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...
pixel-capi
by TheMattBermanMeta Pixel + Conversions API (CAPI) setup, audit, testing, and EMQ optimization. Covers browser pixel installation, server-side CAPI implementation, deduplication, advanced matching, and Event Match Quality scoring across all major platforms.
meta-ads
by TheMattBermanMeta Ads management and reporting — daily checks, campaign performance, creative fatigue, bleeders, winners. Uses a local adapter over Meta Ads CLI in mock/read-only/live-approved modes.
ad-creative-monitor
by TheMattBermanTrack creative performance over time and detect fatigue before it kills ROAS. Monitors CTR decay, frequency creep, and CPC inflation at the ad level.
ad-upload
by TheMattBermanPrepare and upload Meta ad copy/images with official Ads CLI-first workflows where supported. Builds payloads, enforces dry-run/PAUSED-only guardrails, and creates or refreshes ads only after approval. Downstream of ad-copy-generator.
budget-optimizer
by TheMattBermanAnalyze spend efficiency across campaigns and adsets. Recommends budget shifts from underperformers to winners.
ad-copy-generator
by TheMattBermanGenerate high-converting Meta ad copy matched to specific image creatives. Analyzes visuals, writes copy that reinforces the image, cross-references account performance data through the official Ads CLI adapter when available, and outputs payload-ready variants.
google-ads-apply
by TheMattBermanExecute approved draft actions in Google Ads accounts. Supports four safe mutation types plus bounded budget writes: add campaign-level negatives, add ad-group-level negatives, pause keywords, pause ad groups, and set campaign daily budgets through an Apply Manifest. Requires human confirmation via dry-run → approve → execute → verify → audit flow. All actions are logged in the audit trail and fully reversible.
google-ads-tracking
by TheMattBermanDiagnose whether a Google Ads account's conversion tracking is trustworthy enough to optimize. Pulls live data via MCP or works with manual exports. Produces tracking fix drafts when problems are found.
google-ads-rsas
by TheMattBermanGenerate or refine Google Ads RSA recommendations using real buyer language from search terms, intent clusters, and winning modifiers. Pulls live data via MCP or works with manual exports. Produces RSA refresh drafts.
google-ads-negatives
by TheMattBermanRecommend negative keywords for Google Ads with the right match type, scope, and caution. Pulls live data via MCP or works with manual exports. Always produces a negative keyword draft for human review.
google-ads-landing-review
by TheMattBermanDiagnose landing page → conversion path problems for Google Ads traffic. Separates tracking failures from UX/path failures — the two most commonly confused sources of "low conversion rate." Pulls ad and campaign data via MCP, then reviews landing pages via browser or URL fetch to assess message match, load issues, form friction, and conversion path completeness. Produces a landing-review draft when problems are found.
google-ads-daily
by TheMattBermanDaily operator review for Google Ads. Surfaces what changed, where waste is showing up, where signal is emerging, what is ready to scale, and what should be left alone. Pulls live data via MCP or works with manual exports.
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.