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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abridge-cost-tuning
by jeremylongshoreOptimize Abridge clinical AI costs through tier selection, session management, and usage monitoring for healthcare organizations. Use when analyzing Abridge billing, optimizing encounter volume, or right-sizing your Abridge contract for provider count. Trigger: "abridge cost", "abridge pricing", "abridge billing", "abridge budget", "abridge ROI".
salesforce-cost-tuning
by jeremylongshoreOptimize Salesforce costs through API call reduction, edition selection, and license management. Use when analyzing Salesforce costs, reducing API consumption, or choosing the right Salesforce edition for your integration needs. Trigger with phrases like "salesforce cost", "salesforce pricing", "reduce salesforce costs", "salesforce license", "salesforce API usage", "salesforce budget".
partnership-bd
by RefoundAIHelp users build strategic partnerships and business development deals. Use when someone is pursuing a partnership, negotiating a BD deal, working with platforms like Google or Facebook, or trying to build distribution through partners.
contract-checklist
by revfactoryProcurement contract review checklist. Referenced by contract-reviewer and acceptance-builder agents when reviewing contract terms and establishing acceptance criteria. Used for 'contract review', 'contract terms', 'acceptance criteria' requests. Note: legal counsel and contract notarization are out of scope.
procurement-docs
by revfactoryA procurement document set generation pipeline. An agent team collaborates to produce requirements definitions, vendor comparisons, evaluation criteria, contract terms, and acceptance criteria. Use this skill for 'create procurement documents', 'vendor comparison', 'procurement specifications', 'evaluation criteria', 'acceptance criteria', 'procurement documents', 'bid evaluation', 'RFP creation', 'vendor selection', and similar procurement/sourcing document topics. Actual ordering, contract execution, payment processing, and asset registration are out of scope.
pricing-strategy
by revfactory제품/서비스의 가격 전략을 원가 분석, 경쟁 가격, 가치 기반 가격, 시뮬레이션으로 체계화하는 풀 파이프라인. '가격 전략 세워줘', '가격 책정해줘', '프라이싱 전략', '원가 분석', '경쟁 가격 조사', '가격 시뮬레이션', '가격 모델 설계', '요금제 만들어줘', 'SaaS 가격', '구독 모델 설계' 등 가격 전략 수립 전반에 이 스킬을 사용한다. 기존 원가 데이터나 가격 정보가 있는 경우에도 분석·최적화를 지원한다. 단, 회계 장부 작성, 세금 계산, 결제 시스템 개발, 실시간 동적 가격 엔진 구축은 이 스킬의 범위가 아니다.
contract-checklist
by revfactory구매 계약 검토 체크리스트. contract-reviewer와 acceptance-builder 에이전트가 계약 조건을 검토하고 검수 기준을 수립할 때 참조. '계약 검토', '계약 조건', '검수 기준' 요청 시 사용. 단, 법률 자문이나 계약서 공증은 범위 밖.
procurement-docs
by revfactory구매 문서세트 생성 파이프라인. 요구사항 정의부터 벤더 비교, 평가 기준, 계약 조건, 검수 기준까지 에이전트 팀이 협업 생성한다. '구매 문서 만들어줘', '벤더 비교표 작성', '구매 사양서', '평가 기준표', '검수 기준서', '조달 문서', '입찰 평가', 'RFP 작성', '벤더 선정' 등 구매·조달 문서 전반에 이 스킬을 사용한다. 실제 발주·계약 체결, 대금 지급 처리, 자산 등록은 이 스킬의 범위가 아니다.
dfg-grossgeraete-und-cluster-antrag
by KlotzketteGrossgeraete und Cluster-Geraete in DFG-Antrag: Geraete ab 50.000 Euro mit zusaetzlicher Wirtschaftlichkeit, Auslastungsplan, technische Anschlussbedingungen, Wartungsvertrag, Folgekosten. Eigene Geraetekommission der DFG. Pruefraster für Beschaffungsbegruendung und Marktrecherche.
procurement-legal-interface
by KlotzketteProcurement Legal Interface: steuert Einkauf will Kanzleikosten drücken; Legal will Qualität, Privilege und Geschwindigkeit sichern zwischen Kanzlei, Mandant und Rechtsabteilung mit Dashboard, Budget, Fristen, Verantwortlichkeiten, Beziehungspflege und Quellenhygiene im Kanzlei-Mandant-Lifecycle: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
rate-card-review
by KlotzketteRate Card Review: steuert Stundensätze, Seniorität, Staffing, Blended Rate und jährliche Anpassung prüfen zwischen Kanzlei, Mandant und Rechtsabteilung mit Dashboard, Budget, Fristen, Verantwortlichkeiten, Beziehungspflege und Quellenhygiene im Kanzlei-Mandant-Lifecycle: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
rechnung-streit-deeskalation
by KlotzketteRechnungsstreit deeskalieren: steuert Kürzung, Missverständnis, Value Story, Kulanz, Nachlass und künftiger Prozess zwischen Kanzlei, Mandant und Rechtsabteilung mit Dashboard, Budget, Fristen, Verantwortlichkeiten, Beziehungspflege und Quellenhygiene im Kanzlei-Mandant-Lifecycle: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
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.