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...
award-sweet-spots
by borskiCatalog of high-value award redemptions where points dramatically outvalue cash. Tiered by legendary/excellent/good with current rates, devaluation history, and booking caveats.
cabin-codes
by borskiIATA cabin codes (F/J/W/Y) and saver fare class codes (X/I/O) for reading award inventory and determining partner-bookable availability.
southwest
by borskiSearch Southwest Airlines fares and points pricing via Patchright browser automation. SW is not in any GDS or API. Covers all fare classes, Companion Pass value, and fare drop monitoring.
transfer-bonuses
by borskiActive credit card transfer bonuses from Amex, Chase, Capital One, Citi, Bilt, and Rove. Weekly-refreshed data with confidence markers. Use when pricing an award booking that involves a points transfer or deciding whether to wait for a better bonus.
trip-calculator
by borskiCash vs points trip cost comparison. Factors in transfer ratios, taxes, fees, CPP valuations, and opportunity cost to recommend the best redemption strategy for flights and hotels.
tripadvisor
by borskiTripAdvisor Content API for hotel ratings, restaurant search, attraction reviews, rankings, and nearby locations. Use when evaluating hotels or researching destinations. 5K calls/month.
compare-flights
by borskiUnified flight comparison across cash (Duffel, Ignav), award (seats.aero), and portal (Chase, Amex) sources in parallel. Outputs one table with transfer partner optimization and recommendations.
status-match
by borskiStatus match and status challenge rules for airlines and hotels. Covers free direct matches, paid concierge via statusmatch.com with fees, card-granted status, and critical lifetime/once-per-N-years restrictions that make wasted matches expensive.
round-the-world
by borskiRound-the-world, Circle Pacific, and distance-based award products across alliances. Covers Star Alliance RTW, oneworld Explorer, Lufthansa M&M, Qantas, Cathay, JAL, Aeroplan, and Iberia charts.
stopovers
by borskiPer-program stopover rules for award redemptions. Which programs allow free stopovers, how many, how long, and which don't. Covers Icelandair, Aeroplan, Alaska, Flying Blue, Singapore, Cathay, JAL, and the negative space (BA, AA, Delta).
ticketsatwork
by borskiSearch TicketsAtWork (EBG corporate perks) for hotels, theme park tickets, attractions, and rental cars via Patchright. Often beats portals by 10-30%. Use when comparing hotel or ticket prices for a trip.
alliances
by borskiAirline alliance membership (Star Alliance, oneworld, SkyTeam) and cross-alliance booking relationships. Maps which loyalty programs book which airlines, including bilateral partnerships outside alliances.
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.