textrazor-automation
by ComposioHQtextrazor-automation is a Claude skill for automating Textrazor tasks through Composio Rube MCP, with tool discovery, connection checks, and schema-aware execution.
This skill scores 66/100, which means it is acceptable to list but should be presented as a lightweight connector guide rather than a fully developed Textrazor workflow skill. Directory users get enough information to know when to install it and how an agent should start through Rube MCP, but they should expect to rely on live tool discovery and their own task framing for actual Textrazor operations.
- The trigger and scope are clear: automate Textrazor operations through Composio's Textrazor toolkit via Rube MCP.
- Prerequisites and setup steps are explicitly stated, including requiring Rube MCP, using `RUBE_MANAGE_CONNECTIONS` for the `textrazor` toolkit, and confirming an ACTIVE connection.
- The skill gives agents an important execution rule: always call `RUBE_SEARCH_TOOLS` first to retrieve current tool schemas before running workflows.
- No support files, scripts, references, README, or install command are provided beyond the single SKILL.md, so adoption depends on already knowing how to configure MCP skills in the client.
- The workflow is mostly a generic Rube MCP discovery/connection pattern; repository evidence does not show concrete Textrazor use cases, example inputs/outputs, or troubleshooting beyond checking connection status.
Overview of textrazor-automation skill
What textrazor-automation is for
textrazor-automation is a Claude skill for running Textrazor-related automation through Composio’s Rube MCP server. It is designed for workflows where an agent needs to discover the current Textrazor tool schema, confirm an active Textrazor connection, and then execute text analysis tasks through the available Rube tools instead of guessing API calls.
Best-fit users and jobs
This skill is a good fit for teams using Claude with MCP who want to automate natural language processing steps such as extracting structured signals from text, enriching content workflows, or routing documents based on Textrazor analysis. It is especially useful when Textrazor is one step inside a larger Workflow Automation pipeline, because the skill emphasizes tool discovery, connection checks, and execution planning before action.
What makes this skill different
The main value of the textrazor-automation skill is not a large wrapper library; it is an execution pattern for Rube MCP. The upstream skill repeatedly requires RUBE_SEARCH_TOOLS before calling Textrazor operations, which matters because Composio tool schemas can change. This reduces brittle prompts and helps the agent use current tool slugs, input fields, execution plans, and warnings returned by Rube.
Adoption considerations
The repository content is compact: the skill path contains SKILL.md only, with no helper scripts, examples folder, or local test harness. Install it if you already use MCP-capable clients and want a disciplined Textrazor automation prompt pattern. Do not expect a standalone Textrazor SDK, CLI, or offline processor.
How to Use textrazor-automation skill
textrazor-automation install and setup context
Install the skill from the Composio skills repository in a compatible skills environment:
npx skills add ComposioHQ/awesome-claude-skills --skill textrazor-automation
Then configure Rube MCP in your client by adding:
https://rube.app/mcp
The skill requires the rube MCP server and expects RUBE_SEARCH_TOOLS to be available. Before using Textrazor operations, ask the agent to check or create the Textrazor connection through Rube connection management. If the connection is not active, complete the returned authentication flow before continuing.
Inputs the skill needs to work well
A good textrazor-automation usage prompt should include more than “analyze this text.” Provide the business goal, text source, desired analysis, output format, and downstream action. For example:
“Use textrazor-automation for Workflow Automation. First discover current Textrazor tools with RUBE_SEARCH_TOOLS. Analyze the following support ticket text for entities, topics, and sentiment if those capabilities are available. Return JSON with entities, topics, sentiment, confidence_notes, and a suggested routing queue. If the schema differs, adapt to the discovered fields and explain the change.”
This works better because it tells the agent to discover tools first, names the likely analysis types, defines the output shape, and allows schema-aware adaptation.
Practical workflow for reliable execution
Start every run with tool discovery:
RUBE_SEARCH_TOOLS using a use case such as “Textrazor entity and topic extraction for customer feedback.”
Then check the Textrazor connection status through Rube connection management. Only after the connection is active should the agent execute the selected tool. Ask the agent to summarize the discovered tool slug, required fields, optional fields, and any pitfalls before making the call. This small pause catches most schema mismatch failures.
For batch work, run one representative sample first. Confirm that the output fields are usable, then process the remaining items. Textrazor-style NLP outputs can be rich; forcing a concise normalized schema early prevents downstream automation from receiving inconsistent prose.
Repository files to read first
Read composio-skills/textrazor-automation/SKILL.md first; it contains the prerequisites, setup sequence, and the core workflow pattern. There are no extra scripts/, resources/, references/, or metadata.json files in this skill folder, so the install decision mainly depends on whether the SKILL.md pattern matches your MCP and Textrazor workflow.
textrazor-automation skill FAQ
Is textrazor-automation a Textrazor API client?
No. The textrazor-automation skill is a Claude skill that guides an agent to use Composio’s Textrazor toolkit through Rube MCP. It does not replace Textrazor’s own API documentation or provide a local SDK. Its value is orchestration: discover tools, verify connection, adapt to schemas, and run the right operation.
When should I use it instead of an ordinary prompt?
Use it when the agent has access to Rube MCP and must call real Textrazor tools. A generic prompt may hallucinate parameter names or assume old schemas. This skill explicitly instructs the agent to call RUBE_SEARCH_TOOLS first, which is the safer path when tool definitions and required inputs are supplied dynamically.
Is it beginner-friendly?
It is beginner-friendly if your client already supports MCP and you can add the Rube endpoint. Beginners should still understand that there are two setup layers: the Rube MCP server must be available, and the Textrazor connection inside Rube must be active. Without both, the skill cannot complete real Textrazor actions.
When is this skill a poor fit?
Avoid this skill if you need offline NLP, a non-MCP integration, a full application template, or extensive sample code. It is also not ideal if your workflow needs deterministic local tests before deployment, because the repository does not include scripts or fixtures. In those cases, combine direct Textrazor API integration with your own test suite.
How to Improve textrazor-automation skill
Improve prompts for textrazor-automation results
The fastest improvement is to make prompts schema-aware and outcome-specific. Instead of asking for “Textrazor analysis,” specify the document type, analysis purpose, acceptable output format, and how failures should be handled. For example, request: “If the discovered tool does not support sentiment, skip it and include unsupported_capabilities rather than inventing a value.”
Reduce common failure modes
Most failures come from skipping discovery, using an inactive connection, or giving text without a clear downstream decision. Require the agent to report the discovered tool schema before execution. For production-like workflows, also ask it to preserve source IDs so each Textrazor result can be mapped back to the original document.
Iterate after the first output
After the first run, inspect whether the returned entities, topics, categories, or other available fields are too broad, too verbose, or missing confidence context. Then refine the prompt with filtering rules such as minimum relevance, allowed entity types, language assumptions, or a fixed JSON schema. This turns textrazor-automation from a one-off tool call into a repeatable automation step.
Extend the skill for your environment
Because the upstream skill is intentionally minimal, teams can improve it by adding local examples, saved prompt templates, validation rules, or post-processing instructions. Useful additions include a sample ticket-routing workflow, a content-tagging schema, batch-processing guidance, and organization-specific rules for storing or redacting analyzed text before sending it through Textrazor.