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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tooluniverse-clinical-risk-scoring
by mims-harvardCompute and interpret validated bedside clinical risk scores and pretest probabilities for an INDIVIDUAL patient — pick the right score for the scenario, gather inputs, run the deterministic calculator tool, and read the result against an interpretation table. Covers CHA2DS2-VASc (AF stroke risk), HAS-BLED (bleeding on anticoagulation), CURB-65 (pneumonia severity / admit decision), qSOFA (sepsis screen), Child-Pugh + MELD-Na (cirrhosis severity / transplant priority), Wells DVT and Wells PE (VTE pretest probability), ASCVD (10-year cardiovascular risk / statin decision), and eGFR CKD-EPI (kidney function / drug dosing). Use when asked things like "stroke risk for this AF patient", "should this patient be anticoagulated", "pneumonia severity — admit or not?", "sepsis screen this patient", "DVT/PE pretest probability", "10-year cardiovascular risk", "cirrhosis severity / MELD score", or "eGFR / kidney function". Pairs CHA2DS2-VASc with HAS-BLED to weigh anticoagulation. NOT for polygenic/genetic risk (use tool
langcare-pneumonia-curb65
by langcareAssesses pneumonia severity using CURB-65 and PSI/PORT scores to guide disposition (outpatient vs inpatient vs ICU). Pulls vitals, labs, and imaging from FHIR data. Use when asked about pneumonia severity, CURB-65 score, pneumonia disposition, community-acquired pneumonia management, or PORT score calculation.
langcare-vte-risk
by langcareAssesses venous thromboembolism risk using Wells Criteria (DVT and PE) and Caprini Score for surgical patients. Recommends prophylaxis based on risk stratification. Use when asked about VTE risk, DVT risk, PE risk, Wells score, Caprini score, thromboprophylaxis, or blood clot prevention.
langcare-diabetes-panel
by langcareReviews diabetes-related laboratory results against ADA Standards of Care including HbA1c, fasting glucose, lipid panel, renal function, and urine albumin. Tracks glycemic control trends and flags overdue monitoring. Use when asked to review diabetes labs, A1c trends, diabetes panel, glycemic control, or diabetic monitoring.
langcare-lab-interpretation
by langcareRetrieves, organizes, and interprets laboratory results with clinical context including delta checks, abnormal pattern recognition, and drug-lab correlations. Use when asked to interpret labs, review lab results, explain bloodwork, check labs, lab trends, or abnormal labs. Flags critical values requiring immediate action.
langcare-chronic-disease-registries
by langcareQueries and reports on chronic disease registries including diabetes, HTN, CHF, COPD, CKD, and asthma populations with severity distribution, control rates, and patients needing intervention. Use when asked about disease registries, chronic disease population, diabetes registry, HTN registry, disease-specific panel report, or population with specific condition.
kdigo-aki-staging
by hannesillCalculate KDIGO AKI (Acute Kidney Injury) staging for ICU patients using creatinine and urine output criteria. Use for nephrology research, AKI outcome studies, or renal function monitoring.
lods-score
by hannesillCalculate LODS (Logistic Organ Dysfunction Score) for ICU patients. Use for organ dysfunction assessment across 6 systems with weighted scoring.
sofa-score
by hannesillCalculate SOFA (Sequential Organ Failure Assessment) score for ICU patients. Use for sepsis severity assessment, organ dysfunction quantification, mortality prediction, or Sepsis-3 criteria evaluation.
alterlab-clinical-reports
by AlterLab-IEUWrites comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology, pathology, lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP notes, H&P, discharge summaries) — with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools. Use when drafting a case report for journal publication, a radiology/pathology/lab diagnostic report, an ICH-E3 clinical study report (CSR) or SAE narrative, or SOAP/H&P/discharge patient records needing regulatory-compliant formatting. Part of the AlterLab Academic Skills suite.
conducting-daily-rounds
by CaseMarkStructures systematic rounding documentation with overnight events, assessment, and plan updates. Use when documenting daily rounds, updating inpatient plans, or preparing rounding notes.
conducting-mortality-reviews
by CaseMarkStructures mortality case reviews with root cause analysis and system improvement recommendations. Use when conducting M&M reviews, analyzing adverse outcomes, or documenting mortality cases.
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.