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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manufacturing-knowledge
by MRiabovTechnical specifications, material properties, and cost models for CNC Milling and Injection Molding. Use this when the task specifies a 'max_unit_cost' or 'target_quantity', or when planning for specific manufacturing processes.
electromechanics-syntax
by MRiabovLocal authoring syntax for motorized moving parts and future electromechanics sections. Use when editing `assembly_definition.yaml`, `final_assembly`, `moving_parts`, `AssemblyPartConfig.control`, or related motion syntax in planner and solution handoffs.
electronics-engineering
by MRiabovElectromechanical planning and runtime guidance for the current Problemologist electronics stack. Use only when a plan or implementation explicitly includes an `electronics` section, benchmark `electronics_requirements`, circuit validation, electronics review, or simulation evidence for an electromechanical system.
build123d-technical-drawing
by MRiabovAuthor and review build123d technical drawings with `TechnicalDrawing`, projected orthographic views, dimensioning, line layering, and SVG/DXF export. Use when creating or editing drawing scripts, orthographic sheets, annotations, or `render_technical_drawing()` artifacts grounded in an existing 3D build123d model.
cots-parts
by MRiabovCatalog-backed COTS part modeling and validation guidance for Problemologist. Use when a task involves motors, off-the-shelf parts, benchmark fixtures, or translating declared COTS components into build123d geometry and handoff artifacts.
engineer-planner
by MRiabovEngineering planning role for turning benchmark handoff context into implementation-ready plan artifacts. Use when drafting or revising `engineering_plan.md`, `todo.md`, `benchmark_definition.yaml`, `assembly_definition.yaml`, `solution_plan_evidence_script.py`, or `solution_plan_technical_drawing_script.py`; when interpreting `benchmark_assembly_definition.yaml` and `benchmark_script.py` as read-only context; when validating cost, weight, motion contracts, detailed payload trajectory calculations, build-zone feasibility, or exact-grounded inventory mentions; when using `render_technical_drawing()` or media inspection to check planner drafts; when inspecting simulation evidence through frame-indexed `objects.parquet` sidecars; or when deciding whether a proposed engineering approach is infeasible and needs replanning.
mechanical-engineering
by MRiabovMechanical mechanism design, friction-aware passive transfer, and simulation guidance for Problemologist. Use this when solving or reviewing passive-transfer mechanisms, realistic constraints/DOFs, manufacturing-config material coefficients, stress or fluid tasks, or when a benchmark/solution needs concrete mechanical design patterns instead of prompt-specific hints.
engineer-plan-reviewer
by MRiabovEngineer-side review workflow for validating plan and execution handoffs, review manifests, render and simulation evidence, exact inventory grounding, formula-backed motion and payload trajectory derivations, motion-contract plausibility, plan refusals, and stage-scoped review YAML outputs. Use when reviewing engineering `engineering_plan.md`, `todo.md`, `benchmark_definition.yaml`, `assembly_definition.yaml`, `solution_script.py`, validation or simulation artifacts, review manifests, or refusal evidence for the Engineering Plan Reviewer or Engineering Execution Reviewer roles; when inspecting simulation evidence through frame-indexed `objects.parquet` sidecars; or when applying the engineer review checklist for plan, execution, and refusal gates.
run-experiments
by MRiabovRun IntersectionQA and IntersectionEdit experiments in this repository, including local preflight checks, dataset/config selection, experiment-suite orchestration, Vast.ai GPU instance selection, remote bootstrap, SFT/GRPO launch and monitoring, stop rules, artifact preservation, and experiment-record updates. Use when Codex is asked to prepare, launch, resume, debug, monitor, or document training/evaluation experiments for this repo.
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.