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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managing-radiation-dose
by CaseMarkTracks and optimizes radiation exposure using reference levels and ALARA principles. Use when monitoring radiation dose, optimizing CT protocols, or documenting dose reduction efforts.
tracking-incidental-findings
by CaseMarkManages incidental finding follow-up using ACR White Paper recommendations. Use when tracking incidentalomas, scheduling follow-up imaging, or managing unexpected findings.
image-quality-audit
by aizechAssesses medical image quality against clinical standards and identifies optimization opportunities. Use when user mentions "image quality audit", "artifact review", "dose analysis", "protocol deviation", "quality metrics", "diagnostic adequacy", or "technique optimization".
ai-detection-pipeline
by aizechIntegrate AI detection into PACS workflow. Also use when setting up, configuring, or optimizing AI detection systems for medical imaging. Also covers Aidoc, Nvidia Clara, Zebra Medical, MaxQ AI, and Qure AI integration.
image-quality-audit
by aizechAssesses medical image quality against clinical standards and identifies optimization opportunities. Use when user mentions "image quality audit", "artifact review", "dose analysis", "protocol deviation", "quality metrics", "diagnostic adequacy", or "technique optimization".
modality-detection
by aizechAuto-detect imaging modality (CT, MRI, X-ray, US, etc.) from user input, DICOM file headers, or file analysis. Also use when the user mentions "what modality", "detect from file", "identify imaging type", or needs to classify imaging studies. For PACS queries, see pacs-workflow.
pacs-workflow
by aizechQuery PACS, retrieve studies, manage worklists, and integrate with PACS workflows. Also use when the user needs to interact with picture archiving systems, search for imaging studies, retrieve DICOM data, or manage radiologist worklists. For DICOMweb REST queries, see dicom-web-query.
dicos-blender-bag-gen
by jpfieldingGenerate 3D CT scan visualizations of bags and personal items in airport screening trays using Blender MCP. Supports varied container types (carry-on suitcases, backpacks, purses, laptop bags, duffel bags) and loose tray items (watches, phones, laptops, belts, shoes). Creates realistic randomized packing with CT density-based materials, and optionally voxelizes to raw volume data for DICOS export. Use when the user asks to: create a bag scan, generate a CT bag, build a screening scene, make a bag in Blender, simulate an airport X-ray/CT scan, add items to a bag, voxelize a Blender scene, or generate screening training data. Requires Blender MCP connection.
featurej-documentation
by LJMedPhysFeatureJ is a Fiji/ImageJ plugin suite for multi-scale differential feature extraction in grayscale images. This skill documents the validated Groovy automation path through the installed ImageScience classes for Derivatives, Edges, Laplacian, Hessian, Structure, and Statistics, plus the verified FeatureJ menu surface and a parameterized workflow script that saves TIFF and CSV outputs.
biomedical-imaging-expert
by luokai25Expert-level biomedical imaging covering X-ray, CT, MRI, ultrasound, nuclear medicine, optical imaging, and image processing for clinical and research applications.
pydicom
by luokai25Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
dicom-mri
by yamz8Analyze medical DICOM imaging data (MRI, CT, X-ray) from any source — a CD/DVD, USB drive, or local directory. Use this skill whenever the user mentions an MRI scan, CT scan, DICOM files, a medical CD, a radiology disc, or wants to understand, view, or report on any medical imaging study. The skill handles the full pipeline: discovering the data, rendering slice images, analyzing findings across all sequences, and generating structured reports in plain or clinical language. Invoke this skill proactively if the user mentions any of: "MRI", "CT scan", "DICOM", "radiology CD", "medical scan", "shoulder MRI", "brain scan", "x-ray disc", "my doctor gave me a CD", "scan results", or similar.
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.