$ ~/registry/skill/composiohq-composio-skills-bonsai-automation

SKILL

bonsai-automation

Name: bonsai-automation
Author: Composio

Automate Bonsai tasks via Rube MCP using current tool schemas first.

Stars

★ 67,763

Source

GitHub

Updated

2026-07-15

// securityCaution

Prompt only, no code
Open source
Community-trusted· 64.7k

Summary

This skill is an open-source prompt wrapper from a highly trusted GitHub source, with no clear red flags for local execution or local file access. The main caution is its reliance on remote Rube MCP/Composio and a Bonsai authorization flow, so actual data egress and account access depend on the external service and granted scopes.

CredentialsCaution

The material says no API key is needed, but it requires an active Bonsai connection via `RUBE_MANAGE_CONNECTIONS` and completing a returned auth link, indicating third-party OAuth/connection credentials are still involved. The main concern is the granted scope and external token handling, not this skill collecting local environment secrets.

Network egressCaution

The README explicitly requires adding the remote MCP endpoint `https://rube.app/mcp` and executing queries/operations through Composio/Bonsai tools. User task descriptions, arguments, session IDs, and Bonsai-related data will be sent to the declared external service. The endpoint is identifiable, but data does leave the local environment.

Code executionLow risk

Based on the material, this is a prompt-only skill that instructs use of existing Rube MCP tools. It does not describe local install scripts, shell execution, spawning local processes, or requesting system-level permissions. Its 'execution' appears to happen in remote tools rather than as local code execution.

Data accessCaution

There is no stated direct access to the local filesystem or local device data, but once the Bonsai connection is active, remote tools can access and operate on Bonsai account resources within the granted scope. The README also mentions session handling, memory, pagination, and bulk operations, suggesting it may process broader business datasets.

Supply chainCaution

Positive factors are that it comes from a public GitHub repository, is open source, and has high community adoption (64.7k stars), which materially lowers supply-chain risk. However, the skill itself is mainly a wrapper around external Rube/Composio services; the license is unspecified, maintenance is unknown, and actual behavior depends on dynamic tool schemas and remote services, so some caution is still warranted.

Recommendations

Enable it only when Bonsai automation is actually needed, and review the OAuth/connection scopes with least privilege in mind.
Clearly identify and monitor egress to `https://rube.app/mcp` in the client, and avoid sending unnecessary sensitive business content.
Review Rube/Composio privacy, retention, and logging practices before use, especially how session IDs, memory, and execution arguments are stored.
Validate behavior in a test account or restricted Bonsai workspace before using it with production data.

Audited by: gpt-5.4 · 2026-06-17

// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "bonsai-automation" skill from askskill:
1. Download https://raw.githubusercontent.com/ComposioHQ/awesome-claude-skills/master/composio-skills/bonsai-automation/SKILL.md
2. Save it as ~/.claude/skills/bonsai-automation/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// docs

Bonsai Automation via Rube MCP

Automate Bonsai operations through Composio's Bonsai toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/bonsai

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
Active Bonsai connection via RUBE_MANAGE_CONNECTIONS with toolkit bonsai
Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
Call RUBE_MANAGE_CONNECTIONS with toolkit bonsai
If connection is not ACTIVE, follow the returned auth link to complete setup
Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS
queries: [{use_case: "Bonsai operations", known_fields: ""}]
session: {generate_id: true}

This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS
queries: [{use_case: "your specific Bonsai task"}]
session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["bonsai"]
session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL
tools: [{
  tool_slug: "TOOL_SLUG_FROM_SEARCH",
  arguments: {/* schema-compliant args from search results */}
}]
memory: {}
session_id: "your_session_id"

Known Pitfalls

Always search first: Tool schemas change. Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS
Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools
Schema compliance: Use exact field names and types from the search results
Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({})
Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows
Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference

Operation	Approach
Find tools	`RUBE_SEARCH_TOOLS` with Bonsai-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `bonsai`
Execute	`RUBE_MULTI_EXECUTE_TOOL` with discovered tool slugs
Bulk ops	`RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`
Full schema	`RUBE_GET_TOOL_SCHEMAS` for tools with `schemaRef`

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