campaign-cleaner-automation
by ComposioHQcampaign-cleaner-automation helps agents run Campaign Cleaner workflows through Rube MCP by verifying the connection, discovering current tool schemas, and executing safer cleanup tasks.
This skill scores 64/100, which means it is acceptable for listing but should be treated as a lightweight Rube MCP workflow wrapper rather than a fully self-contained automation. Directory users get enough information to understand that it helps agents discover and run Campaign Cleaner tools through Composio/Rube, but they should expect to rely on live tool discovery for exact schemas and task-specific execution details.
- Clear trigger and scope: it is specifically for automating Campaign Cleaner operations through Composio's Campaign Cleaner toolkit via Rube MCP.
- Operational prerequisites are explicit, including Rube MCP availability, an active Campaign Cleaner connection, and use of RUBE_MANAGE_CONNECTIONS.
- Good agent safety pattern: it repeatedly instructs agents to call RUBE_SEARCH_TOOLS first to retrieve current tool slugs, schemas, plans, and pitfalls.
- The repository evidence shows only a single SKILL.md with no scripts, references, examples, or support files, so practical implementation depth is limited.
- Workflow guidance appears generic and depends heavily on live Rube tool discovery rather than documenting concrete Campaign Cleaner tasks or edge cases.
Overview of campaign-cleaner-automation skill
What campaign-cleaner-automation does
campaign-cleaner-automation is a Claude skill for running Campaign Cleaner operations through Composio’s Rube MCP server. Its main value is not a fixed campaign-cleaning script; it teaches the agent to discover the current Campaign Cleaner tool schemas first, confirm the user’s connection, and then execute the correct Rube tools with less guesswork.
Best-fit users and jobs
This campaign-cleaner-automation skill is best for teams already using Campaign Cleaner or evaluating it inside a workflow automation stack. It fits jobs such as cleaning campaign data, standardizing campaign-related records, validating available Campaign Cleaner actions, or building repeatable assistant-led cleanup workflows where the exact tool schema may change over time.
What makes it different from a normal prompt
A generic prompt may ask an assistant to “clean this campaign,” but it will not know which Campaign Cleaner tools are currently exposed through Rube MCP. This skill’s differentiator is its discovery-first pattern: call RUBE_SEARCH_TOOLS, inspect the returned slugs and schemas, verify connection status with RUBE_MANAGE_CONNECTIONS, then run the operation. That makes it more reliable for live Workflow Automation than hardcoded instructions.
Important adoption constraints
The skill depends on Rube MCP and an active Campaign Cleaner connection. If your client cannot access MCP tools, or if Campaign Cleaner is not connected through Composio, this skill will not execute real operations. It also has a compact repository footprint: the primary source is SKILL.md, with no bundled scripts, examples folder, or separate README.
How to Use campaign-cleaner-automation skill
campaign-cleaner-automation install context
Install the skill from the ComposioHQ skill collection if your environment supports skill installation:
npx skills add ComposioHQ/awesome-claude-skills --skill campaign-cleaner-automation
Then configure Rube MCP in your AI client by adding https://rube.app/mcp as an MCP server. The upstream skill states that no API key is needed for the endpoint itself, but Campaign Cleaner still needs an active connection. Confirm that RUBE_SEARCH_TOOLS is available before expecting the skill to work.
Read these source details first
Start with composio-skills/campaign-cleaner-automation/SKILL.md. It contains the operational contract: Rube MCP is required, campaign_cleaner must be connected, and tool discovery must happen before execution. There are no support folders such as scripts/, resources/, or rules/, so do not assume hidden automation logic exists outside the skill file.
Key source points to check:
- prerequisites for
RUBE_SEARCH_TOOLS - Campaign Cleaner connection setup through
RUBE_MANAGE_CONNECTIONS - the “Tool Discovery” pattern
- the “Core Workflow Pattern” before running tasks
Inputs the skill needs to work well
For strong campaign-cleaner-automation usage, give the assistant the business goal, the Campaign Cleaner task type, the data scope, and the safety boundaries. A weak prompt is: “Clean my campaign data.” A stronger prompt is:
“Use campaign-cleaner-automation for Campaign Cleaner via Rube MCP. First discover tools for deduplicating and validating campaign records. Work only on the Q4 email campaign dataset. Before executing any mutation, show the selected tool slug, required fields, and a dry-run style plan if the tool supports it.”
This improves output because the agent can search for the right tool schema, avoid accidental broad changes, and ask for missing fields before execution.
Suggested workflow for reliable execution
Use a staged workflow. First, ask the agent to verify Rube MCP availability. Second, have it call RUBE_MANAGE_CONNECTIONS for toolkit campaign_cleaner and confirm the connection is ACTIVE. Third, run RUBE_SEARCH_TOOLS with a specific use case, such as “deduplicate Campaign Cleaner contacts” or “validate campaign records before export.” Fourth, review the returned schema and execution plan. Only then authorize the actual Campaign Cleaner operation.
campaign-cleaner-automation skill FAQ
Is campaign-cleaner-automation for beginners?
It is beginner-friendly only if your AI client already supports MCP tools. The skill’s instructions are straightforward, but the user must understand that the assistant is not simply generating text; it is discovering and calling external Campaign Cleaner tools through Rube MCP.
When should I not use this skill?
Do not use campaign-cleaner-automation if you only need static advice about campaign hygiene, spreadsheet formulas, or a one-off manual checklist. It is also a poor fit if your organization cannot connect Campaign Cleaner through Composio or if you cannot review tool calls before they change live campaign data.
How is this different from direct Composio usage?
Direct Composio usage requires you or your agent to know which toolkit actions to call. This skill provides an agent-facing pattern: connect, search tools, read current schemas, then execute. That is useful when Campaign Cleaner tool definitions evolve or when you want the assistant to adapt to the current toolkit instead of relying on stale action names.
Does it include ready-made cleanup scripts?
No. The repository evidence shows a single SKILL.md and no bundled helper scripts or reference assets. The skill is an execution guide for Rube MCP tool use, not a packaged campaign-cleaning library. Its value is in correct orchestration, not prewritten transformations.
How to Improve campaign-cleaner-automation skill
Provide sharper task framing
The most common failure mode is asking for a broad cleanup without naming the operation. Improve campaign-cleaner-automation results by specifying whether you want deduplication, validation, enrichment, normalization, export preparation, or audit-style reporting. Also include the dataset boundary, fields that matter, and whether changes are allowed immediately.
Require schema review before action
Because the skill explicitly says to search tools first, make that review visible. Ask the assistant to summarize the discovered tool slug, required parameters, optional parameters, risks, and missing inputs before calling an execution tool. This catches schema mismatches and prevents the agent from inventing fields that Campaign Cleaner does not accept.
Add safety gates for mutable workflows
For production Workflow Automation, add confirmation gates. Require the assistant to separate discovery, planning, and execution. If a tool can update or delete records, ask for a preview, row count, affected segment, or rollback note before authorizing the call. This is especially important because the skill does not ship with custom guardrail scripts.
Iterate after the first run
After the first output, evaluate whether the selected Campaign Cleaner operation matched the business goal. If not, ask the agent to run RUBE_SEARCH_TOOLS again with a narrower use case and the known fields returned from the previous step. Iteration is not wasted time here; it is how the skill adapts to current Rube schemas and produces cleaner, safer automation.