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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seedance-ugc-ads
by williamsforealGenerate multi-shot UGC video ads for DTC brands and creative agencies using Seedance 2.0 via the fal.ai API. Auto-writes UGC dialogue and shot lists from a product image plus an ad angle (unboxing, testimonial, lifestyle demo, problem-solution, before-after, or freeform), fires prompts to Seedance, downloads every clip, and stitches them into one finished vertical ad with ffmpeg. Use whenever the user wants to create a UGC ad, generate UGC video, make a TikTok/Reels/Meta video ad, turn a product photo into video, clone a UGC ad, or make creator-style video content for a DTC brand. Also trigger on phrases like "make me a UGC video ad," "generate a Seedance ad," "UGC from this product," or when the user drops a product image and asks for a video ad.
self-improving-agent
by williamsforeal[STUB — NOT YET BUILT] Meta-learning layer — agents that update their own skill files based on outcomes. Q4 2026 target. (P3 — see REGISTRY.md §3.30)
ad-family
by williamsforealGenerate an Ad Family using the Motion Methodology from a winning ad. Use when user says "ad family", "scale this ad", "creative strategy", "learned concept", or provides a winning ad to analyze and expand into multiple formats.
campaign-launcher
by williamsforealUse this skill when the user asks Claude to plan, structure, or launch a Meta ad campaign — from pre-launch brief through PAUSED ad creation via Meta Ads MCP. Encodes the AI Com Academy testing framework, ad set structure, and the coach-approval gates that govern Jake's launches.
creative-testing-framework
by williamsforealUse this skill when the user asks Claude to design a creative testing plan, structure a hook/angle/format test, or decide what to test next. Encodes the principle of isolating one variable per test, batch sizes for statistical confidence, and the iteration cycle that compounds learnings.
post-launch-analysis
by williamsforealUse this skill when the user asks Claude to review live Meta campaign performance — Day 3, Day 7, or Day 14 reviews, kill/scale/iterate decisions, or any "how is my campaign doing" question. Encodes the Scale DTC Statistical Analysis Hierarchy and the AI Com Academy decision tree.
ad-concept-generator
by williamsforealUse this skill when the user asks Claude to generate ad concept directions, brainstorm new creative angles, or plan a batch of ads before any image generation happens. Concept-first work — produces strategic directions grounded in avatar + offer + awareness level, not generic prompts.
ad-family-architect
by williamsforealUse this skill when the user has a validated winning ad (or a high-conviction concept) and wants to build a "family" of variants around it for systematic A/B testing. Encodes the principle that you scale by exploiting one winner's pattern across controlled axes, not by generating 10 random ads.
hook-bank-generator
by williamsforealUse this skill when the user asks Claude to generate or refresh a TikTok slideshow hook bank for a brand. Produces 20-25 hooks across pattern interrupt / listicle / curiosity / pain-first / identity / POV / before-after categories, scored on hook strength and brand fit.
ad-family-architect
by williamsforealUse when building an ad family, scaling a winning ad, or applying Motion Methodology to generate creative variants from a single concept.
json-to-comfy
by williamsforealUse when converting structured JSON (from image-to-json) into ComfyUI-ready prompts and workflow settings.
s3-uploader
by williamsforeal[STUB — NOT YET BUILT] Upload images/videos to S3/R2 CDN with public-read ACL. Returns canonical URL for Airtable attachment fields. (P2 — see REGISTRY.md §3.20)
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.