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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cold-call-prep
by anthropicsPrep for a cold-call — predict the professor's likely questions and drill them Socratically, flagging where you're shaky so you know what to re-read before class. Use when the user says "prep for class tomorrow", "cold call [case]", "what might [professor] ask on", or points at assigned reading.
cold-start-interview
by anthropicsAbout-you interview and materials intake — classes, bar jurisdiction, learning style (drill-me vs explain-to-me), past outlines, graded essays, old exams, MBE sets, syllabi, papers. Use on a fresh install, when the user says "set up" or "get started", or with --check-integrations to re-probe connectors.
session
by anthropicsRun a focused N-question study session on a subject — MBE, essay, or flashcards. Tracks performance and updates the study plan. Use when the user says "run me 10 questions on [subject]", "do a session on [subject]", "let's do 5 cards on [subject]", or wants to drill a fixed number of questions and have the plan adapt.
socratic-drill
by anthropicsSocratic drilling — it asks, you answer, it pushes back. Does NOT give you the answer until you've earned it. Use when the user says "drill me on", "quiz me", "socratic", "test me on [subject]", or wants to study actively.
supervisor-review-queue
by anthropicsProfessor's review queue — student output waits here for professor approval before going to clients or courts. Only active if "formal review queue" supervision style was chosen at setup; otherwise dormant. Use when the professor wants to see what's waiting for review, approve, edit-then-approve, or return an item.
aris-rebuttal
by OpenLAIRWorkflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds. Use when user says "rebuttal", "reply to reviewers", "ICML rebuttal", "OpenReview response", or wants to answer external reviews safely.
schulung-legistik
by KlotzketteTrainerleitfaden für Legistik-Schulung mit der Arbeitsakte elektronisches Pflichtpostfach. Anwendungsfall Referenten oder Mitarbeiter von Verbanden sollen legistische Kernkompetenz in zwei Tagen Inhouse-Schulung oder einer Woche Fortbildung erwerben. Lernziele Auftragsaufnahme Normebenen-Routing Verfassungsrecht- und Europarechts-Quercheck Referentenentwurf Begründung Synopse XML Folgenabschaetzung. Sechs Stationen mit Lernziel Aufgabe Stolperfallen Erwartungshorizont. Output Trainerleitfaden Aufgabenhefte Erwartungshorizonten je Station. Kompatibel mit der Arbeitsakte legistik-pflichtpostfach.
methodenlehre-oeffentliches-strafrecht
by KlotzketteÜbt die öffentlich-rechtliche Methodenlehre — Schichtenprüfung bei Grundrechten, Verhältnismäßigkeit, Ermessen und Ermessensfehler, Verwaltungsaktqualität, prozessuale Methodik der Klagearten, unionsrechtskonforme Auslegung, Vorrang des EU-Rechts, Vorlage an EuGH und BVerfG. Lädt, wenn der Nutzer Grundrechtsprüfung, Verhältnismäßigkeit, Ermessen prüfen, Klageart bestimmen oder Vorlage EuGH sagt im Jurastudium: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
lernplan
by KlotzketteErstellt oder aktualisiert einen strukturierten Lernplan für das Erste Staatsexamen, das Referendariat oder das Zweite Staatsexamen — phasenbezogen, nach Schwächen gewichtet, adaptiv nach Lernverlauf. Berücksichtigt Repetitoriumskalender (Alpmann, Hemmer, Jura Intensiv, Kaiser-Skripten). Lädt, wenn der Nutzer Lernplan erstellen, Examensvorbereitung planen, Stundenplan Staatsexamen oder wie soll ich für [Prüfung] lernen sagt im Jurastudium: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
methodenlehre-strafrecht
by KlotzketteÜbt die strafrechtliche Methodenlehre — dreistufiger Verbrechensaufbau (Tatbestand, Rechtswidrigkeit, Schuld), Trennung objektiver/subjektiver Tatbestand, Konkurrenzlehre (Tateinheit § 52, Tatmehrheit § 53, Gesetzeskonkurrenz), Analogieverbot Art. 103 II GG, Auslegung im Lichte des Bestimmtheitsgebots. Lädt, wenn der Nutzer Strafrecht-Aufbau, Verbrechensaufbau prüfen, Konkurrenzen Strafrecht, Analogieverbot oder Vorsatz subsumieren sagt im Jurastudium: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
methodenlehre-zivilrecht
by KlotzketteÜbt die zivilrechtliche Methodenlehre für Studierende — Anspruchsgrundlagen-Schema, AGL-Reihenfolge (vertraglich, vertragsähnlich, dinglich, deliktisch, bereicherungsrechtlich), Konkurrenzen, Auslegung von Willenserklärungen (§§ 133/157 BGB), Auslegung von AGB (§ 305 ff. BGB), Verkehrssitte. Lädt, wenn der Nutzer AGL-Reihenfolge, Anspruchsprüfung, Willenserklärung auslegen, AGB auslegen oder Konkurrenzen Zivilrecht sagt im Jurastudium: prüft konkret die einschlägigen Tatbestandsmerkmale, Fristen, Belege und Rechtsprechung. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Arbeitsschritt.
legal-simulation-patrick-munro
by lawve-aiFramework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
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.