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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ftc-disclosure-spot-checker
by archive-dot-comReviews a submitted creator caption, script, or post for FTC disclosure compliance and flags specific issues with concrete fixes. This skill should be used when checking if a creator's caption has proper FTC disclosure, reviewing influencer content for ad disclosure compliance, auditing a sponsored post for proper
ugc-hook-analyzer
by archive-dot-comAnalyze a batch of UGC performance data (CSV exports from Archive or similar platforms) to extract hook patterns, value prop rankings, and content format insights, then turn those findings into talking points, hook templates, and a shareable one-page launch brief grounded in real data. This skill should be used when analyzing UGC performance data, finding what hooks are working with creators, ranking value props by engagement, prepping creator briefings from real performance data, generating talking points for a launch from past UGC, building a UGC insights brief, doing pre-launch content research, or figuring out which creator messages and formats actually drive engagement. For converting these insights into a full campaign brief, see campaign-brief-generator. For ranking individual creators by post-campaign performance, see post-campaign-creator-scorecard. For setting realistic KPI benchmarks before launch, see performance-benchmark-setter. For calculating engagement rates across the dataset, see engagement
universal-creator-follow-up-chaser
by archive-dot-comGenerate a personalized follow-up sequence for any creator chasing scenario — missing info, unsigned contract, late content, missing metrics, or incomplete whitelisting setup. This skill should be used when chasing a creator for a response, writing a follow-up message to an influencer, nudging a creator about a late deliverable, following up on an unsigned contract, requesting missing campaign metrics, chasing whitelisting or ad access setup, escalating a non-responsive creator, writing a reminder to a creator who ghosted, building a follow-up cadence for overdue items, drafting a polite but firm nudge to an influencer, or managing creator communication when deadlines slip. For writing initial outreach messages, see creator-outreach-sequence-generator. For classifying and triaging creator replies, see reply-triage-classifier. For negotiating rates after a creator responds, see creator-negotiation-assistant.
utm-parameter-builder
by archive-dot-comGenerate correctly formatted UTM parameters and full tracking URLs for creator marketing campaigns. This skill should be used when building UTM links for influencers, creating tracking URLs for a creator campaign, generating UTM strings for multiple creators, setting up campaign tracking links, formatting UTM parameters for influencer partnerships, building attribution links for creator posts, creating unique tracking URLs per creator, batch generating UTM links for a roster of creators, or setting up link tracking before a campaign launch. For measuring campaign results after launch, see campaign-roi-calculator. For normalizing metrics from multiple sources, see metrics-normalization-formatter.
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.