stormglass-io-automation
by ComposioHQstormglass-io-automation helps Claude run Stormglass IO workflows through Composio Rube MCP, with connection checks and schema discovery before execution.
This skill scores 66/100, which means it is acceptable to list but should be presented as a limited automation helper rather than a complete Stormglass workflow pack. Directory users get enough evidence to understand when to use it—Stormglass IO operations through Composio/Rube MCP—and how to begin, but the repository evidence shows thin Stormglass-specific operational detail and no supporting assets.
- Valid frontmatter clearly identifies the skill, Stormglass IO scope, and required Rube MCP dependency.
- Prerequisites and setup steps explain that RUBE_SEARCH_TOOLS and an ACTIVE stormglass_io connection via RUBE_MANAGE_CONNECTIONS are required before use.
- The skill gives an explicit tool-discovery pattern and instructs agents to fetch current schemas before executing, reducing schema guesswork.
- The skill is mostly a Rube MCP/tool-discovery wrapper and provides limited Stormglass-specific task guidance in the available evidence.
- No support files, scripts, references, README, or install command are included beyond SKILL.md, so adoption depends on already understanding Rube MCP setup.
Overview of stormglass-io-automation skill
What stormglass-io-automation is for
stormglass-io-automation is a Claude skill for running Stormglass IO workflows through Composio’s Rube MCP server. It is designed for users who want an agent to discover the current Stormglass IO tool schemas, verify authentication, and execute weather or marine-data operations without hard-coding stale API assumptions into prompts.
The skill is most useful when your workflow depends on the Stormglass IO toolkit exposed through Rube, not when you are calling the Stormglass REST API directly from application code.
Best-fit users and jobs
Use this skill if you need an AI assistant to help with workflow automation around Stormglass IO data access, such as checking available toolkit actions, preparing tool calls, validating connection status, or chaining Stormglass operations into a larger automation.
Good fits include:
- Operations teams building weather-aware automations
- Developers prototyping Stormglass IO workflows through MCP
- Analysts who need tool-assisted data retrieval rather than manual API exploration
- Claude users already using Composio/Rube for third-party app actions
Key differentiator: schema discovery first
The main value of the stormglass-io-automation skill is its insistence on tool discovery before execution. The upstream skill tells the agent to call RUBE_SEARCH_TOOLS first so it can retrieve current tool slugs, input schemas, execution guidance, and pitfalls. That matters because MCP tool schemas can change, and guessing field names is one of the fastest ways to get failed or low-quality automation runs.
How to Use stormglass-io-automation skill
Install and connect stormglass-io-automation
Install the skill in a compatible Claude skills setup, for example:
npx skills add ComposioHQ/awesome-claude-skills --skill stormglass-io-automation
Then configure Rube MCP by adding this server endpoint in your client configuration:
https://rube.app/mcp
Before expecting the skill to work, confirm that the MCP environment exposes RUBE_SEARCH_TOOLS. The skill also requires an active Stormglass IO connection through RUBE_MANAGE_CONNECTIONS using toolkit stormglass_io. If the connection is not active, complete the returned authentication flow before running Stormglass tasks.
Inputs the skill needs from you
For reliable stormglass-io-automation usage, give the agent the actual task, not just “use Stormglass.” Include:
- The Stormglass IO outcome you want
- Location details or identifiers required by the eventual tool schema
- Time range, forecast window, or historical period if relevant
- Units, output format, and filtering needs
- Whether the result should be returned directly or used in another workflow
Weak prompt:
Get Stormglass data.
Stronger prompt:
Use stormglass-io-automation for Workflow Automation. First discover the current Stormglass IO tools through Rube. Then find the best available tool for retrieving marine weather data for a specified location and time window. Ask me for any required fields that the schema needs before executing.
This works better because it gives the agent permission to discover schemas, select the correct tool, and pause when required inputs are missing.
Recommended workflow pattern
A practical stormglass-io-automation guide should follow this order:
- Verify
RUBE_SEARCH_TOOLSis available. - Use
RUBE_MANAGE_CONNECTIONSwith toolkitstormglass_io. - Confirm the Stormglass IO connection is
ACTIVE. - Call
RUBE_SEARCH_TOOLSwith the specific use case, not a generic query. - Inspect returned tool slugs, schemas, and pitfalls.
- Execute the selected tool only after required fields are known.
- Summarize result assumptions, missing fields, and follow-up options.
The most important habit is to search tools with your specific use case, such as “retrieve marine forecast for a coastal coordinate” or “check available Stormglass IO weather endpoints,” rather than only “Stormglass IO operations.”
Files to read before installing
This skill is compact: the repository path mainly contains SKILL.md. Read that file first because it defines the MCP requirement, connection setup, and the core discovery-before-execution pattern. There are no visible helper scripts, rule packs, or reference folders in the provided file tree, so adoption depends on whether your environment already supports Rube MCP and Composio connections.
stormglass-io-automation skill FAQ
Is stormglass-io-automation beginner-friendly?
It is beginner-friendly if you already use Claude with MCP tools, but it is not a zero-setup Stormglass API wrapper. You need Rube MCP available and a Stormglass IO connection activated through Composio. Beginners should expect the first run to involve authentication and tool discovery rather than immediate data retrieval.
Why not use an ordinary prompt instead?
A generic prompt may invent tool names, assume outdated schemas, or skip authentication checks. The stormglass-io-automation skill gives the agent a narrower operating pattern: verify Rube, manage the Stormglass IO connection, search current tools, then execute. That structure reduces avoidable MCP failures.
When should I not install it?
Do not install it if you want direct SDK code, raw Stormglass REST API examples, or offline weather calculations. It is also a poor fit if your Claude environment cannot connect to https://rube.app/mcp or if your organization blocks external MCP servers and OAuth-style connection flows.
Does it include ready-made automations?
The visible repository evidence does not show scripts, templates, or bundled workflow examples beyond the instructions in SKILL.md. Treat it as an execution pattern for Stormglass IO via Rube MCP, not as a full automation library.
How to Improve stormglass-io-automation skill
Improve prompts for stormglass-io-automation
To get better results, write prompts that separate discovery, validation, and execution. For example:
Use stormglass-io-automation. Search the current Stormglass IO tools for a workflow that can retrieve the needed marine/weather data. Report the matching tool name, required schema fields, and any missing inputs before making the call.
This prevents premature execution and gives you a chance to provide missing parameters.
Add missing operating context
The skill will perform better when you supply constraints that the tool schema cannot infer, such as preferred units, time zone, acceptable forecast horizon, precision requirements, and whether partial results are useful. If your workflow feeds another system, state the target format, for example JSON, table, CSV-ready rows, or a short operational summary.
Watch for common failure modes
Common blockers are inactive Stormglass IO connections, skipped RUBE_SEARCH_TOOLS calls, vague use cases, and prompts that assume a specific tool slug before discovery. If the agent fails, ask it to restart from connection verification and repeat tool discovery with a narrower use case.
Iterate after the first output
After the first run, improve the workflow by asking for the exact tool schema used, required fields, optional fields that could improve accuracy, and any pitfalls returned by Rube. Save those details in your project prompt or runbook so future stormglass-io-automation install and usage sessions start with clearer requirements while still allowing schema discovery to stay current.