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

SKILL

bigml-automation

Name: bigml-automation
Author: Composio

Automate BigML tasks and machine learning workflows through Rube MCP.

Stars

★ 67,807

Source

GitHub

Updated

2026-07-15

// securityCaution

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

Summary

Overall this appears to be a prompt-only skill with no declared secrets or fixed remote endpoint, and it comes from a high-star open-source repository, so no clear high-risk red flags are evident. The main caution is that it relies on Rube MCP and a BigML connection, so normal tool invocation and data handling will occur at runtime.

CredentialsLow risk

No credentials or tokens are required in the materials; it explicitly states that no API keys are needed and connection is handled via Rube MCP. There is no clear credential exposure or abuse vector here.

Network egressLow risk

No fixed business egress endpoint is declared; only Rube MCP and Composio/BigML-related connectivity are mentioned. This is within the stated scope and shows no red flag for sending data to unknown destinations.

Code executionCaution

This is an orchestration-style skill and does not itself expose local process spawning or arbitrary code execution, but it does invoke external tools such as RUBE_SEARCH_TOOLS, RUBE_MANAGE_CONNECTIONS, and RUBE_MULTI_EXECUTE_TOOL, which is normal tool-execution risk.

Data accessCaution

Data access depends on the BigML connection and the specific tool schemas selected; the README instructs discovering tools first and then executing by schema, indicating access to BigML-side resources, but no excessive local file access beyond the declared function is shown.

Supply chainCaution

The source is a GitHub open-source repository with strong community adoption (64.7k stars), which is positive; however, the license is unspecified, maintenance status is unknown, and the README depends on the third-party Rube/Composio ecosystem, so routine supply-chain verification is still warranted.

Recommendations

Enable Rube MCP only when needed and confirm the BigML connection identity and permission scope.
Check the tool schemas returned by RUBE_SEARCH_TOOLS before execution to avoid using stale documentation.
Apply least-privilege and minimum-data principles for BigML operations that import or export data.
For production use, дополнate/verify the repository license, maintenance activity, and dependency audit.

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

// download

Download SKILL.mdInstall manifest ↗

// docs

Bigml Automation via Rube MCP

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

Toolkit docs: composio.dev/toolkits/bigml

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
Active Bigml connection via RUBE_MANAGE_CONNECTIONS with toolkit bigml
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 bigml
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: "Bigml 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 Bigml task"}]
session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["bigml"]
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 Bigml-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `bigml`
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`

Powered by Composio

Automate curated workflows with Rube MCP after checking current tool schemas first.

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