machina: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

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.

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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:

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-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.

View source ↗

File metadata

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

View original text

---
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

```bash
# 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

```bash
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:

```bash
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

```bash
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`.

```bash
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.

```bash
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:

```bash
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 <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 ...`~~ — 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.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
The skill relies on an external CLI and MCP server, which introduces supply-chain and data-exfiltration risks, but these are mitigated by explicit confirmation requirements and inspect-before-run guidance.

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: MIT
Version: 1.0.0
Last GitHub push: Sep 4, 2026
Registry updated: Sep 6, 2026
Instruction path: skills/machina/SKILL.md @ bf996fb9fb85

Version reported in registry metadata; check source releases before relying on it.

Quality

70/100

Strong

Trust

57/100

Do not auto-install

Audit

75/100

Needs review

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "The skill relies on an external CLI and MCP server, which introduces supply-chain and data-exfiltration risks, but these are mitigated by explicit confirmation requirements and inspect-before-run guidance.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 211 stars, 30 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "The skill relies on an external CLI and MCP server, which introduces supply-chain and data-exfiltration risks, but these are mitigated by explicit confirmation requirements and inspect-before-run guidance.",
      "The skill does not specify what data is transmitted to the Machina MCP server, though the risk metadata discloses cloud upload.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 70,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "7d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 85,
      "audit_score": 93
    },
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 60956,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill relies on an external CLI and MCP server, which introduces supply-chain and data-exfiltration risks, but these are mitigated by explicit confirmation requirements and inspect-before-run guidance.",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use machina in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "machina-sports-machina (machina)",
      "install_command": "npx skills add machina-sports/sports-skills --skill machina",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "machina-sports-machina",
      "task": "Use machina in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/machina-sports-machina",
    "api": "https://www.openagentskill.com/api/agent/skills/machina-sports-machina",
    "audit": "https://www.openagentskill.com/skills/machina-sports-machina/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=machina-sports-machina&task=Use%20machina%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20machina%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20machina%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/machina-sports-machina/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/machina-sports-machina"
  }
}

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Creator: machina-sports
Source: machina-sports/sports-skills
Indexed by: OpenAgentSkill community index

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