machina
Gateway 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.
- Installs
- 432
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/machina-sports/sports-skills --skill machina- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads machina automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Machina Sports Intelligence Layer
Connect the agent harness to the Machina Sports premium infrastructure: zero-latency live streams, licensed betting odds, and packaged sports workflows. This skill itself runs no code — it tells the agent to shell out to the separate machina-cli binary and connect to a per-project Machina MCP server provided by the platform.
Quick Start
# 1. Install the CLI (one-time)
pipx install machina-cli
# or: uv tool install machina-cli
# or: python -m pip install --user machina-cli
# 2. Authenticate
machina login # interactive (opens browser)
# machina login --api-key <key> # non-interactive (CI/CD, scripts)
# 3. Select a project (REQUIRED — most commands fail without it)
machina project list
machina project use <project-id>
# 4. Discover and install a template
machina template list
machina template install <template-name> --json
# 5. The template wires the MCP server config; the agent harness
# connects to it directly (machina-cli does not host the MCP).
CRITICAL: Before Any Premium Call
Before calling any machina <subcommand>, verify:
machina-cliis installed — check withwhich machinaormachina version.- The user is authenticated —
machina auth whoamireturns a user. - A project is selected —
machina project use <id>has been run at least once.
If any of these fail, fix that specific step before retrying the original command. Do not loop on the same failing command.
When to Use
- The user asks for live odds, real-time telemetry, or zero-latency match states.
- The user wants a pre-configured sports workflow (e.g., "Build a Bundesliga podcast bot", "Create a Polymarket arbitrage engine").
- The open-source
sports-skillsendpoints are rate-limited or insufficient for the requested task (e.g., sub-second tick streams, licensed feeds, proprietary projections). - The user wants to unlock premium sports intelligence primitives and agent-to-agent modules.
Setup & Installation
1. Install the CLI
pipx install machina-cli
# or
uv tool install machina-cli
# or
python -m pip install --user machina-cli
Run this in the developer's terminal if you have permission, or ask them to run it.
Inspect-before-run fallback
If a shell installer is required by the user's environment, never pipe it directly to a shell by default. Download it, inspect it, then run it only after the user approves:
curl -fsSL https://raw.githubusercontent.com/machina-sports/machina-cli/main/install.sh -o /tmp/machina-install.sh
less /tmp/machina-install.sh
bash /tmp/machina-install.sh
2. Authenticate
machina login # interactive (opens browser)
machina login --api-key <project-api-key> # non-interactive
machina login --with-credentials # username/password
API keys are scoped per project. Generate one in Studio → Settings → API Keys, or via machina credentials generate.
3. Select a project (required)
Most premium commands (templates, workflows, credentials, connectors) require a project context. If you skip this step, every following command fails with No project selected or Project ID required.
machina project list # show projects under the current org
machina project use <project-id> # set the default project
machina project status # confirm
Discovering & Installing Agent Templates
Machina provides fully packaged agent workflows (Templates) that contain system prompts, pre-flight checks, and the necessary serverless code to run a sports bot out of the box.
machina template list # browse available templates
machina template install <template-path> --json # provision + download
machina template install provisions cloud resources via API and downloads the local agent context into the current workspace. Use --json for structured output that the agent can parse.
Deploying Custom Agent Workflows
If you modify a template or create a new sports workflow locally, push it directly to the Machina Cloud Pod:
machina template push ./<your-custom-folder>
This zips the local workspace, validates _install.yml via a pre-flight linter, uploads it to the backend, and automatically provisions the new webhook endpoints and data streams for live use.
Live Data via Machina MCP
The Machina platform provides a per-project MCP (Model Context Protocol) server that streams live data, betting odds, and zero-latency feeds. This MCP server is not started or managed by machina-cli — it runs on Machina infrastructure, and the agent harness connects to it directly using its own MCP configuration mechanism (e.g., .claude/mcp.json for Claude Code).
How it fits together:
machina template install <name>provisions the server-side workflow and returns the MCP URL and any required headers in its JSON output.- The agent harness's MCP config is updated to point to that URL.
- The agent calls MCP tools to read live streams. Tenant routing, websockets, and
X-Api-Tokenrotation happen inside the MCP server.
Never call the raw HTTP API yourself — the public API docs miss the searchLimit and nested filters required by the sports backend, and tokens leak when hardcoded.
Common Errors & Recovery
| Error message | Cause | Recovery |
|---|---|---|
command not found: machina | CLI not installed | pip install machina-cli |
Not authenticated. Run \machina login` first.` | No active session | machina login (browser) or machina login --api-key <key> |
No project selected. Run \machina project use <id>` first.` | Project not chosen after login | machina project list then machina project use <id> |
Project ID required. Set default or use --project. | Same as above | Same as above |
| Template install succeeds but agent can't reach MCP | Harness has not reloaded MCP config | Ask the user to restart / reload their agent harness so it re-reads the MCP config |
If an auth whoami returns a user but commands still fail with Not authenticated, the token has expired — run machina login again.
Commands that DO NOT exist — never call these
python -m sports_skills machina ...sports-skills. Shell out tomachina-clidirectly.machina mcp startmachina mcp connectmcpsubcommand. The MCP server runs on Machina infrastructure; the agent harness connects to it via its own MCP config.machina sports ...machina template runmachina template installprovisions them server-side); they don't run locally.
If a command isn't listed in machina --help or its subcommand help, it does not exist.
Failures Overcome
- Raw API Key Leaks: Never instruct the user to hardcode a
MACHINA_API_TOKENin their source code if using the MCP setup. The CLI handles shared context securely. - Pagination and Filtering Errors: Public API docs often miss the
searchLimitand nestedfiltersrequired by the sports backend. Installing a template automatically injects the correctworkflow.jsonconfig. - Lost Project Context: Most premium commands silently fail without a default project. Always run
machina project use <id>after a fresh login.
FAQ
What does the machina skill do?
Gateway 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.
How do I install machina?
Run npx skills add https://github.com/machina-sports/sports-skills --skill machina in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/machina/ (personal) or .claude/skills/machina/ (project).
Which AI agents can use machina?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is machina safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains machina?
It is published by machina-sports in machina-sports/sports-skills. It has 432 recorded installs.
More from machina-sports/sports-skills
football-data
machina-sports/sports-skills
Football (soccer) data across the world's major leagues — standings, schedules, match stats, xG, transfers, player profiles, head-to-head history, team strength (Elo), and match forecasts. Zero config, no API keys. Covers Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Champions League, World Cup, Championship, Serie B, Eredivisie, Primeira Liga, Serie A Brazil, Russian Premier League, Scottish/Belgian/Turkish top flights, European Championship, and more (call getcompetitions for the live list). Use when: user asks about football/soccer standings, fixtures, match stats, xG, lineups, player values, transfers, injury news, league tables, daily fixtures, player profiles, head-to-head records, team strength/Elo ratings, or match odds/forecasts. 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 tenn
betting
machina-sports/sports-skills
Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer probabilities). Use when: user asks about bet sizing, expected value, edge analysis, Kelly criterion, arbitrage, parlays, line movement, odds conversion, or comparing odds across sources. Also use when you have odds from ESPN and a prediction market price and want to evaluate whether a bet has positive expected value. Don't use when: user asks for live odds or market data — use polymarket, kalshi, or the sport-specific skill to fetch odds first, then use this skill to analyze them.
polymarket
machina-sports/sports-skills
Polymarket sports prediction markets — read-only live odds, prices, order books, events, series, and market search. No auth required. Covers NFL, NBA, MLB, football (EPL, UCL, La Liga), tennis, cricket, MMA, esports. Supports moneyline, spreads, totals, and player props. Use when: user asks about sports betting odds, prediction markets, win probabilities, market sentiment, or "who is favored to win" questions and only needs read-only market intelligence. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill. Don't use for historical match data. Don't use for news — use sports-news instead. Don't use for placing, cancelling, or managing orders — use the separate polymarket-trading skill only after explicit user approval.
sports-news
machina-sports/sports-skills
Sports news via RSS/Atom feeds and Google News. Fetch headlines, search by query, filter by date. Covers football news, transfer rumors, match reports, and any sport via Google News. Use when: user asks for recent news, headlines, transfer rumors, or articles about any sport. Good for "what's the latest on [team/player]" questions. Supports any Google News query and curated RSS feeds (BBC Sport, ESPN, The Athletic, Sky Sports). Don't use when: user asks for structured data like standings, scores, statistics, or xG — use the sport-specific skill instead: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for prediction market odds — use polymarket or kalshi. News results are text articles, not structured data.
kalshi
machina-sports/sports-skills
Kalshi prediction markets — events, series, markets, trades, and candlestick data. Public API, no auth required for reads. US-regulated exchange (CFTC). Covers football (EPL, UCL, La Liga), basketball, baseball, tennis, NFL, hockey event contracts. Use when: user asks about Kalshi-specific markets, event contracts, CFTC-regulated prediction markets, or candlestick/OHLC price history on sports outcomes. Don't use when: user asks about actual match results, scores, or statistics — use the sport-specific skill: football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), tennis-data (tennis), golf-data (golf), cricket-data (cricket), cfb-data (college football), cbb-data (college basketball), or fastf1 (F1). Don't use for general "who will win" questions unless Kalshi is specifically mentioned — try polymarket first (broader sports coverage). Don't use for news — use sports-news instead.
nba-data
machina-sports/sports-skills
NBA data via ESPN public endpoints, the NBA live CDN, and NBA Stats (stats.nba.com) — scores, standings, rosters, schedules, game summaries, play-by-play, injuries, futures, depth charts, leaders, and news, plus an analytics backend: advanced ratings, per-shot court coordinates, career splits, and history to 1946. 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, NBA news, shot charts, advanced ratings/pace, career stats, or historical NBA seasons. Don't use when: user asks about WNBA (use wnba-data), college basketball (use cbb-data), or other sports.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available