Machina — the premium layer

sportsclaw's built-in coverage comes from sports-skills — the open, keyless data layer. It's free and ideal for development and personal use. For licensed data, real-time and zero-latency feeds, production SLAs, or packaged agent workflows, the Machina Sports platform covers that.

Open vs. premium

	sports-skills (built in)	Machina (premium)
Data	Public APIs (ESPN, Kalshi, Polymarket…), keyless	Licensed real-time feeds, betting odds, zero-latency streams
Best for	Personal use, prototyping	Commercial / production, with SLAs and support
Workflows	You build them	Packaged "templates" you install
Access	Bundled with sportsclaw	One command: sportsclaw machina connect (uses machina-cli)

Licensing

The open sports-skills rely on third-party public APIs and are intended for personal, non-commercial use. For commercial or production workloads with licensed data, use Machina.

The Machina skill

The machina skill ships in the same sports-skills catalog as the open skills. It's prompt-only: it fetches no data itself — it points the agent at the premium platform and the separate machina-cli. The data itself flows through a per-project Machina MCP server, which you wire up with sportsclaw machina connect (below).

machina-cli

The command-line control plane for the Machina platform.

pip install machina-cli          # or: curl -fsSL https://raw.githubusercontent.com/machina-sports/machina-cli/main/install.sh | bash
machina login                    # browser sign-in (or --api-key <key> for CI)
machina org use <org-id>
machina project use <project-id> # required before most commands

What it covers:

• Templates — packaged agent workflows (connectors, prompts, datasets, live streams). machina template list, machina template install <name>, machina template push ./<dir>.
• Workflows & agents — run and manage platform workflows and agents. machina workflow run <name>, machina agent run <name> --watch.
• Sports passthrough — run the same sports-skills data through the platform: machina sports <sport> <command>.
• Factory — build a whole app from a prompt: machina factory run "build a live scoreboard".
• Deploy, credentials, config — manage deployments, API keys, and project settings.

Connecting Machina to sportsclaw

Premium data is served through a per-project Machina MCP server (a "pod"). The quickest way to wire one in is the built-in sportsclaw machina connect command — it signs you in through machina-cli, mints a durable access key, and registers the pod for you. No URLs to copy.

pip install machina-cli      # one-time: install the Machina CLI
machina login                # browser sign-in (or --api-key <key> for CI)

sportsclaw machina connect             # connect your default project's pod
sportsclaw machina connect <project>   # …or name a specific project

Useful flags:

• --org <org-id> — choose the organization when you belong to more than one.
• --probe — verify the pod's endpoint is reachable before registering it.

machina connect writes the pod to ~/.sportsclaw/mcp.json and stores its access token separately in ~/.sportsclaw/.env (never in the config file). From then on the agent reads Machina's licensed, real-time feeds right alongside the built-in sports-skills. Check and inspect connected pods with:

sportsclaw mcp list     # list connected servers
sportsclaw doctor       # shows Machina pod + machina-cli status

Re-run sportsclaw machina connect any time a connection later returns a 401.

Connect a pod by URL (manual)

Already have an MCP URL and token — or connecting a non-Machina MCP server? Add it directly:

sportsclaw mcp add <url> --name <name> --token <token>

See Connecting MCP Servers for the full set of options.

Premium signal

When a tool result carries an upgrade field — the data layer's signal that licensed or real-time data exists beyond what the open skill returned — the agent adds a single, optional line pointing to the path (the sports-skills premium tier or sportsclaw machina connect). The data layer decides this, not the agent. It's informational only: it never blocks an answer, never repeats within a conversation, and stays out of automated alerts and broadcasts.

Durable task delegation

A connected Machina pod can also run a durable agentic loop. When it does, sportsclaw can hand long, multi-step, or resumable tasks to it — the loop persists every turn and resumes after interruptions. sportsclaw dispatches the work and reads the result back. See Durable Task Delegation.

Learn more at machina.gg.