entelligence-automation
by ComposioHQentelligence-automation helps Claude automate Entelligence tasks through Composio Rube MCP by checking connections and searching current tool schemas before execution.
This skill scores 66/100, which means it is acceptable for directory listing but should be presented as a lightweight MCP-routing skill rather than a full Entelligence workflow pack. Directory users get enough information to know it requires Rube MCP and an active Entelligence connection, but they should expect to rely on runtime tool discovery because the repository evidence does not show detailed Entelligence-specific examples or support materials.
- Valid skill frontmatter declares the required Rube MCP dependency and a concise purpose: automating Entelligence tasks through Composio.
- Prerequisites and setup steps clearly tell users to connect Rube MCP, manage the Entelligence connection, and verify ACTIVE status before workflows.
- The skill gives an explicit operational pattern to call RUBE_SEARCH_TOOLS first, which improves schema correctness and triggerability for agents.
- No support files, scripts, reference docs, or README are included beyond SKILL.md, so adoption depends on the generic Rube MCP flow.
- The evidence shows little Entelligence-specific workflow detail; agents must discover current tools and schemas at runtime rather than following concrete task recipes.
Overview of entelligence-automation skill
What entelligence-automation is for
entelligence-automation is a Claude skill for automating Entelligence operations through Composio’s Rube MCP server. Its main value is not a fixed one-click workflow; it teaches the agent to discover the current Entelligence tool schemas first, confirm the connection state, and then execute the right Rube tool calls with fewer assumptions.
Use this skill when you want Claude to act as an operator for Entelligence-related workflow automation instead of writing free-form instructions that may not match the latest Composio toolkit schema.
Best-fit users and jobs
The entelligence-automation skill is best for users who already have, or can create, an Entelligence connection in Composio and want an AI agent to run repeatable tasks through MCP. It fits teams that care about tool-backed execution, connection checks, and schema-aware prompts.
It is especially useful when your request depends on live tool availability, because the skill explicitly requires RUBE_SEARCH_TOOLS before execution. That discovery step helps prevent stale parameter names, wrong tool slugs, and hallucinated Entelligence actions.
Key differentiators for Workflow Automation
For Workflow Automation, the important differentiator is the skill’s discovery-first operating pattern. Instead of assuming a hardcoded Entelligence API shape, the agent should:
- verify Rube MCP access,
- check the Entelligence connection,
- search for the current tools and schemas,
- execute only after the active connection and inputs are clear.
That makes entelligence-automation more suitable for MCP-based operational work than a generic “automate Entelligence” prompt.
Adoption constraints to check first
Before installing or relying on this skill, confirm that your Claude client supports MCP and can connect to Rube at https://rube.app/mcp. You also need an active Entelligence connection managed through RUBE_MANAGE_CONNECTIONS with toolkit entelligence.
The repository contains a single SKILL.md and no bundled scripts, examples, rules, or reference files. Treat it as a compact execution pattern, not a complete automation library.
How to Use entelligence-automation skill
entelligence-automation install and setup path
Install the skill from the GitHub skill directory context with:
npx skills add ComposioHQ/awesome-claude-skills --skill entelligence-automation
Then configure Rube MCP in your client by adding:
https://rube.app/mcp
After MCP is available, verify that RUBE_SEARCH_TOOLS responds. Next, use RUBE_MANAGE_CONNECTIONS with toolkit entelligence and complete any returned authorization flow. Do not ask the agent to run Entelligence workflows until the connection status is ACTIVE.
Inputs the skill needs from you
For reliable entelligence-automation usage, give the agent a concrete operational goal, the relevant object names or identifiers, desired output format, and any safety boundaries. The skill can discover schemas, but it cannot infer your business intent or approval rules.
Weak prompt:
Automate my Entelligence task.
Stronger prompt:
Use entelligence-automation to find the current Entelligence tools through Rube MCP, confirm my Entelligence connection is active, then prepare an execution plan for updating the specified records. Do not execute changes until you show the tool slug, required fields, and a dry-run summary.
This works better because it separates discovery, planning, validation, and execution.
Practical workflow for first run
Start by reading SKILL.md in composio-skills/entelligence-automation. The most important sections are Prerequisites, Setup, Tool Discovery, and Core Workflow Pattern.
A safe first run should follow this sequence:
- Ask Claude to call
RUBE_SEARCH_TOOLSfor your specific Entelligence use case. - Review returned tool slugs, schemas, execution plans, and pitfalls.
- Ask Claude to check or establish the Entelligence connection with
RUBE_MANAGE_CONNECTIONS. - Provide required parameters based on the discovered schema.
- Request a plan or dry run before allowing state-changing actions.
Prompt pattern for better results
A high-signal prompt should include the task, constraints, and verification step:
Use the entelligence-automation skill. First search Rube tools for: “[specific Entelligence task]”. Use the returned schema only. If the Entelligence connection is not active, stop and give me the auth instructions. Before executing, summarize the selected tool, required fields, missing inputs, and expected result.
This prompt aligns with the skill’s built-in rule: always search tools first for current schemas.
entelligence-automation skill FAQ
Is entelligence-automation useful without Rube MCP?
No. The entelligence-automation skill requires Rube MCP. Its core workflow depends on RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS, so it is not useful in a client that cannot access MCP tools.
How is this better than an ordinary Claude prompt?
An ordinary prompt may produce general instructions or assume outdated API details. entelligence-automation tells the agent to discover the current Composio Entelligence toolkit schema before acting. That matters when tool slugs, required parameters, or recommended execution plans may change.
Is this beginner-friendly?
It is beginner-friendly if you are comfortable connecting an MCP server and following an auth link. It is not ideal for users who expect a visual, no-setup automation builder. The skill is concise and operational, but it assumes you can work with Claude tool calls and connection states.
When should I not use this skill?
Do not use it if your goal is to learn the Entelligence product conceptually, build a custom integration from scratch, or run workflows without Composio/Rube. Also avoid it for high-risk production changes unless you require dry-run summaries, human approval, and explicit confirmation before execution.
How to Improve entelligence-automation skill
Improve entelligence-automation prompts with specific intent
The biggest improvement comes from replacing vague automation requests with task-specific discovery queries. Instead of saying “handle Entelligence,” describe the exact operation, target objects, filters, expected state, and whether changes are allowed.
Good inputs help RUBE_SEARCH_TOOLS return better matches, and they help the agent choose the correct schema instead of overgeneralizing from the toolkit name.
Common failure modes to prevent
The most common failure is skipping tool discovery and guessing parameters. Prevent this by requiring Claude to show the discovered tool slug and schema before execution.
Other avoidable problems include inactive connections, missing required fields, unclear approval boundaries, and asking for broad multi-step automation before validating one small operation. Start narrow, confirm the tool behavior, then expand.
Iterate after the first output
After the first entelligence-automation output, ask for a structured review:
- Which Rube tools were found?
- Which tool was selected and why?
- What inputs are still missing?
- What action would change data?
- What can be safely previewed before execution?
This turns the first response into an audit checkpoint instead of a blind run.
Add local guardrails for production use
Because the repository provides only SKILL.md, teams should add their own operating rules around approvals, logging, naming conventions, and rollback expectations. For production Workflow Automation, require a plan-first mode, explicit confirmation for state-changing calls, and saved summaries of tool inputs and results.