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.
Querying local SQLite index...
kyc-completeness-validator
by writerValidate KYC/CDD documentation completeness and identify gaps in customer due diligence files. Use when reviewing customer onboarding packages, assessing CDD/EDD requirements, validating beneficial ownership under CDD Rule, or preparing for BSA/AML examinations requiring documentation compliance.
regulator-communication-drafting
by writerDraft regulator-ready communications for financial institutions. Use when preparing responses to regulatory inquiries, drafting MRA/MRIA remediation updates, composing supervisory correspondence, preparing examination response letters, or creating regulatory notification filings aligned with OCC, Federal Reserve, FDIC, and state regulator expectations.
strategic-initiative-tracking
by writerTrack and report on strategic initiative progress using milestone management, KPI monitoring, and portfolio-level health assessments. Use when monitoring strategic plan execution, preparing initiative status reports for leadership, identifying at-risk initiatives, or managing strategic portfolio prioritization.
longitudinal-patient-summaries
by writerGenerate timeline-based patient summaries that synthesize clinical events, diagnoses, treatments, and utilization across multi-year care histories. Use when preparing care transition summaries, complex case reviews, care coordination briefs, or longitudinal health records for clinical decision support.
sdoh-risk-identifier
by writerIdentify and assess social determinants of health risks using validated screening tools (PRAPARE, AHC-HRSN) to enable targeted resource linkage, care plan adaptation, and health equity improvement across patient populations.
readmission-risk-explanation
by writerExplain readmission risk drivers using LACE index scoring, clinical and social determinant analysis, and targeted intervention recommendations. Use when assessing 30-day readmission risk, explaining readmission prediction model outputs, designing readmission reduction interventions, or reporting on Hospital Readmissions Reduction Program metrics.
health-literacy-adapter
by writerDynamically adjust healthcare content readability and presentation based on assessed health literacy levels using validated instruments (REALM, NVS, TOFHLA) and adaptive content transformation strategies aligned with universal precautions approach.
multilingual-patient-content
by writerGenerate and validate language-safe healthcare communications for Limited English Proficiency (LEP) populations following Section 1557, CMS language access requirements, and culturally appropriate health communication standards.
associate-enablement-coach
by writerGenerate store-level playbooks that translate performance gaps into actionable coaching scripts, training priorities, and behavioral nudges tailored to individual store contexts and associate skill profiles.
lab-result-explanation
by writerExplain laboratory results for clinicians with clinical context, reference range interpretation, critical value flagging, trending analysis, and diagnostic significance mapping. Use when interpreting lab panels, explaining abnormal results, correlating lab findings with clinical conditions, or generating lab summary reports.
authorization-timeline-predictor
by writerPredict prior authorization processing timelines and identify delay risk factors by analyzing payer response patterns, service complexity, clinical documentation completeness, and historical approval data. Use when estimating authorization turnaround for scheduling, identifying at-risk authorizations, optimizing submission timing, or managing patient expectations for prior auth dependent services.
liquidity-forecast-narratives
by writerGenerate executive-ready narratives explaining liquidity forecasts, LCR/NSFR positions, HQLA composition, and cash flow projections. Use when explaining liquidity positions, writing ALCO reports, summarizing Basel III liquidity metrics, or narrating funding gaps and surplus positions.
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.