retellai-automation
by ComposioHQretellai-automation is a Claude skill for Retell AI operations through Composio Rube MCP, with schema-first tool discovery and connection checks before execution.
This skill scores 66/100, which means it is acceptable for directory listing but should be presented as a lightweight connector-oriented skill rather than a full Retellai automation playbook. Directory users can understand when to trigger it and how to start through Rube MCP, but they should expect to rely on live tool discovery for the actual Retellai operation schemas and execution details.
- Frontmatter is valid and clearly declares the required MCP dependency: `mcp: [rube]`.
- The skill gives actionable prerequisites and setup steps, including adding `https://rube.app/mcp`, checking `RUBE_SEARCH_TOOLS`, and using `RUBE_MANAGE_CONNECTIONS` for the `retellai` toolkit.
- It repeatedly instructs agents to call `RUBE_SEARCH_TOOLS` first, which improves schema freshness and reduces guessing when invoking Retellai tools.
- No support files, scripts, examples, or README are present beyond SKILL.md, so users get little validation or runnable scaffolding.
- Workflow guidance is mostly generic Rube MCP discovery/connection flow; the evidence does not show concrete Retellai-specific task examples or edge-case handling.
Overview of retellai-automation skill
What retellai-automation does
retellai-automation is a Claude skill for running Retell AI operations through Composio’s Rube MCP server. Its main job is not to hard-code one Retell workflow; it teaches the agent to discover the current Retellai tool schemas first, verify the user’s Retellai connection, then execute the right Rube MCP tools with fewer schema mistakes.
Best-fit users and workflows
This retellai-automation skill is a good fit if you already use Retell AI and want an AI agent to help with operational tasks such as finding available Retellai actions, preparing tool calls, checking connection state, or chaining Retell-related steps inside a broader Workflow Automation process. It is most useful for users who want Claude to operate through MCP tools rather than only write instructions for a human.
Key differentiator: schema-first automation
The strongest design choice is the required RUBE_SEARCH_TOOLS step. Retellai and Composio tool schemas can change, so the skill tells the agent to search available tools before calling them. That makes it safer than a static prompt that assumes field names, required parameters, or tool slugs from memory.
What to know before installing
The repository path contains only SKILL.md, so this is a compact operational skill rather than a large framework with helper scripts or examples. Adoption depends on your MCP client supporting Rube MCP, RUBE_SEARCH_TOOLS being available, and your Retellai connection being active through RUBE_MANAGE_CONNECTIONS.
How to Use retellai-automation skill
retellai-automation install and setup context
Install the skill from the GitHub skill collection with:
npx skills add ComposioHQ/awesome-claude-skills --skill retellai-automation
Then configure Rube MCP in your client by adding the MCP server endpoint:
https://rube.app/mcp
After installation, confirm that the MCP tool RUBE_SEARCH_TOOLS responds. Next, use RUBE_MANAGE_CONNECTIONS with toolkit retellai and complete the returned authentication flow if the connection is not ACTIVE. Do not start a Retellai workflow until the connection status is confirmed active.
Inputs the skill needs from you
For reliable retellai-automation usage, give the agent the actual business task, not only “use Retellai.” Include the Retell object or operation you care about, the desired end state, known identifiers, constraints, and whether the action should only be planned or actually executed.
Weak request:
- “Automate Retellai.”
Stronger request:
- “Use retellai-automation to discover current Rube tools for Retellai. I need to update an existing voice agent’s configuration, but first show me the tool schema, required fields, and any destructive actions before execution. The Retellai connection should be checked before calling tools.”
The stronger prompt works because it forces tool discovery, connection verification, schema inspection, and a safety checkpoint.
Practical workflow for first run
Start by reading composio-skills/retellai-automation/SKILL.md; there are no extra README.md, rules/, resources/, or scripts in this skill folder. In Claude or another compatible client, ask the agent to follow this sequence:
- Call
RUBE_SEARCH_TOOLSfor the specific Retellai task. - Reuse the returned session ID for related searches or execution planning.
- Call
RUBE_MANAGE_CONNECTIONSfor toolkitretellai. - Confirm the connection is
ACTIVE. - Present the discovered tool slug, schema, required fields, and planned action.
- Execute only after you approve any write, update, or delete operation.
This pattern is especially important for retellai-automation for Workflow Automation because downstream steps can fail if an early MCP call uses stale field names.
Tips that improve output quality
Ask the agent to quote the discovered schema before forming arguments. If you have IDs, names, phone numbers, agent names, call records, or environment boundaries, provide them up front. If you do not know the right Retellai operation, describe the outcome and ask the agent to search tools using that use case. For sensitive workflows, require a dry-run plan before execution and ask the agent to identify which tool calls are read-only versus state-changing.
retellai-automation skill FAQ
Is retellai-automation only for developers?
No, but it assumes you are using an AI client that can run MCP tools. Non-developers can use it if Rube MCP is configured and the Retellai connection is authorized. Developers will get more value when embedding Retellai actions into repeatable operational workflows.
How is this better than an ordinary Retellai prompt?
A normal prompt can describe what to do, but it may invent tool names or rely on outdated schemas. The retellai-automation skill explicitly instructs the agent to call RUBE_SEARCH_TOOLS first, then use the current schemas returned by Composio’s Rube MCP layer. That reduces guesswork when tool definitions change.
When should I not use this skill?
Do not use it if you only need general Retell AI strategy, copywriting for voice agents, or documentation summaries without executing MCP tools. Also avoid it when you cannot connect Rube MCP, cannot authorize the Retellai toolkit, or need a fully packaged automation script; this skill is an agent workflow guide, not a standalone CLI.
What files should I inspect before trusting it?
Inspect SKILL.md in the skill folder. It contains the prerequisites, setup instructions, tool discovery pattern, and core workflow. Because there are no supporting scripts or reference files, your install decision should focus on whether that MCP-driven pattern matches your environment.
How to Improve retellai-automation skill
Make retellai-automation prompts more specific
The most common failure mode is giving the agent a vague Retellai goal. Improve results by stating the exact operation, target entity, acceptable changes, and approval rules. For example: “Find the current tools for listing Retellai agents, retrieve the matching agent by name, and stop before making changes” is much safer than “manage my agents.”
Add checkpoints for write operations
For create, update, delete, or call-triggering workflows, ask the agent to separate discovery, planning, and execution. Require it to show the tool slug, arguments, missing fields, and expected effect before calling the final tool. This protects against accidental changes when multiple Retellai tools appear relevant.
Iterate from tool results, not assumptions
After the first RUBE_SEARCH_TOOLS result, refine the request using the returned schema. If the tool requires fields you did not provide, supply exact values rather than asking the model to infer them. If a call fails, ask the agent to compare the failed arguments against the latest schema and connection status before retrying.
Strengthen the skill for team use
Teams can improve retellai-automation by documenting their preferred Retellai workflows, naming conventions, approval policy, and safe defaults in their own project instructions. Pair this skill with internal runbooks that define which Retellai actions are allowed automatically and which require human approval.