streamtime-automation

Overview of streamtime-automation skill

What streamtime-automation does

streamtime-automation is a Claude skill for running Streamtime workflow automation through Composio’s Rube MCP server. It helps an AI agent discover the current Streamtime tool schemas, verify the user’s Streamtime connection, and execute Streamtime operations without relying on stale hardcoded API assumptions.

The skill is best for users who already use Streamtime for jobs, scheduling, time, tasks, clients, or project operations and want an assistant to perform repeatable actions through MCP rather than only draft instructions.

Best-fit users and workflows

Use the streamtime-automation skill when you want Claude to act as an operations assistant for Streamtime, especially for workflow automation that depends on live tool availability. Good fits include checking available Streamtime actions, preparing structured requests, updating records, retrieving project or job data, and chaining multiple Streamtime steps after confirming the active connection.

It is most useful for teams that already have Streamtime connected through Rube MCP and need reliable execution patterns, not just general productivity advice.

Key differentiator: search tools first

The important design choice is that the skill tells the agent to call RUBE_SEARCH_TOOLS before doing anything else. That matters because Composio tool schemas can change, and Streamtime actions may require specific field names or input formats. The skill’s value is not a large local codebase; it is a disciplined execution pattern: discover tools, confirm connection, then run the right operation with the latest schema.

Adoption considerations

This is a lightweight skill with a single SKILL.md and no bundled scripts, examples, or reference files. That keeps installation simple, but it also means successful use depends on a working MCP setup and clear user intent. If you do not have Rube MCP available or cannot authorize Streamtime through RUBE_MANAGE_CONNECTIONS, the skill will not be able to perform live automation.

How to Use streamtime-automation skill

streamtime-automation install context

Install the skill from the Composio skills repository, then use it in an MCP-capable Claude environment:

npx skills add ComposioHQ/awesome-claude-skills --skill streamtime-automation

The upstream SKILL.md does not define a custom install script. The required runtime dependency is Rube MCP. Add https://rube.app/mcp as an MCP server in your client configuration, then confirm the MCP tools are visible. The key tools the agent expects are RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS.

Required setup before usage

Before asking for a Streamtime action, make sure the assistant can complete this setup flow:

1. Confirm RUBE_SEARCH_TOOLS responds.
2. Use RUBE_MANAGE_CONNECTIONS with toolkit streamtime.
3. If the connection is not ACTIVE, follow the returned authentication link.
4. Re-check the connection status before executing any workflow.

This matters because a good prompt cannot compensate for an inactive Streamtime connection. If authentication is incomplete, ask the agent to stop after the connection check instead of inventing results.

Prompting the skill well

A weak request is: “Update Streamtime.”

A stronger request is:

“Use the streamtime-automation skill. First call RUBE_SEARCH_TOOLS for current Streamtime schemas. Then check the streamtime connection. If active, find the relevant tool for updating a job status. I need job <job name or ID> changed to <status>. Show me the tool name, required fields, and ask before executing if any required identifier is missing.”

This works better because it gives the agent the target object, intended action, safety boundary, and discovery requirement. For read-only tasks, say so explicitly. For write actions, specify whether the agent may execute immediately or must present a dry-run plan first.

Repository files to read first

Start with composio-skills/streamtime-automation/SKILL.md. There are no extra README.md, rules/, resources/, references/, or scripts/ folders in the skill directory, so the SKILL.md is the source of truth.

When reviewing it, focus on the prerequisite list, the setup sequence, and the “Tool Discovery” pattern. The most important operational instruction is to search tools for the specific Streamtime task, not just a generic “Streamtime operations” query.

streamtime-automation skill FAQ

Is streamtime-automation only for developers?

No. The Streamtime user does not need to write code, but someone must configure MCP access in the client. After Rube MCP and the Streamtime connection are active, non-developers can use the streamtime-automation skill by describing the Streamtime task in business terms and letting the agent discover the correct tool schema.

How is this better than an ordinary prompt?

A normal prompt may guess the Streamtime API shape or produce manual instructions. This skill instructs the agent to use Rube MCP, search for current Streamtime tools, check authentication, and follow the returned schemas. That makes it better for live workflow automation where field names, tool slugs, and required inputs matter.

When should I not use this skill?

Do not use it if you only need a generic Streamtime how-to, a policy document, or a workflow diagram with no live execution. Also avoid it when your environment cannot expose Rube MCP tools, when Streamtime authorization is unavailable, or when you need fully offline automation. The skill depends on the external Rube MCP tool layer.

What should I verify before installing?

Confirm your AI client supports MCP servers, that https://rube.app/mcp can be added, and that your organization permits connecting Streamtime through Composio/Rube. Also check whether your intended workflow involves writes to production data. For destructive or billing-related changes, require the agent to show the discovered tool and proposed payload before execution.

How to Improve streamtime-automation skill

Improve streamtime-automation prompts with identifiers

The fastest way to improve results is to provide stable identifiers: job ID, client name, task name, date range, team member, project status, or exact Streamtime object name. If you only provide a vague description, the agent may need extra lookup steps or may find multiple matching records.

Use: “Find active jobs for client Acme Studio created after 2025-01-01, then summarize matching IDs before any update.”

Avoid: “Clean up Acme jobs.”

Reduce failures with explicit execution rules

For write workflows, include a confirmation rule. Example:

“Discover the current Streamtime tool schema, prepare the payload, and stop for approval before executing any update.”

For read workflows, give permission to run immediately:

“This is read-only. You may execute the discovered search/list tools after confirming the Streamtime connection is active.”

These rules reduce accidental changes and help the agent choose an appropriate plan.

Iterate after the first output

After the first run, ask for the discovered tool slug, required fields, optional fields, and any pitfalls returned by RUBE_SEARCH_TOOLS. Save that information in your team’s internal workflow notes, but still require fresh discovery in future sessions because schemas may change.

If the result is incomplete, do not simply say “try again.” Add the missing constraint: date range, Streamtime object type, status, owner, or whether archived records should be included.

Add local guidance if your team repeats tasks

Because the upstream skill is intentionally minimal, teams can improve practical usage by adding their own wrapper prompts or internal runbooks. Useful additions include approved Streamtime actions, fields that must never be changed without approval, standard date formats, naming conventions, and examples of safe dry-run requests. This keeps streamtime-automation focused while making it more reliable for your specific Workflow Automation needs.