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name: machina description: | 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. license: MIT metadata: author: machina-sports version: "0.3.0" risk: mode: premium_mcp money_movement: false secrets_required: true external_mcp: true cloud_upload: true requires_explicit_confirmation: true

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-cli is installed — check with which machina or machina version.
• The user is authenticated — machina auth whoami returns 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-skills endpoints 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:

1. machina template install <name> provisions the server-side workflow and returns the MCP URL and any required headers in its JSON output.
2. The agent harness's MCP config is updated to point to that URL.
3. The agent calls MCP tools to read live streams. Tenant routing, websockets, and X-Api-Token rotation 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 ` 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 ... — this skill has no CLI module under sports-skills. Shell out to machina-cli directly.
• machina mcp start / machina mcp connect — there is no mcp subcommand. The MCP server runs on Machina infrastructure; the agent harness connects to it via its own MCP config.
• machina sports ... — does not exist. Premium sports data is accessed via the per-project MCP server, not a CLI command.
• machina template run — templates are deployed (machina template install provisions 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_TOKEN in their source code if using the MCP setup. The CLI handles shared context securely.
• Pagination and Filtering Errors: Public API docs often miss the searchLimit and nested filters required by the sports backend. Installing a template automatically injects the correct workflow.json config.
• Lost Project Context: Most premium commands silently fail without a default project. Always run machina project use <id> after a fresh login.