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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ads-amazon
by AgriciDanielAmazon Ads deep analysis covering Sponsored Products, Sponsored Brands (incl. Sponsored Brands Video), Sponsored Display (audiences + contextual), and basic Amazon DSP. Evaluates campaign structure, ACOS/TACOS targets, search-term harvesting, negative keyword discipline, Brand Analytics signals, day-parting, bid management, auto vs manual campaign mix, ASIN targeting, and DSP retargeting. Use when user says Amazon Ads, Amazon advertising, Amazon PPC, Amazon search ads, Sponsored Products, Sponsored Brands, Sponsored Display, Amazon DSP, ACOS, TACOS, retail media audit, Amazon Marketing Services, AMS, or Amazon seller advertising.
meta-ad-builder
by krusemediallcPublish finished creatives as live Meta (Facebook/Instagram) ads via the Meta Marketing API, plus research and ad-copy support. Uploads an image or video, builds a multi-variant TEXT_LIQUIDITY creative, and creates a PAUSED ad in an existing ad set. Also pulls top-performing ads (ranked by ROAS) and competitor ads from the Ad Library to inform copy. Use when the user asks to deploy / publish / launch a creative as a Meta or Facebook ad, build a Meta ad, push a video or image into an ad set, pull their top ads, or research competitor ads. Not for generating creative (use the image/video skills) and not for writing AdTable/Airtable rows (use adtable-light).
reddit-ads
by alinaqiReddit Ads API - campaigns, targeting, conversions, agentic optimization
05-ad-copy-global
by minhnv08076 ad copy variations (2 TOFU + 2 MOFU + 2 BOFU) for global markets. Frameworks: AIDA, PAS, BAB. Platforms: Meta, Google, TikTok. INCLUDES Dropshipping Mode (4 templates) for Shopify dropshippers. Trigger: 'ad copy', 'Facebook ads', 'TikTok ads', 'Google Ads copy', 'dropshipping ads', 'Shopify ads'.
hormozi-ad-factory
by pedronauckGenerates 150-750+ ad variations using Alex Hormozi's combinatorial Hook x Meat x CTA framework. Triggers on requests to create ads, ad copy, ad scripts, marketing creatives, video ad scripts, ad hooks, CTAs, ad testing, ad scaling, or ad factories. Also triggers on mentions of Hormozi's ad method or combinatorial ad creation. Does not trigger for general copywriting, email marketing, landing pages, or non-advertising content.
multi-channel
by aiskillstoreUnified social and advertising command center. Manages social inbox, optimizes ad performance, tracks keywords, coordinates messaging across YouTube, Instagram, TikTok, LinkedIn, Twitter, Facebook, Reddit, and Pinterest.
amazon-negative-keywords
by nexscope-aiAmazon negative keyword management and optimization. Search term analysis, waste reduction, savings estimation, and automated negative keyword strategies. Use when the user asks about negative keywords, search term analysis, ad waste reduction, or PPC optimization.
afa-fb
by afadtcMeta 广告优化引擎——Facebook/Instagram 广告账户结构、受众策略、出价优化、创意测试、Advantage+ 、CBO/ABO。Use when user mentions: Facebook广告, Meta Ads, Instagram广告, FB广告, CPM, CPA, 受众定向, targeting, Advantage+, CBO, ABO, 广告组, ad set, 像素, pixel, ROAS, Meta投放.
afa-paid
by afadtc付费获客 Supervisor——统筹 Meta/Google/TikTok 广告与创意生产的跨渠道预算分配、策略协同与路由。Use when user mentions: 付费广告, paid ads, 广告投放, 预算分配, budget allocation, 跨渠道广告, 广告策略, ad strategy, 获客成本, CAC, 广告效果, 投放优化, 多渠道广告.
afa-tt
by afadtcTikTok 广告优化引擎——TikTok Ads 账户结构、受众策略、创意方向、TikTok Shop、达人合作。Use when user mentions: TikTok, 抖音广告, TikTok Ads, TikTok Shop, 短视频广告, Spark Ads, 达人广告, creator ads, TikTok投放, TikTok开店, TikTok营销.
juliang-qianchuan-official-perspective
by swaylq巨量千川官方方法论视角. 抖音电商核心广告平台官方思路 (并入抖音电商后, 重 ROI / GMV). 调用此 skill 时, 用千川官方框架做投流决策.
brand-ad-copy
by jgertonGenerate platform-specific ad copy (Google Ads, social, email) from brand voice and messaging. Handles character limits, platform conventions, and A/B variants. Triggers when someone needs ad copy, social media copy, email campaigns, or platform-specific marketing content.
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.