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...
google-ads-competitive-intel
by kayasinanPriority 4 agent. Monitors competitor ads via Google Ads Transparency Center (web scraping, no official API). Analyzes Auction Insights from Data Analyst. Scores ads by longevity. Identifies Mode B replication candidates. Writes to g_competitors and g_competitor_ads tables.
google-ads-postclick-analyst
by kayasinanPriority 3 agent. Analyzes post-click GA4 behavior for Google Ads traffic. Landing page scoring, funnel drop-off mapping, session paths. Minimal changes from Meta version — nearly identical since all data from GA4. Filter utm_source=google. Writes to landing_pages table.
meta-ads-competitive-intel
by kayasinanPriority 4 agent. Monitors competitor ads via Meta Ad Library, scores ads by longevity (longer running = better performance signal), extracts Creative DNA (format, layout, color, copy structure, visual style), identifies Mode B replication candidates. Writes to competitors and competitor_ads tables.
meta-ads-creative-producer
by kayasinanPriority 5 agent. Generates production-ready ad creatives using Gemini vision AI. Three modes — Mode A (replicate own winners), Mode B (replicate competitor ads from Competitive Intel), Mode B-H (replicate human-submitted inspiration). Runs 6-point QC pipeline including text density check. Writes to creative_registry. Respects weekly_ad_volume and brand visual identity.
meta-ads-data-placement-analyst
by kayasinanPriority 1 agent. Pulls Meta Ads API + GA4 Data API data, reconciles triple-source metrics (Meta, GA4 True, AR Assumed Real), builds audience segments, detects cannibalization, monitors tracking health. Writes to daily_metrics, audiences, cannibalization_scores, tracking_health. The foundation — every other agent depends on this data.
meta-ads-orchestrator
by kayasinanCentral orchestrator for the Meta Ads AI Agent System. Manages 6-day optimization cycles, coordinates 7 sub-agents via SSH to Machine B, handles human creative inspiration input, proposes campaign scaling, enforces budget authority (human approves ALL spending), and delivers Cycle Summaries. Supports multi-brand operation. This is the brain — it decides WHAT to do and dispatches work.
meta-ads-orchestrator
by kayasinanCentral orchestrator for the Meta Ads AI Agent System. Manages 6-day optimization cycles, coordinates 7 sub-agents via SSH to Machine B, handles human creative inspiration input, proposes campaign scaling, enforces budget authority (human approves ALL spending), and delivers Cycle Summaries. Supports multi-brand operation. This is the brain — it decides WHAT to do and dispatches work.
meta-ads-setup
by kayasinanFirst-boot setup skill for the Meta Ads AI Agent System. Walks through 8 phases: self-check, Supabase schema deployment, credential collection, Machine B SSH connection, agent deployment, multi-brand onboarding, agent testing, and system readiness verification. Run this ONCE to initialize the entire system. Re-run specific phases for new brands or troubleshooting.
meta-ad-optimizer
by kayasinanExecute Meta (Facebook/Instagram) ad account changes — pause/enable ads, adjust budgets, add exclusions, consolidate campaigns, upload creatives. Use ONLY after meta-ad-analyst has produced recommendations AND the user has explicitly confirmed. Never execute without user approval.
google-ads-campaign-creator
by kayasinanPriority 6 agent. Builds campaign structures in Google Ads API — campaigns, ad groups, ads, keywords, extensions. All in PAUSED status. Three modes — Mode 1 (ad rotation), Mode 2 (ad group changes), Mode 3 (new campaign). Enforces naming conventions, UTM parameters, 22-point launch checklist. NEVER activates without human approval. Proposes budgets but NEVER commits them.
google-ads-setup
by kayasinanFirst-boot setup skill for the Google Ads AI Agent System. Walks through 8 phases: self-check, Supabase schema deployment, credential collection, Machine B SSH connection, agent deployment, multi-brand onboarding, agent testing, and system readiness verification. Run this ONCE to initialize the entire system. Re-run specific phases for new brands or troubleshooting.
meta-ads-setup
by kayasinanFirst-boot setup skill for the Meta Ads AI Agent System. Walks through 8 phases: self-check, Supabase schema deployment, credential collection, Machine B SSH connection, agent deployment, multi-brand onboarding, agent testing, and system readiness verification. Run this ONCE to initialize the entire system. Re-run specific phases for new brands or troubleshooting.
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.