repairshopr-automation
by ComposioHQrepairshopr-automation helps agents automate RepairShopr operations through Composio Rube MCP. Use it to verify the RepairShopr connection, discover current tool schemas with RUBE_SEARCH_TOOLS, and run read-first workflows with safer approval steps.
This skill scores 64/100, which means it is acceptable to list but should be presented as a lightweight connector-oriented skill rather than a full Repairshopr automation playbook. Directory users get enough information to understand the required MCP setup and core tool-discovery pattern, but should expect limited Repairshopr-specific operational guidance beyond dynamic tool search.
- Valid skill metadata clearly names the Repairshopr automation use case and declares the required Rube MCP dependency.
- SKILL.md gives concrete prerequisites and setup steps, including connecting Rube MCP, using RUBE_MANAGE_CONNECTIONS with toolkit `repairshopr`, and confirming ACTIVE status.
- The skill repeatedly instructs agents to call RUBE_SEARCH_TOOLS first, which should reduce schema guesswork when invoking current Composio Repairshopr tools.
- No support files, scripts, references, or README are included beyond SKILL.md, so adoption depends entirely on the short inline instructions.
- The workflow is mostly a generic Rube MCP discovery-and-execution pattern; it does not provide many Repairshopr-specific task examples or edge-case handling.
Overview of repairshopr-automation skill
What repairshopr-automation does
repairshopr-automation is a Claude skill for automating RepairShopr operations through Composio’s Rube MCP. Its main value is not a fixed script; it teaches the agent to discover the current RepairShopr tool schemas first, verify the account connection, and then run actions through the available Rube tools instead of guessing API fields.
Use this skill when you want an AI agent to help with RepairShopr workflow automation such as looking up records, preparing operational updates, or executing supported actions after tool discovery confirms the exact available capability.
Best-fit users and workflows
The repairshopr-automation skill is best for repair shops, MSPs, operations teams, or internal automation builders already using RepairShopr and willing to connect it through Rube MCP. It fits workflows where accuracy matters more than speed: customer records, tickets, job tracking, invoices, assets, or similar RepairShopr objects should be handled only after the agent confirms the live tool schema.
It is especially useful if your team wants natural-language automation without writing a custom RepairShopr API integration.
Key adoption requirement
The critical requirement is an active Rube MCP connection with the RepairShopr toolkit enabled. The skill explicitly depends on RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS; without those tools, it cannot safely operate.
A practical blocker is assuming the skill contains all RepairShopr schemas locally. It does not. The repository includes a single SKILL.md, and the workflow depends on live tool discovery from Rube.
How to Use repairshopr-automation skill
repairshopr-automation install context
Install the skill from the source repository path:
npx skills add ComposioHQ/awesome-claude-skills --skill repairshopr-automation
Then configure Rube MCP in your AI client by adding the MCP server endpoint:
https://rube.app/mcp
Before asking for any RepairShopr action, confirm that RUBE_SEARCH_TOOLS is available. Then use RUBE_MANAGE_CONNECTIONS with toolkit repairshopr and complete the returned authentication flow if the connection is not ACTIVE.
Inputs the skill needs from you
For reliable repairshopr-automation usage, provide the business goal, the RepairShopr object type, identifiers you already know, and the safety boundary. A weak prompt is: “Update a customer.” A stronger prompt is:
“Use repairshopr-automation for Workflow Automation. First search Rube tools for current RepairShopr schemas. Check that the RepairShopr connection is active. Then find customer Acme Device Repair and summarize what fields can be safely updated before changing anything. Do not write changes until I confirm.”
This works better because it tells the agent to discover tools, verify auth, identify the target record, and pause before mutation.
Recommended workflow
A safe repairshopr-automation guide follows four steps:
- Discover tools with
RUBE_SEARCH_TOOLSfor the exact use case. - Check the RepairShopr connection with
RUBE_MANAGE_CONNECTIONS. - Map the user request to the returned tool slug and schema.
- Execute read-only checks first, then perform writes only when the target and fields are unambiguous.
For write actions, ask the agent to show the planned tool call, required fields, and expected effect before execution. This reduces accidental updates caused by similar customer names, incomplete ticket context, or stale assumptions about RepairShopr fields.
Repository files to read first
Start with composio-skills/repairshopr-automation/SKILL.md. There are no bundled scripts, references, resources, or README files in this skill folder, so the skill’s behavior is defined almost entirely by that file plus the live Rube MCP tools.
Pay special attention to the “Prerequisites,” “Setup,” “Tool Discovery,” and “Core Workflow Pattern” sections. Those sections explain why the agent must search for current schemas instead of relying on remembered RepairShopr API shapes.
repairshopr-automation skill FAQ
Is repairshopr-automation useful without Rube MCP?
No. The skill requires Rube MCP and an active RepairShopr connection. If your environment cannot expose RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS, a normal prompt may help draft instructions, but it cannot execute the supported RepairShopr workflow safely.
How is this different from a generic RepairShopr prompt?
A generic prompt may hallucinate endpoint names, field names, or action formats. repairshopr-automation instructs the agent to discover the live Composio RepairShopr toolkit schema first, then use the returned tool slugs and input requirements. That makes it better for execution-oriented automation, not just planning.
Is the skill beginner-friendly?
It is beginner-friendly if your AI client already supports MCP tools. Non-technical users can describe the desired RepairShopr outcome in plain English, but someone still needs to connect Rube MCP and authorize the RepairShopr toolkit. The main concept to understand is that the first step should always be tool discovery, not immediate execution.
When should I not use this skill?
Do not use it for offline data cleanup, unsupported RepairShopr endpoints, bulk destructive edits without review, or environments where RepairShopr credentials cannot be connected to Rube. Also avoid using it when you need a fully audited custom integration with version-controlled business logic; this skill is better for assisted operational automation than for replacing a governed backend service.
How to Improve repairshopr-automation skill
Improve prompts for repairshopr-automation
Better prompts include the target object, known identifiers, desired outcome, allowed actions, and confirmation rules. For example:
“Search available RepairShopr tools for ticket lookup and update. Verify connection status. Find ticket 12345, summarize current status and assigned technician, then propose the exact update needed to mark it ready for pickup. Do not submit the update until I approve.”
This gives the agent enough structure to avoid broad searches and premature writes.
Prevent common failure modes
The most common failures are skipping tool discovery, acting on the wrong record, using outdated schema assumptions, and performing write actions without confirmation. Prevent them by requiring the agent to state: the discovered tool name, required input fields, selected record, and whether the next step is read-only or mutating.
For ambiguous names, ask for disambiguation instead of allowing the agent to choose. For bulk operations, require a preview list before any update.
Iterate after the first output
After the first run, refine the workflow with operational details: naming conventions, ticket status meanings, technician assignment rules, invoice approval steps, or customer communication policies. These details are not included in the upstream skill, but they materially improve automation quality.
A good iteration prompt is: “Use the same discovered RepairShopr tools, but apply our rule that warranty tickets must not be closed until parts usage is checked.”
Extend the skill responsibly
If you maintain a local version of repairshopr-automation, consider adding examples for your most common RepairShopr tasks, approval checkpoints for write actions, and shop-specific field conventions. Keep the central rule intact: always call RUBE_SEARCH_TOOLS first so the skill stays aligned with current Composio schemas rather than hard-coded assumptions.