Skill UI

Automates Tisane workflows by guiding you through Composio’s Rube MCP toolkit calls, including tool discovery, connection checks, and multi-step execution while respecting schema, session, and memory requirements.

A comprehensive, adaptive framework for managing the entire software development lifecycle, from initial project specification to final execution. It automatically sizes the depth of the workflow (Specify, Design, Tasks, Execute) based on the complexity and scope of the feature or project. Ideal for starting new projects, mapping existing codebases, implementing complex features with atomic verification, and maintaining persistent state across sessions.

Automates Todoist task, project, section, filter, and bulk workflows via Composio’s Rube MCP, guiding tool discovery, connection setup, and ordered API calls for create/update/complete/delete operations.

Automate Toggl Track workflows from Claude Code: log entries, stop timers, create projects and clients, manage tags, and inspect workspaces with natural language commands for faster time tracking.

Automate Token Metrics operations through Composio’s toolkit by orchestrating Rube MCP searches, connection checks, and schema-compliant executions so every `RUBE_MULTI_EXECUTE_TOOL` call runs with up-to-date tooling details and pagination awareness.

Uses Rube MCP and Composio's Tomtom toolkit to automate Tomtom workflows, from fetching live tool schemas with `RUBE_SEARCH_TOOLS` to validating `RUBE_MANAGE_CONNECTIONS` status and executing `RUBE_MULTI_EXECUTE_TOOL` calls with schema-compliant arguments.

Coordinates Toneden operations through Composio's Rube MCP toolkit, ensuring tool discovery, connection validation, and schema-compliant executions for marketing workflows.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

Automates the Tpscheck toolkit via Composio's Rube MCP by discovering tools, verifying connections, and running workflows while respecting dynamic schemas, session reuse, and memory requirements.

This skill provides comprehensive guidance for the entire lifecycle of Conductor tracks—the logical work units used to organize features, bugs, and refactors. It assists users in creating, reviewing, updating specifications (spec.md), developing plans (plan.md), and managing the status and metadata of tasks from initial conception through final completion, ensuring structured and traceable development processes.

Automates guidance on train/test split workflows inside ML training, surfacing best-practice patterns, code snippets, and validation tips for PyTorch, TensorFlow, and sklearn projects.