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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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world-cup
by machina-sportsPremium FIFA World Cup 2026 market & match intelligence — a hosted, read-only layer that fuses official match truth (fixtures, standings, squads, injuries, player performance) with live prediction markets (Kalshi + Polymarket: prices, order books, price history, movers, cross-venue edges) and AI-grounded context (prematch briefs, move explanations, fan/social pulse). Every entity carries a canonical machina URN cross-walked across api-football, sportradar, opta, entain and ESPN, so a market resolves to a fixture resolves to two teams. This skill is prompt-only and premium: it routes the agent to the hosted World Cup Intelligence project (a per-project Machina MCP server) via `machina-cli`. It runs no code locally and ships no API keys. Use when: the user wants World Cup 2026 odds + match context together, asks "what moved and why", wants a grounded market brief or fan-sentiment read on a fixture, or needs one stable id that joins markets ↔ fixtures ↔ teams across providers. Don't use when: the user wants fr
machina
by machina-sportsGateway to the Machina Sports premium platform — packaged agent workflows ("templates"), licensed real-time data, betting odds, and zero-latency live streams. This skill is prompt-only: it shells out to the separate `machina-cli` binary and routes the agent to a per-project Machina MCP server. Use when: the user asks for live odds, real-time telemetry, zero-latency match states, sub-second tick streams, packaged sports workflows (e.g., "Build a Bundesliga podcast bot", "Create a Polymarket arbitrage engine"), or when the open-source sports-skills are rate-limited or insufficient for the task. Don't use when: the user wants snapshot data from public APIs — use the sport-specific skill (nfl-data, polymarket, markets, …). Don't use to fetch data through raw HTTP — use the Machina MCP server, not a `requests` call.
football-data
by machina-sportsFootball (soccer) data across 13 leagues — standings, schedules, match stats, xG, transfers, player profiles. Zero config, no API keys. Covers Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Champions League, World Cup, Championship, Eredivisie, Primeira Liga, Serie A Brazil, European Championship. Use when: user asks about football/soccer standings, fixtures, match stats, xG, lineups, player values, transfers, injury news, league tables, daily fixtures, or player profiles. Don't use when: user asks about American football/NFL (use nfl-data), college football (use cfb-data), NBA (use nba-data), WNBA (use wnba-data), college basketball (use cbb-data), NHL (use nhl-data), MLB (use mlb-data), tennis (use tennis-data), golf (use golf-data), cricket (use cricket-data), Formula 1 (use fastf1), or betting odds (use polymarket or kalshi). Don't use for live/real-time scores — data updates post-match. Don't use get_season_leaders or get_missing_players for non-Premier League leagues (they return empty). D
cfb-data
by machina-sportsCollege Football (CFB) data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, rankings, injuries, futures, team/player stats, and news for NCAA Division I FBS. Zero config, no API keys. Use when: user asks about college football scores, standings, rankings, team rosters, schedules, game results, play-by-play, injuries, betting futures, team/player statistics, or CFB news. Don't use when: user asks about NFL (use nfl-data), college basketball (use cbb-data), or non-sports topics.
wnba-data
by machina-sportsWNBA data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, win probability, injuries, transactions, futures, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about WNBA scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, or WNBA news. Don't use when: user asks about NBA (use nba-data), college basketball (use cbb-data), or other sports.
nba-data
by machina-sportsNBA data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, win probability, injuries, transactions, futures, depth charts, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about NBA scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, depth charts, team/player statistics, or NBA news. Don't use when: user asks about WNBA (use wnba-data), college basketball (use cbb-data), or other sports.
nhl-data
by machina-sportsNHL data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, injuries, transactions, futures, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about NHL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, or NHL news. Don't use when: user asks about other hockey leagues (AHL, KHL, college hockey). For other sports use: nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), mlb-data (MLB), football-data (soccer), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), fastf1 (F1). For betting odds use polymarket or kalshi. For news use sports-news.
sports-reporter
by machina-sportsGenerates original sports journalism articles by consuming real-time data from the sports-skills skills. Covers game previews, live reports, post-game, team analysis, and player profiles for all supported sports. Use when: the user asks to write, generate, create, or draft an article, preview, report, analysis, summary, or journalistic coverage about games, teams, players, scores, statistics, or sports results. Do not use when: the user only wants raw data without journalistic text — use the sport-specific skill directly (nfl-data, nba-data, football-data, etc.). Do not use when: the user wants to search for news published by third parties — use sports-news.
volleyball-data
by machina-sportsDutch volleyball data (Eredivisie, Topdivisie, Superdivisie, and the full Dutch pyramid) via the Nevobo API. Standings, schedules, results, clubs, tournaments, and news. Zero config, no API keys. Use when: user asks about Dutch volleyball, Eredivisie volleyball, Nevobo, volleyball standings, volleyball match results, volleyball schedules, or Dutch volleyball clubs. Don't use when: user asks about other sports — use football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), tennis-data (tennis), or fastf1 (F1). For betting odds use polymarket or kalshi. For general news use sports-news.
cbb-data
by machina-sportsCollege Basketball (CBB) data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, win probability, rankings, futures, team/player stats, and news for NCAA Division I men's basketball. Zero config, no API keys. Use when: user asks about college basketball scores, March Madness, NCAA tournament, standings, rankings, team rosters, schedules, play-by-play, betting futures, team/player statistics, or CBB news. Don't use when: user asks about NBA/WNBA (use nba-data/wnba-data), college football (use cfb-data), or non-sports topics.
nfl-data
by machina-sportsNFL data via ESPN public endpoints plus an nflverse backend for schedules, weekly rosters, play-by-play, and normalized player/team stat tables. Zero config, no API keys. Use when: user asks about NFL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, depth charts, team/player statistics, or NFL news. Don't use when: user asks about football/soccer (use football-data), college football (use cfb-data), or other sports.
xctf-data
by machina-sportsNCAA cross country and track & field athlete data via TFRRS (tfrrs.org) and news via The Stride Report. Fetch athlete profiles including all personal records (PRs), eligibility year, school, full season-by-season results history, and XC/TF news. Zero config, no API keys. Use when: user asks about NCAA cross country, NCAA track and field, college running, TFRRS athlete profiles, personal records, PRs, XC or TF season results, individual athlete performance history, or XC/TF news. Don't use when: user asks about professional track, Diamond League, or other sports — use nfl-data, nba-data, wnba-data, nhl-data, mlb-data, golf-data, cricket-data, cfb-data, cbb-data, tennis-data, fastf1, or volleyball-data. For betting use polymarket or kalshi.
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.