rosette-text-analytics-automation
by ComposioHQrosette-text-analytics-automation helps agents run Rosette Text Analytics through Composio Rube MCP by checking connections, discovering live tool schemas with RUBE_SEARCH_TOOLS, and executing text analysis workflows.
This skill scores 66/100, which means it is acceptable for directory listing but should be presented as a lightweight Rube MCP automation guide rather than a fully self-contained Rosette workflow pack. Directory users get enough information to understand the dependency, connection setup, and tool-discovery-first execution pattern, but adoption still requires runtime schema discovery and some guesswork about specific Rosette Text Analytics operations.
- Valid skill frontmatter clearly declares the `rube` MCP requirement and a specific trigger: automating Rosette Text Analytics via Composio/Rube.
- Prerequisites and setup are explicit enough for an agent to verify `RUBE_SEARCH_TOOLS`, manage the `rosette_text_analytics` connection, and require ACTIVE status before execution.
- The skill repeatedly instructs agents to discover current schemas before running workflows, which reduces the risk of stale tool calls for an API-backed toolkit.
- Execution depends on live Rube MCP tool discovery; the skill provides no pinned Rosette tool schemas or support scripts, so agents must rely on `RUBE_SEARCH_TOOLS` at runtime.
- Repository evidence shows only a single `SKILL.md` with no install command or examples beyond MCP/tool-discovery patterns, limiting confidence for users who need concrete Rosette task coverage.
Overview of rosette-text-analytics-automation skill
What rosette-text-analytics-automation does
rosette-text-analytics-automation is a Claude skill for running Rosette Text Analytics workflows through Composio’s Rube MCP server. Its main value is not a fixed prompt template; it teaches the agent to discover the current Rosette toolkit tools first, check connection status, and then execute text-analysis operations using the live schema returned by Rube.
Best fit for Data Analysis workflows
Use the rosette-text-analytics-automation skill when your data-analysis task depends on structured signals extracted from text: entities, names, language-related metadata, categorization-style outputs, or other Rosette toolkit capabilities exposed through Composio. It is most useful when the source material is unstructured text and you want the agent to convert it into repeatable API-backed outputs rather than one-off LLM guesses.
What makes this skill different
The important differentiator is the required discovery step: RUBE_SEARCH_TOOLS must be called before execution. That matters because Composio tool names, parameters, and recommended plans can change. Instead of assuming a stale schema, the skill instructs the agent to ask Rube for available Rosette Text Analytics tools, input fields, pitfalls, and execution guidance before running a workflow.
Adoption requirements and limits
This is a lightweight skill with a single SKILL.md file and no helper scripts, rules, or bundled examples. To use it, your client must support MCP, Rube MCP must be configured, and a Rosette Text Analytics connection must be active through RUBE_MANAGE_CONNECTIONS. If you only need a conceptual explanation of Rosette or do not have access to MCP tools, an ordinary prompt may be enough.
How to Use rosette-text-analytics-automation skill
rosette-text-analytics-automation install context
Install the skill from the Composio skills repository if your environment supports Claude skills:
npx skills add ComposioHQ/awesome-claude-skills --skill rosette-text-analytics-automation
Then configure Rube MCP in your client by adding the MCP server endpoint:
https://rube.app/mcp
The skill itself does not include API keys or scripts. After MCP is available, verify that RUBE_SEARCH_TOOLS responds. Then use RUBE_MANAGE_CONNECTIONS with toolkit rosette_text_analytics; if the connection is not ACTIVE, complete the returned authorization flow before asking the agent to process text.
Inputs the skill needs from you
For reliable rosette-text-analytics-automation usage, give the agent the text source, desired Rosette operation, output format, and constraints. A weak request is: “Analyze these documents with Rosette.” A stronger request is:
“Use rosette-text-analytics-automation to analyze the following customer-support notes. First discover the current Rosette Text Analytics tools with RUBE_SEARCH_TOOLS. Then choose the appropriate tool for entity or name extraction, run only after confirming the rosette_text_analytics connection is active, and return a table with source_id, extracted item, type, confidence if available, and any records that failed.”
This improves results because it tells the agent what to discover, what to validate, what output shape you need, and how to handle incomplete tool responses.
Practical workflow for first run
Start by reading composio-skills/rosette-text-analytics-automation/SKILL.md; it is the only source file and contains the setup, discovery, and workflow pattern. A good first run should follow this order:
- Confirm Rube MCP is reachable.
- Call
RUBE_MANAGE_CONNECTIONSforrosette_text_analytics. - If inactive, complete authorization and re-check status.
- Call
RUBE_SEARCH_TOOLSwith a use case such as “Rosette Text Analytics operations.” - Select tools based on the returned schema, not guessed parameter names.
- Execute on a small sample before processing a full dataset.
- Save the tool slug, schema, and assumptions used for auditability.
Prompt pattern that reduces tool errors
Ask the agent to show its tool plan before execution when the dataset is large or high-value. For example:
“Before running the Rosette tool, summarize the discovered tool slug, required fields, optional fields, expected output, and any pitfalls returned by Rube. If required fields are missing, ask me for them instead of guessing.”
This is especially useful because the skill’s central constraint is schema freshness. The agent should not hard-code historical parameters or silently substitute fields that the active Rosette tool does not accept.
rosette-text-analytics-automation skill FAQ
Is rosette-text-analytics-automation for beginners?
Yes, if your client already supports MCP and you can follow an authorization link. The skill is short and operational, but beginners should know that it depends on external tools: Rube MCP and an active Rosette Text Analytics connection. Without those, the agent can explain the workflow but cannot execute it.
How is this better than a normal Claude prompt?
A normal prompt may summarize or infer text analytics results from the model alone. The rosette-text-analytics-automation skill is designed to route the task through Composio’s Rosette Text Analytics toolkit. That makes it better for workflows where you care about API-backed extraction, repeatability, current schemas, and connection checks.
When should I not use this skill?
Do not use it when you need offline-only processing, have no MCP access, cannot authorize the Rosette toolkit, or only need a rough natural-language summary. It is also not a complete ETL framework: if you need batching, persistence, retries, or dashboards, you will need to add that orchestration outside the skill.
What repository files should I inspect first?
Read SKILL.md first and, in this repository snapshot, only. There are no README.md, scripts/, resources/, references/, or rules/ folders for this skill. That keeps installation simple, but it also means you should rely on RUBE_SEARCH_TOOLS and Composio’s live toolkit documentation for exact schemas.
How to Improve rosette-text-analytics-automation skill
Improve rosette-text-analytics-automation inputs
The fastest way to improve output quality is to provide cleaner task boundaries. Include sample text, record identifiers, language expectations, desired extraction type, output columns, and error-handling rules. For Data Analysis work, specify whether you need row-level results, aggregate counts, deduped entities, confidence thresholds, or raw API output for later validation.
Avoid common failure modes
The most common failure is skipping tool discovery and calling a guessed tool name or field. The second is running analysis before the rosette_text_analytics connection is active. The third is giving vague goals that do not map to a specific Rosette capability. Prevent all three by requiring the agent to: search tools first, verify connection status, map the task to a discovered tool, and ask for missing required fields.
Iterate after the first output
Run a small sample first, inspect whether the output columns match your downstream use case, then refine. If entity names are too broad, ask for filtering rules. If results are hard to audit, request the source text span or original record ID when available. If batch output is inconsistent, ask the agent to normalize the response into a fixed table and preserve raw tool errors separately.
Add project-specific guidance
Because the upstream skill is intentionally minimal, teams can improve it by adding local conventions: preferred output schemas, batching limits, naming standards, review checklists, and examples for common Rosette workflows. Keep those additions separate from the core rule that the agent must always call RUBE_SEARCH_TOOLS first, because live schema discovery is the skill’s main reliability safeguard.