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.
Querying local SQLite index...
aplus-module-generator
by sellersessionsGenerate the 5-7 vertical A+ Premium modules for an Amazon listing — Higgsfield image generation + structured layout following the two-half architecture (top half capture & convert, bottom half visual FAQ). Use when running `aplus-module-generator {ASIN}` or when planning A+ Premium content that follows Keplo's methodology.
sqp-priority-rank
by sellersessionsScore and rank an Amazon ASIN portfolio by CRO opportunity using SellerApp keyword data, BSR, and reviews. Wraps and extends the existing `cro-priority-tracker` skill. Use when running `sqp-priority-rank` on a portfolio, when deciding which ASIN gets research budget next, or when a client has 20+ ASINs and needs triage. Defaults to the Keplo scoring formula: Data Strength 40 / $ Impact 30 / Effort 15 / Speed 15.
ad-creative-batch
by sellersessionsGenerate ad creative variations across formats (UGC, TV spot, Wild Card) for an Amazon product using Higgsfield Ad Engine. Replaces a $5K/mo agency retainer per Higgsfield's positioning. Use when running `ad-creative-batch {ASIN}` or when building Sponsored Brand Video / external traffic ad campaigns.
listing-battle
by sellersessionsStage your Amazon listing vs top 2-3 competitors on a ProductPinion Amazon Search Simulation, get real shoppers to pick favorites + explain why. Use when running `listing-battle {ASIN}`, when evaluating competitive positioning before SERP-attack, or when client wants direct head-to-head shopper data.
quarterly-listing-refresh
by sellersessionsQuarterly check-in pipeline for an Amazon ASIN — re-runs review mining, detects sentiment shifts, generates 3 refresh concepts, polls them with shoppers, and recommends a single test to launch. Use when running `quarterly-listing-refresh {ASIN}` every 90 days for active CRO clients to catch listing decay.
ugc-video-creator
by sellersessionsGenerate 6-15 second UGC-style Amazon product videos using Higgsfield Kling / Veo / Seedance with the UGC preset. Use when running `ugc-video-creator {ASIN}` for Sponsored Brand Video, Brand Store, or A+ Premium video slots. Replaces UGC creator commissions ($500-$2K per video) with same-day AI generation.
portfolio-lqi-watch
by sellersessionsMonthly portfolio-wide LQI sweep with diff against prior month — detects which ASINs improved, stagnated, or regressed in listing quality. Use when running `portfolio-lqi-watch {client}` monthly via `/loop monthly` or as part of CRO Partner Program monthly review.
asin-deep-research
by sellersessionsOne-shot ASIN research package using SellerApp via n8n MCP. Pulls product details, reviews (1-5★ with sentiment), Rufus queries, reverse ASIN keywords, competitor SERP, LQI score, and BSR history into a pre-filled CRO Research Brief. Use when starting any new ASIN engagement, before content planning, or when running `asin-deep-research {ASIN}`. Replaces 3-4 hours of manual data pulling.
listing-quality-audit
by sellersessionsRun SellerApp's Listing Quality Index (LQI) across an ASIN portfolio (up to 100 ASINs per request) to surface section-level issues — title, bullets, description, images, videos, Q&A, ratings, reviews. Outputs a triage list ranked by lowest score. Use when running `listing-quality-audit {ASIN1,ASIN2,...}`, when onboarding a new client (LQI sweep across catalog), or when picking which ASINs need urgent fixes.
profit-recalc-on-change
by sellersessionsRe-run Amazon profit calculation when BSR or competitor pricing shifts trigger a margin review. Uses SellerApp Profit Calculator to estimate referral fees, FBA fees, variable closing fees at proposed price. Use when running `profit-recalc-on-change {ASIN}`, when planning a repricing decision, or as a sub-step of pricing pipelines.
hero-video-builder
by sellersessionsGenerate cinematic Amazon A+ Premium hero video using Higgsfield Veo 3.1 / Sora 2 / Cinema Studio. Use when running `hero-video-builder {ASIN}` or when A+ Premium needs a top-of-page hero video that's higher production than UGC.
main-image-concepts
by sellersessionsGenerate 5-10 Amazon main image concepts for an ASIN using Higgsfield AI image generation. Defaults to GPT Image (latest) and Nano Banana Pro — best-quality only. Each concept is scored against the 6-dimension main image rubric (Fidelity, Background, Scroll-Stop, Compliance, Creative, Quality). Use when running `main-image-concepts {ASIN}`, when you need standalone main image ideation without polling, or as a sub-step of `main-image-pipeline`.
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.