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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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langcare-mental-health
by langcarePerforms mental health screening using PHQ-9 (depression), GAD-7 (anxiety), AUDIT-C (alcohol use), and Columbia Suicide Severity Rating Scale from FHIR Observation data. Tracks symptom trends and treatment response. Use when asked for mental health screening, depression assessment, PHQ-9 score, GAD-7 score, anxiety assessment, or behavioral health evaluation.
mental-health-screening
by langcareImplements validated mental health screening tools (PHQ-9, GAD-7, AUDIT-C, C-SSRS, MDQ, PC-PTSD-5) with scoring, interpretation, and safety planning. Use when user asks to "screen for depression", "anxiety screening", "PHQ-9 score", "GAD-7", "suicide risk assessment", "AUDIT-C", "bipolar screening", "PTSD screen", "mental health assessment", or needs behavioral health screening documentation. Do NOT use for psychiatric medication management, therapy notes, or detailed psychiatric evaluations.
langcare-insurance-coverage
by langcareRetrieves and analyzes insurance coverage from FHIR Coverage and Organization resources including coordination of benefits, coverage gaps, and eligibility details. Use when asked about insurance info, coverage details, benefits check, coordination of benefits, coverage verification, or payer information.
discharge-summary-writer
by langcareGenerates comprehensive discharge summaries per CMS and Joint Commission requirements from FHIR data including admission and discharge diagnoses, hospital course, procedures, discharge medications with changes, follow-up, and patient instructions. Use when user asks to "write a discharge summary", "create DC summary", "discharge documentation", mentions "discharge paperwork", "transition of care document", or needs discharge documentation. Do NOT use for admission H&P, daily progress notes, outpatient visit notes, or transfer summaries.
insurance-coverage-summary
by langcareRetrieves and analyzes FHIR Coverage and Organization resources to produce a structured insurance summary with coordination of benefits analysis. Use when user asks to "check insurance", "coverage details", "verify coverage", "insurance summary", "what plan is the patient on", "primary vs secondary insurance", or "coverage status". Do NOT use for clinical data, demographics beyond insurance, or billing claim submission.
patient-demographics-summary
by langcareRetrieves and formats a complete patient demographic summary from FHIR Patient, RelatedPerson, and Coverage resources. Use when user asks to "pull demographics", "get patient info", "show patient details", "who is this patient", "patient summary", "emergency contacts", "insurance info", or needs a quick overview of non-clinical patient data. Do NOT use for clinical summaries, lab results, medication lists, or diagnosis reviews.
langcare-pediatric-growth
by langcareAssesses pediatric growth by plotting weight, height/length, head circumference, and BMI against WHO (0-2 years) and CDC (2-20 years) growth chart percentiles. Flags growth faltering, obesity, and failure to thrive. Use when asked about pediatric growth, growth chart, growth percentiles, FTT, childhood obesity, or weight-for-age.
pediatric-growth-assessment
by langcareAssesses pediatric growth using WHO and CDC growth charts with percentile and z-score calculations. Use when user asks to "plot growth", "check growth chart", "growth assessment", "growth percentiles", "is the child growing normally", "failure to thrive", "FTT workup", "pediatric BMI", mentions "growth velocity", "weight for age", "height for age", or needs growth trend analysis for a child. Do NOT use for adult BMI, adult weight management, or prenatal growth (use prenatal-visit-workflow).
prenatal-visit-workflow
by langcarePerforms structured prenatal visit assessment organized by trimester with ACOG guideline alignment. Use when user asks to "review prenatal visit", "prenatal assessment", "OB visit", "pregnancy checkup", "trimester labs", "prenatal labs", mentions "gestational age", "fundal height", "prenatal care", or needs pregnancy-related clinical data organized by visit schedule. Do NOT use for labor and delivery, postpartum care, gynecologic exams, or non-obstetric visits.
langcare-quality-measures
by langcareCalculates HEDIS-style quality measures from FHIR data including denominator/numerator/exclusion logic, measure rates, gap-to-goal analysis, and non-compliant patient identification. Use when asked to calculate quality measures, HEDIS rates, quality dashboard, star rating, measure compliance, or CMS quality scores.
clinical-summary-generator
by langcareGenerates a comprehensive CCD-style clinical summary from multiple FHIR resources following US Core and C-CDA structure. Use when user asks to "generate a clinical summary", "create a CCD", "summarize the chart", "give me the full picture", "comprehensive summary", "patient overview", "transition of care summary", or needs a consolidated clinical view. Do NOT use for single-domain queries like "just allergies" or "just meds" -- use domain-specific skills instead.
langcare-cardiovascular-risk
by langcareCalculates cardiovascular risk scores including CHA2DS2-VASc, HEART Score, ASCVD Pooled Cohort Equations, and HAS-BLED from FHIR data. Generates treatment-threshold recommendations per ACC/AHA guidelines. Use when asked to assess cardiac risk, stroke risk in AFib, HEART score, CHA2DS2-VASc, ASCVD risk, or statin candidacy.
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.