related print failures.
select-print-material
by pjt222Choose 3D print material → mechanical|thermal|chemical reqs. PLA, PETG, ABS, ASA, TPU, Nylon, resin variants w/ property compare. Use → select for specific reqs, outdoor|chemical exposure, food-safe|biocompat, balance printability vs perf, troubleshoot material-related fails.
select-print-material
by pjt222Choose 3D printing materials based on mechanical, thermal, and chemical requirements. Covers PLA, PETG, ABS, ASA, TPU, Nylon, and resin variants with property comparisons. Use when selecting material for parts with specific mechanical or thermal requirements, choosing for outdoor or chemical exposure, evaluating food-safe or biocompatible applications, balancing printability vs. performance, or troubleshooting material-related print failures.
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.