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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pathology-report-checker
by patolojiAIAnalyzes surgical pathology cancer reports for compliance with CAP (College of American Pathologists) and ICCR (International Collaboration on Cancer Reporting) guidelines. Validates pTNM staging (AJCC 8th edition), checks required element completeness with severity-based scoring, generates blank or pre-filled CAP synoptic templates, creates 3-5 line tumor board summaries, suggests SNOMED CT and ICD-O-3 codes, converts free-text narratives to structured synoptic format, and drafts amendments/addenda. Use when the user pastes or uploads a pathology report (txt, pdf, docx, image, xlsx) and asks to "check CAP compliance", "verify staging", "generate synoptic template", "tumor board summary", "convert to synoptic", "calculate TNM stage", or "code with SNOMED" for breast invasive carcinoma, colorectal resection, exocrine pancreas carcinoma, or gastric carcinoma. Supports English and Turkish. Do NOT use for non-cancer pathology (inflammatory, infectious), cytology, or unsupported tumor types.
pathology-template-generator
by patolojiAIGenerates blank or pre-filled CAP-compliant synoptic pathology report templates with every required data element listed. Use when the user asks to "generate a synoptic template", "create a CAP report skeleton", "give me a blank breast lumpectomy template", "Whipple specimen template", "pre-fill template with these findings", or any variant of synoptic template creation. Supports breast, colorectal, pancreas, and gastric carcinoma in English and Turkish (.txt, .md output).
breast-pathology-specialist
by patolojiAIComprehensive breast cancer pathology workflow combining CAP/ICCR compliance validation, synoptic template generation, TNM staging (AJCC 8th edition), biomarker reporting (ER/PR/HER2/Ki-67) per ASCO/CAP guidelines, tumor board summaries, and SNOMED coding for invasive breast carcinoma. Use when the user uploads or pastes a breast pathology report (mastectomy, lumpectomy, wide local excision, sentinel/axillary lymph node biopsy) and asks to "check breast report", "validate breast pathology", "stage this breast cancer", "interpret ER/PR/HER2", "generate breast template", or any breast-cancer-specific pathology task. Supports .pdf, .docx, .txt input in English and Turkish.
colorectal-pathology-specialist
by patolojiAIComprehensive colorectal cancer pathology workflow combining CAP/ICCR compliance validation, synoptic template generation, TNM staging (AJCC 8th edition), MSI/MMR testing interpretation, mesorectal excision quality (MERCURY) grading, tumor board summaries, and SNOMED coding. Use when the user uploads or pastes a colorectal pathology report (colectomy, low anterior resection, abdominoperineal resection, polypectomy) and asks to "check colorectal report", "validate CAP ColoRectal protocol", "stage this colorectal cancer", "interpret MSI/MMR", "assess TME quality", or any colorectal-cancer-specific pathology task. Supports .pdf, .docx, .txt input in English and Turkish.
gastric-pathology-specialist
by patolojiAIComprehensive gastric cancer pathology workflow combining CAP/ICCR compliance validation, synoptic template generation, TNM staging (AJCC 8th edition), Lauren classification (intestinal/diffuse/mixed), WHO histologic typing, HER2 testing interpretation per ASCO/CAP, MSI/MMR status, tumor board summaries, and SNOMED coding. Use when the user uploads or pastes a gastric pathology report (total/subtotal gastrectomy, esophagogastrectomy, endoscopic submucosal dissection) and asks to "check gastric report", "validate CAP Stomach protocol", "stage this gastric cancer", "interpret Lauren classification", "interpret HER2 IHC/FISH", or any gastric-cancer-specific pathology task. Supports .pdf, .docx, .txt input in English and Turkish.
pancreas-pathology-specialist
by patolojiAIComprehensive pancreatic cancer pathology workflow combining CAP/ICCR compliance validation, synoptic template generation, TNM staging (AJCC 8th edition for exocrine pancreas), Whipple/distal-pancreatectomy specimen dissection guidance, axial slicing and margin assessment (SMA, SMV, posterior, anterior, pancreatic neck, bile duct), tumor board summaries, and SNOMED coding. Use when the user uploads or pastes a pancreatic pathology report (Whipple, distal pancreatectomy, total pancreatectomy, pancreatic biopsy) and asks to "check pancreas report", "validate CAP Panc.Exo protocol", "stage this pancreatic cancer", "assess Whipple margins", or any pancreatic-cancer-specific pathology task. Supports .pdf, .docx, .txt input in English and Turkish.
tnm-stage-calculator
by patolojiAIFast TNM stage group calculation from pT, pN, pM categories using AJCC 8th edition criteria, with automatic consistency validation. Use when the user asks "what stage is pT2 N1 M0", "calculate TNM stage", "stage group for pT3 N1b breast", "is pT2 N0 M0 stage I or II", "stage this pancreatic cancer", or any pT/pN/pM to stage-group conversion. Supports breast, colorectal, pancreas, and gastric carcinoma. Flags inconsistencies like tumor-size-vs-pT mismatches and node-count-vs-pN mismatches.
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.