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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public-speaking-embodied
by LeoYeAIPhysical presence and delivery skills for public speaking and presentations. Use when someone needs to present at work, speak at an event, lead a meeting, or wants to develop commanding physical presence in group settings.
argumentation-framework
by revfactoryA specialized skill that systematically provides the Toulmin argument model, evidence type classification, and rebuttal strategies for debate argumentation. Used by pro-debater and con-debater agents to construct robust arguments and effectively rebut opposing claims. Automatically applied in contexts such as 'argumentation structure', 'Toulmin model', 'argument construction', 'rebuttal strategy', 'evidence types', 'argument strengthening'. However, legal advocacy and academic paper argumentation structure are outside the scope of this skill.
argumentation-framework
by revfactory토론 논증 구축에 필요한 Toulmin 논증 모델, 증거 유형 분류, 반박 전략을 체계적으로 제공하는 전문 스킬. pro-debater와 con-debater가 견고한 논증을 구축하고 상대 논거를 효과적으로 반박할 때 활용한다. '논증 구조', 'Toulmin 모델', '논거 구축', '반박 전략', '증거 유형', '논증 강화' 등의 맥락에서 자동 적용한다. 단, 법률적 변론이나 학술 논문의 논증 구조 작성은 이 스킬의 범위가 아니다.
media-literacy-deconstruction-protocol
by GarethManningDesign a media deconstruction protocol analysing persuasion, bias, or representation in media texts. Use when teaching students to critically read advertisements, news, or social media content.
figure-rhetoric
by Mathews-TomEvaluate whether figures and plots in a manuscript effectively communicate the claims they support. Audits chart-type fit, axis design, visual hierarchy, data density, caption interpretation, perceptual accuracy, and narrative arc across 8 dimensions. Triggers on: "do my figures work", "check my plots", "are my graphs clear", "figure audit", "do my figures support my claims", "visualization review", "figure rhetoric", "plot review", "chart critique", "visual argument check". Companion to manuscript-review §12 (legibility) and figure-table-quality (rendering).
debate-practice-coach
by travisjneumanInteractive debate and argument practice with structured feedback, counterargument generation, logical fallacy identification, and scoring rubrics. Use when preparing for debates, improving persuasion skills, or practicing argumentation.
media-literacy
by TibsfoxMedia literacy and critical analysis of media messages across platforms and formats. Covers media ecology (McLuhan), encoding/decoding (Hall), news literacy, digital media analysis, propaganda techniques, algorithmic curation, visual rhetoric, and ethical media consumption. Use when analyzing media messages, evaluating news sources, understanding how media technology shapes communication, detecting propaganda, or studying the relationship between medium and message.
public-speaking
by TibsfoxPublic speaking and oral presentation skills for effective communication. Covers speech structure (introduction, body, conclusion), delivery techniques (projection, pace, pause, gesture), audience analysis, impromptu and extemporaneous methods, managing anxiety, visual aids, and persuasive versus informative speaking. Use when preparing speeches, practicing delivery, analyzing presentations, or building confidence in oral communication.
algorithmic-awareness
by TibsfoxUnderstanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.
pragmatics-communication
by TibsfoxPragmatics and communicative competence across languages -- speech acts (Austin/Searle), Grice's conversational maxims and implicature, politeness theory (Brown & Levinson), face-threatening acts, discourse structure, turn-taking, repair strategies, register and formality, cross-cultural communication, communicative strategies (circumlocution, approximation, appeal for assistance), and the gap between grammatical competence and communicative performance. Use when teaching communication skills, analyzing cross-cultural misunderstandings, building conversational fluency, or understanding why grammatically correct sentences can be pragmatically inappropriate.
debate-coach
by rar-fileArgue both sides of any topic with structured reasoning
fact-checker
by rar-fileVerify claims, identify misinformation, and assess source credibility
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.