leadoku-automation
by ComposioHQleadoku-automation helps Claude automate Leadoku through Composio Rube MCP by checking the Leadoku connection and discovering current tool schemas before execution.
This skill scores 66/100, which means it is acceptable to list but should be presented as a lightweight connector pattern rather than a complete Leadoku automation playbook. Directory users get enough information to know it is for Leadoku via Composio/Rube MCP and how an agent should begin, but the repository evidence shows limited task-specific workflow depth and no supporting install assets.
- Valid skill frontmatter with a clear trigger: automate Leadoku tasks through Rube MCP, with `requires: mcp: [rube]`.
- Includes practical prerequisites and setup checks, including verifying `RUBE_SEARCH_TOOLS`, using `RUBE_MANAGE_CONNECTIONS`, and confirming an ACTIVE Leadoku connection.
- Emphasizes schema discovery before execution, which should reduce brittle tool calls by having the agent fetch current Leadoku tool schemas first.
- No support files, scripts, README, or install command are present; adoption depends on users already knowing how to configure MCP in their client.
- Workflow content is mostly a generic Rube discovery/connection pattern rather than concrete Leadoku task recipes, so agents may still need to infer task-specific steps after tool discovery.
Overview of leadoku-automation skill
What leadoku-automation does
leadoku-automation is a Claude skill for automating Leadoku operations through Composio’s Rube MCP server. Its main value is not a fixed set of hard-coded Leadoku actions; it teaches the agent to discover the current Leadoku tool schemas first, verify the user’s Leadoku connection, and then execute the right workflow through Rube.
This matters because MCP tool schemas can change. The leadoku-automation skill is designed around a safer pattern: search tools, confirm connection status, inspect the returned schema, then run the task.
Best-fit users and workflow fit
This skill is a good fit if you already use Leadoku and want an AI agent to help with repetitive or structured Leadoku tasks inside a workflow automation environment. It is especially relevant for users working with Claude-compatible skill systems, Composio toolkits, and Rube MCP.
It is less useful if you want a standalone Leadoku client, a no-code UI, or documentation for every Leadoku endpoint. The skill depends on live tool discovery through RUBE_SEARCH_TOOLS, so adoption depends on having Rube MCP available in your AI client.
Key differentiator: discover before execution
The most important instruction in the repository is: always call RUBE_SEARCH_TOOLS before attempting Leadoku actions. That makes leadoku-automation different from a generic “automate this app” prompt. It pushes the agent to retrieve current tool names, input schemas, execution plans, and known pitfalls before making changes.
For install-decision purposes, this is the core reason to use the leadoku-automation skill: it reduces guessing when operating against Leadoku through Composio’s toolkit.
How to Use leadoku-automation skill
leadoku-automation install and MCP setup
If your skill manager supports installing from GitHub, add the skill from the ComposioHQ skill repository, for example:
npx skills add ComposioHQ/awesome-claude-skills --skill leadoku-automation
Then configure Rube MCP in your AI client by adding:
https://rube.app/mcp
The upstream skill states that no API keys are needed for the MCP endpoint itself, but you still need an active Leadoku connection. Confirm that RUBE_SEARCH_TOOLS is available before expecting the skill to work.
Required connection checks before using Leadoku
Before running a real workflow, the agent should check the Leadoku connection with RUBE_MANAGE_CONNECTIONS using toolkit leadoku. If the connection is not ACTIVE, follow the returned authentication link and complete authorization.
A practical first prompt is:
Use the leadoku-automation skill. First verify that Rube MCP is available, then check whether my
leadokutoolkit connection is active. Do not run any Leadoku operation until you have searched the available tools and confirmed the required schema.
This prompt is stronger than “automate Leadoku” because it gives the agent a safe sequence and makes connection state a required gate.
Turning a rough task into a usable prompt
For best leadoku-automation usage, describe the Leadoku outcome, the relevant object or record type, the constraints, and whether the action should be read-only or mutating.
Weak prompt:
Update my Leadoku data.
Better prompt:
Use leadoku-automation for Workflow Automation. Search Rube tools for the exact Leadoku operation needed to update lead records. Check the active Leadoku connection first. Before making changes, show me the discovered tool slug, required fields, optional fields, and a proposed execution plan. Only proceed after I approve.
This gives the agent enough context to select tools through RUBE_SEARCH_TOOLS without inventing unsupported parameters.
Repository files to read first
The repository path is:
composio-skills/leadoku-automation/SKILL.md
Read SKILL.md first because this skill is compact and the operational rules are concentrated there. There are no supporting scripts/, references/, resources/, or rules/ folders in the provided tree, so do not expect hidden helper code. The skill’s practical behavior comes from the MCP instructions and Composio Leadoku toolkit discovery, not from local scripts.
leadoku-automation skill FAQ
Is leadoku-automation enough without Rube MCP?
No. The skill requires Rube MCP, specifically access to RUBE_SEARCH_TOOLS. Without that tool, the agent cannot discover current Leadoku schemas and the main safety mechanism of the skill is lost.
You also need an active Leadoku connection through RUBE_MANAGE_CONNECTIONS with toolkit leadoku.
How is this better than an ordinary prompt?
A generic prompt may guess tool names, fields, or API behavior. The leadoku-automation skill tells the agent to search for current Leadoku tools before execution and to use the returned schemas. That is the difference between “try to automate Leadoku” and “discover the supported Leadoku operation, validate connection state, then execute.”
Is this suitable for beginners?
Yes, if the beginner is comfortable configuring an MCP server and following an authentication link. It is not ideal for someone who wants a purely visual setup or who has never used MCP tools. The most important beginner rule is simple: do not skip tool discovery or connection verification.
When should I not use this skill?
Do not use leadoku-automation when you need offline processing, a fixed API wrapper, or guaranteed behavior without live tool lookup. Also avoid using it for destructive Leadoku operations unless your prompt requires the agent to summarize the discovered schema, preview the action, and wait for approval.
How to Improve leadoku-automation skill
Improve leadoku-automation results with stronger inputs
The skill works best when you provide a specific Leadoku task and operational boundaries. Include:
- the business goal
- the Leadoku object or workflow area
- whether the action is read-only or write-enabled
- required filters or identifiers
- approval requirements before mutation
- expected output format
For example:
Search Leadoku tools for exporting or listing lead records. Use only read-only operations. Filter to records matching my stated criteria if the schema supports it. Return the tool slug, input schema, and a table of results. If filtering is not supported, explain the closest supported workflow.
Common failure modes to prevent
The main failure mode is skipping RUBE_SEARCH_TOOLS and assuming a schema. Prevent this by explicitly requiring discovery first. Another failure mode is attempting a workflow before the Leadoku connection is active. Require RUBE_MANAGE_CONNECTIONS before execution.
A third issue is vague intent. “Manage my leads” is too broad. “Find the Leadoku tool for creating a lead, show required fields, and ask before creating anything” is actionable.
Iterate after the first tool search
The first RUBE_SEARCH_TOOLS response should shape the rest of the workflow. Ask the agent to revise the plan after tool discovery instead of forcing a plan upfront.
Useful follow-up prompt:
Based on the discovered Leadoku tools, identify the safest workflow. If multiple tools could apply, compare them by required fields, risk of data modification, and fit for my goal. Ask me for any missing required values before execution.
This keeps leadoku-automation grounded in live schema evidence.
Add local guardrails for production use
For production Workflow Automation, wrap the skill with your own approval rules. Require confirmation before create, update, delete, bulk action, or external notification steps. Ask for dry-run summaries when supported by the discovered tool behavior.
The upstream skill is intentionally lightweight, so serious teams should add conventions around logging, approval, naming, and rollback planning in their own prompts or workspace instructions.