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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ugc-repurposer
by wanghaishengTransforms influencer content into multiple formats for use across channels including ads, website, email, and social. Maximizes the value of every piece of content created.
outreach-manager
by wanghaishengManages influencer outreach including personalized pitch creation, follow-up sequences, negotiation strategies, and relationship tracking. Helps secure partnerships efficiently while building genuine connections.
landing-optimizer
by wanghaishengOptimizes landing pages for influencer-driven traffic to maximize conversions. Ensures consistency between influencer content and landing experience for better performance.
audience-analyzer
by wanghaishengAnalyzes target audience demographics, psychographics, behaviors, and platform preferences to inform influencer selection and campaign strategy. Essential foundation for effective influencer marketing.
trend-spotter
by wanghaishengIdentifies trending topics, hashtags, content formats, and cultural moments relevant to your brand. Helps time influencer campaigns for maximum relevance and engagement.
influencer-discovery
by wanghaishengDiscovers and compiles lists of relevant influencers across platforms based on niche, audience demographics, content style, and brand fit. The foundation of any successful influencer marketing program.
campaign-planner
by wanghaishengCreates comprehensive influencer marketing campaign plans including objectives, strategy, influencer selection criteria, content requirements, timeline, budget allocation, and success metrics. Your campaign blueprint.
report-generator
by wanghaishengCreates comprehensive influencer marketing campaign reports for different stakeholders including executives, clients, and internal teams. Transforms data into compelling narratives.
start
by wanghaishengFirst-time onboarding — asks where you are, then guides you to the right workflow. No assumptions.
level-encounter-planner
by wanghaishengStructure spaces and encounters to deliver on pillars and pacing. The craft of "what happens where and in what order." Use this skill when: (1) designing level layouts, encounter sequences, or scenario structures, (2) building pacing curves (tension/release patterns), (3) defining encounter composition (enemy mix, challenge types, resource pressure), (4) choosing spatial flow models (linear, hub-and-spoke, open, gated), (5) planning difficulty ramping across a game, (6) designing for replayability through encounter variation, (7) mapping narrative beats to specific encounters or locations. Medium-agnostic — works for digital levels, tabletop scenarios, and hybrid encounters.
player-experience-modeler
by wanghaishengMap the intended emotional journey and psychological engagement model for a game. Defines what the player should feel at each stage of play and what motivates continued engagement. Use this skill when: (1) translating a vision and pillars into concrete emotional targets, (2) designing the pacing of a play session or campaign, (3) evaluating whether a game delivers the intended experience, (4) diagnosing why a game "feels off" despite sound mechanics, (5) choosing between design options based on which better serves the intended experience, (6) mapping flow state targets and challenge curves. Medium-agnostic — works for digital, tabletop, and hybrid games.
core-loop-designer
by wanghaishengDefine the fundamental action cycle — what the player does repeatedly and why it stays engaging. The heartbeat of the game. Use this skill when: (1) translating pillars and experience targets into a concrete gameplay cycle, (2) diagnosing why a game feels repetitive or disengaging, (3) designing nested loop structures (moment-to-moment, session, meta/campaign), (4) identifying attrition risk points where players disengage, (5) evaluating whether a proposed mechanic serves the core loop or fragments it, (6) designing how the loop evolves over time to sustain long-term engagement. Medium-agnostic — works for digital, tabletop, and hybrid games.
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.