chatbotkit-automation
by ComposioHQchatbotkit-automation helps agents automate Chatbotkit workflows through Composio Rube MCP by checking the connection, searching current tool schemas first, and executing with verified fields.
Score: 67/100. This is acceptable for listing because it gives agents a usable MCP-based pattern for discovering and running Chatbotkit tools, with enough setup and connection guidance to reduce guesswork versus a generic prompt. Directory users should understand, however, that it is a thin automation wrapper with limited concrete workflow examples and no supplemental implementation files.
- Defines a clear trigger and scope: automating Chatbotkit operations through Composio's Chatbotkit toolkit via Rube MCP.
- Lists concrete prerequisites and setup checks, including Rube MCP availability, `RUBE_MANAGE_CONNECTIONS` with toolkit `chatbotkit`, and requiring an ACTIVE connection before workflows.
- Emphasizes `RUBE_SEARCH_TOOLS` first and provides example calls, which should help agents retrieve current tool schemas instead of relying on stale assumptions.
- No support files, scripts, references, or README are provided beyond SKILL.md, so adoption depends entirely on the short in-skill instructions and external Composio/Rube tooling.
- The workflow guidance is mostly discovery-oriented rather than offering concrete Chatbotkit task recipes, so agents may still need to infer exact operations after tool search.
Overview of chatbotkit-automation skill
What chatbotkit-automation does
chatbotkit-automation is a Claude skill for automating Chatbotkit operations through Composio’s Rube MCP toolkit. Its core value is not a fixed set of hard-coded Chatbotkit commands; it teaches the agent to discover the current Chatbotkit tool schemas first, verify the connection, and then execute the right Rube MCP tool for the requested workflow.
Use this skill when you want an AI agent to help manage Chatbotkit resources with less guesswork than a generic prompt, especially when tool names, required fields, or supported actions may change over time.
Best-fit users and workflows
The chatbotkit-automation skill is best for users who already use Chatbotkit and want Claude or another MCP-capable assistant to perform operational tasks through Composio. Typical use cases include checking available Chatbotkit actions, preparing automation plans, managing Chatbotkit-connected resources, or running repeatable workflow steps after authentication is active.
It is most useful for developers, automation builders, AI ops teams, and technical product teams that prefer tool-discovered execution over manually reading API docs for every small change.
Key differentiator: search tools first
The most important rule in this skill is: always call RUBE_SEARCH_TOOLS before running a Chatbotkit workflow. That makes chatbotkit-automation different from a static prompt template. Instead of assuming the current schema, the agent should retrieve available tool slugs, input fields, execution guidance, and pitfalls from Rube MCP at runtime.
This matters because automation failures often come from stale field names, missing auth, or using the wrong operation. The skill is designed to reduce those failures by making discovery part of the workflow.
How to Use chatbotkit-automation skill
chatbotkit-automation install and setup context
Install the skill from the Composio skills repository:
npx skills add ComposioHQ/awesome-claude-skills --skill chatbotkit-automation
The skill requires Rube MCP, not just the repository files. Add https://rube.app/mcp as an MCP server in your client configuration, then confirm that RUBE_SEARCH_TOOLS is available. Next, use RUBE_MANAGE_CONNECTIONS with toolkit chatbotkit and complete the returned authentication flow if the connection is not ACTIVE.
Do not treat installation as complete until the Chatbotkit connection is active. Most failed chatbotkit-automation usage starts with trying to execute a workflow before Rube can access the connected toolkit.
Inputs the skill needs before it can act
For strong chatbotkit-automation usage, give the agent three things: the specific Chatbotkit outcome you want, any known resource identifiers or names, and the safety boundary for changes. A vague request like “automate Chatbotkit” forces the agent to discover too broadly.
A stronger prompt is:
“Use chatbotkit-automation to find the current Rube MCP tools for Chatbotkit. I want to update an existing bot configuration, not create a new bot. First check the active connection, then search for the relevant tool schema, show me required fields, and wait for confirmation before executing changes.”
That prompt improves output because it tells the agent the intent, the operation type, and the approval rule.
Practical workflow for first run
Start by reading composio-skills/chatbotkit-automation/SKILL.md; this is the only support file in the skill directory and contains the operating pattern. Then run the workflow in this order:
- Confirm
RUBE_SEARCH_TOOLSresponds. - Check or create the Chatbotkit connection with
RUBE_MANAGE_CONNECTIONS. - Search for tools using your actual use case, not a generic phrase.
- Review returned schemas and recommended execution plans.
- Execute only after required fields and connection status are clear.
For example, search with “create a Chatbotkit conversation record” or “list Chatbotkit bots” rather than “Chatbotkit operations.” Specific search wording usually produces more relevant tool candidates and fewer ambiguous execution paths.
Tips that improve execution quality
Ask the agent to preserve the Rube session ID after discovery so follow-up tool searches and actions stay context-aware. If the returned schema includes required fields you do not know, ask the agent to stop and list missing values instead of inventing them.
For production workflows, request a two-step plan: first “discover and validate,” then “execute.” This is especially important for Chatbotkit changes that may affect live bots, conversations, datasets, or customer-facing automation.
chatbotkit-automation skill FAQ
Is chatbotkit-automation only for Chatbotkit users?
Yes. The chatbotkit-automation skill is specifically scoped to Chatbotkit operations through Composio’s Chatbotkit toolkit. If your workflow is about another app or a direct Chatbotkit API integration outside Rube MCP, this skill is not the right primary tool.
How is this better than an ordinary prompt?
A normal prompt may guess tool names or rely on outdated assumptions. chatbotkit-automation instructs the agent to discover current Rube MCP tools first, check the Chatbotkit connection, and use returned schemas before execution. That makes it better for real automation where authentication, required fields, and supported actions must be verified.
Is this beginner-friendly?
It is beginner-friendly for users who can configure an MCP server and follow an auth link, but it is not a no-code tutorial for Chatbotkit itself. You should understand what Chatbotkit resource you want to work with and be able to approve or reject proposed changes.
When should I not use this skill?
Do not use chatbotkit-automation when you need offline planning only, when Rube MCP is unavailable in your client, or when your Chatbotkit connection cannot be authenticated. Also avoid it for high-risk bulk changes unless you can review the discovered tool schema and confirm the exact records or resources being modified.
How to Improve chatbotkit-automation skill
Improve prompts with specific Chatbotkit intent
The best improvement is better task framing. Replace broad requests with concrete outcomes, constraints, and approval rules.
Weak prompt: “Use Chatbotkit tools.”
Better prompt: “Use chatbotkit-automation for Workflow Automation. Search current Rube MCP Chatbotkit tools for listing existing bots. Check connection status first, return the exact tool slug and required fields, and do not modify anything.”
This gives the agent a safe read-only path and makes the first result easier to verify.
Avoid common failure modes
Common failures include skipping RUBE_SEARCH_TOOLS, assuming the connection is active, using stale field names, or executing with incomplete identifiers. Add explicit guardrails such as “do not infer missing IDs,” “show the schema before execution,” and “ask before write operations.”
If a tool call fails, do not immediately retry with guessed parameters. Ask the agent to re-run discovery for the exact failed use case and compare the returned schema with the attempted input.
Iterate after the first output
After the first discovery response, refine the request using the actual tool names and required fields returned by Rube. For example: “Now use the discovered tool for listing bots, with no filters, and summarize IDs and names only.” This turns a broad automation request into a controlled sequence.
For recurring workflows, save the successful prompt pattern, including the connection check, discovery phrase, approval step, and expected output format. That makes future chatbotkit-automation usage faster without bypassing the runtime schema check that keeps the workflow reliable.