agenty-automation
by ComposioHQagenty-automation helps run Agenty tasks through Composio Rube MCP by verifying the connection, searching current tool schemas first, and executing workflow automation with less guesswork.
Score: 68/100. This is acceptable for listing because it gives agents a credible activation path for Agenty operations through Rube MCP and enough setup guidance to start safely. For directory users, the score means it is useful if they already use or are willing to configure Rube/Composio, but it is not a deeply packaged skill with concrete Agenty automations or supporting examples.
- Frontmatter clearly names the trigger domain and requirement: Agenty automation through the Rube MCP with the `rube` MCP dependency.
- Prerequisites and setup steps are explicit, including checking `RUBE_SEARCH_TOOLS`, using `RUBE_MANAGE_CONNECTIONS`, and confirming an ACTIVE Agenty connection before workflows.
- The skill emphasizes schema discovery before execution, which should reduce tool-call mismatch when Composio tool schemas change.
- No install command or supporting files are provided beyond SKILL.md, so users must already know how to add the Rube MCP endpoint in their client.
- Workflow guidance is mostly generic discovery/connection/execution patterning rather than concrete Agenty task recipes, increasing reliance on RUBE_SEARCH_TOOLS at runtime.
Overview of agenty-automation skill
What agenty-automation does
agenty-automation is a Claude skill for running Agenty-related actions through Composio’s Rube MCP server. It is designed for users who want an AI assistant to discover the current Agenty tool schema, verify the Agenty connection, and then execute workflow automation tasks without guessing tool names or stale parameters.
The key value is not a long prompt template. The skill enforces the right operating pattern: connect Rube MCP, authenticate the agenty toolkit, call RUBE_SEARCH_TOOLS first, then use the returned schemas to run the task.
Best-fit users and jobs
This agenty-automation skill fits teams already using Agenty or evaluating Agenty for workflow automation, data operations, scraping-adjacent tasks, or platform task orchestration through an AI client that supports MCP. It is most useful when your request depends on live tool availability, account-level permissions, or schemas that may change over time.
Use it when you need the assistant to translate a business goal such as “run this Agenty operation and report the result” into a tool-discovery-first workflow rather than a one-shot generic answer.
Main differentiator
The differentiator is schema freshness. The upstream skill explicitly tells the assistant to use RUBE_SEARCH_TOOLS before execution, because Composio tool names, required fields, and recommended plans can vary. That reduces a common automation failure: calling an integration tool with outdated assumptions.
Important limitations
This is a thin orchestration skill with only SKILL.md in the repository. There are no bundled scripts, reference examples, rules, or test fixtures. Adoption depends on your MCP client, Rube availability, an active Agenty connection, and the schemas returned at runtime. If you need a complete Agenty tutorial, local SDK wrapper, or prebuilt business workflow, this repo will not provide that by itself.
How to Use agenty-automation skill
agenty-automation install context
Install the skill from the Composio skill collection, then configure Rube MCP in your AI client. A typical directory install command is:
npx skills add ComposioHQ/awesome-claude-skills --skill agenty-automation
The skill itself requires MCP access to Rube:
https://rube.app/mcp
After adding the MCP server, verify that RUBE_SEARCH_TOOLS is available. Then use RUBE_MANAGE_CONNECTIONS with toolkit agenty to check whether the Agenty connection is ACTIVE. If it is not active, follow the returned authorization link before asking the assistant to run any Agenty workflow.
Inputs the skill needs
For strong agenty-automation usage, give the assistant a concrete Agenty goal, the target object or workflow, expected output, and safety boundaries. Avoid prompts like “automate Agenty.” Instead, provide the operational intent.
Weak input:
Use Agenty to get my data.
Stronger input:
Use agenty-automation to discover available Agenty tools, confirm my
agentyconnection is active, then find the best tool for exporting the latest results from my specified Agenty workflow. Return the tool chosen, required fields, execution result, and any follow-up action needed. Do not create, delete, or modify workflows unless you ask first.
This improves results because the assistant knows to discover tools, preserve state, report decisions, and avoid destructive changes.
Recommended workflow
Start every session with discovery, even if the task seems familiar:
- Ask the assistant to invoke the agenty-automation skill.
- Confirm
RUBE_SEARCH_TOOLSresponds. - Search for tools using your exact use case, not a generic phrase.
- Check
agentyconnection status withRUBE_MANAGE_CONNECTIONS. - Review the returned schema, required fields, and pitfalls.
- Execute only after the assistant summarizes the intended tool call.
- Ask for a short result log: tool used, inputs supplied, output received, and next step.
This workflow is slower than a direct call, but it is safer for integrations where schemas and account permissions matter.
Repository files to read first
The repository path is composio-skills/agenty-automation, and the main file is SKILL.md. Read it before install to confirm the prerequisites and tool-discovery pattern. There are no extra README.md, rules/, resources/, references/, or scripts/ folders in the provided tree, so all operational guidance is concentrated in the skill file.
The most important source sections are Prerequisites, Setup, Tool Discovery, and Core Workflow Pattern.
agenty-automation skill FAQ
Is agenty-automation for Workflow Automation or Agenty administration?
It can support both, depending on what Composio exposes through the Agenty toolkit at runtime. The skill does not hard-code a fixed list of Agenty actions. It asks Rube to discover the available tools for your use case, then works from the returned schema. That makes it suitable for Agenty workflow automation tasks where current tool discovery matters.
How is this better than an ordinary prompt?
An ordinary prompt may invent tool names, assume old parameters, or skip authentication checks. The agenty-automation skill gives the assistant a required sequence: search tools first, manage the Agenty connection, and use current schemas. That sequence is the main reason to install it.
Is it beginner-friendly?
It is beginner-friendly if you already know what you want Agenty to do and your AI client supports MCP. It is not a beginner course on Agenty. New users should expect to handle connection authorization and clarify task intent before the assistant can execute useful operations.
When should I not use this skill?
Do not use it when you need offline automation, a local-only script, a guaranteed fixed API contract, or a fully documented end-to-end Agenty playbook. Also avoid it for high-risk destructive actions unless you add explicit confirmation requirements, because the skill’s safety depends on your prompt boundaries and the tool schemas returned by Rube.
How to Improve agenty-automation skill
Improve agenty-automation prompts
Better prompts produce better tool discovery. Include:
- The Agenty task goal
- Relevant workflow, agent, dataset, project, or account context
- Whether the assistant may create, update, delete, run, or only inspect
- Desired output format
- Error-handling preference
Example:
Use agenty-automation to search current Rube tools for checking the status of an Agenty workflow run. If the connection is inactive, stop and show the auth step. If active, inspect only; do not change configuration. Return a concise table with tool name, required inputs, result, and any missing information.
Avoid common failure modes
The biggest failure mode is skipping RUBE_SEARCH_TOOLS and relying on assumed schemas. The second is running a workflow before confirming the agenty connection is active. The third is giving the assistant an outcome but not the constraints, which can lead to overbroad actions.
To reduce risk, require a “plan before execute” step for any action that changes Agenty state. For read-only tasks, say so explicitly.
Iterate after the first output
After the first run, ask the assistant to refine based on actual tool responses:
- “What required fields are still missing?”
- “Which returned tool is safest for read-only inspection?”
- “Summarize the schema fields I need to provide next time.”
- “Convert this successful run into a reusable prompt checklist.”
This turns a one-off agenty-automation usage into a repeatable workflow for your team.
What would make the skill stronger
The upstream skill would be more adoptable with example prompts for common Agenty tasks, a connection troubleshooting section, and sample before/after tool discovery flows. A small reference file showing safe read-only patterns versus write actions would also help users understand boundaries before they install agenty-automation in production workflows.