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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3f-competitor-sitemap-analyst
by anandan-digital-marketerReverse-engineers a competitor's full SEO strategy from their sitemap. Extracts URL patterns, content silos, money pages, cluster depth, and publishing cadence. Outputs 5 moves to match and 5 gaps to attack. Run on each top competitor once per quarter.
3h-topical-cluster-builder
by anandan-digital-marketerMaps the site's content into topical clusters (hub/spoke model). Identifies which pillar pages exist, which spokes support them, and which spokes are missing. Outputs a cluster map per topic, with a content creation priority list to fill gaps and a linking plan to connect existing content. Feeds into the internal linking strategist (2C) and content brief generator (2A).
3g-paa-harvester
by anandan-digital-marketerHarvests People Also Ask (PAA) questions for a target keyword or topic. Categorises questions by intent type, identifies which ones have featured snippet opportunities, and outputs ready-to-use FAQ section content and content brief inputs. PAA questions are high-value LLM citation targets.
1b-single-page-scorer
by anandan-digital-marketerDeep SEO audit on a single URL. Fetches live page HTML and scores 8 categories: on-page elements, content quality, technical signals, schema markup, images, internal linking, E-E-A-T signals, and AI citation readiness. Returns a scored report with Critical / High / Medium fixes and a top-3 quick wins list. Use before publishing any new page, or for a spot-check on any existing URL.
2b-meta-optimizer
by anandan-digital-marketerBatch rewrites title tags and meta descriptions for pages with high impressions but low CTR. For each URL: fetches current title and meta, pulls GSC performance data, analyzes what top CTR pages do differently, and outputs 3 title options and 2 meta options per URL. Outputs an upload-ready CSV for bulk implementation. Primary use case: the 105 CTR-crisis pages identified in the Director report (>3K impressions, <1% CTR).
1m-redirect-implementation
by anandan-digital-marketerGenerates ready-to-implement 301 redirect rules from the blog redirect plan. Reads the existing redirect analysis Excel file, takes the 189 DELETE and 19 REDIRECT entries, and outputs .htaccess rules, Nginx config blocks, and a WordPress Redirection plugin import CSV — whichever format is needed. The analysis was done in January 2026. This agent implements it.
2a-content-brief-generator
by anandan-digital-marketerGenerates a complete 10-point content brief for a target keyword. Pulls live GSC data for the keyword, fetches top 5 SERP results, checks for cannibalization against existing content, and outputs a structured brief ready for a writer. Includes LLM citation optimizations — direct-answer-first, Q&A blocks, schema suggestions. Writer gets zero guesswork.
2c-internal-linking-strategist
by anandan-digital-marketerBuilds an internal linking plan that pushes link equity to commercial pages without breaking topical clusters. Given a list of site URLs or a target page, outputs 20 specific source→target link pairs with anchor text, 5 links to remove or change, and a topic cluster map. Run quarterly and after every batch of 5+ new pages published.
1n-sitemap-manager
by anandan-digital-marketerGenerate a fresh XML sitemap from live GSC data, or validate an existing sitemap against GSC indexed URLs. Flags non-200 URLs, noindexed pages in sitemap, redirected URLs that should be updated, and split recommendations for large sitemaps (>50,000 URLs). Also generates the robots.txt sitemap reference line.
1d-schema-generator
by anandan-digital-marketerGenerates complete, valid JSON-LD structured data markup from scratch for any page type. Detects existing schema on the page, flags deprecated types, validates required fields, and outputs ready-to-paste JSON-LD blocks. Use when adding schema to a new page, auditing an existing page's structured data, or generating schema for a batch of pages.
6g-review-aggregator
by anandan-digital-marketerPulls G2 and Capterra ratings and recent reviews on demand. Aggregates review sentiment, identifies most-cited strengths and weaknesses, tracks rating trends, and surfaces review content useful for VS pages (6C) and trust signals on landing pages. Also flags competitor review trends.
6a-backlink-monitor
by anandan-digital-marketerTracks new and lost backlinks for yourdomain.com and top competitors using the Semrush MCP (already connected). Weekly comparison: which sites linked to us this week, which links were lost, and what competitors gained. Outputs a prioritised link intelligence report with action items.
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.