Skill UI

Automate Claid AI operations via the Rube MCP-powered Composio toolkit by first discovering tools, verifying active connections, and executing schema compliant actions through multi-tool calls, keeping sessions and memory consistent.

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

Defines how to automate Codeinterpreter tasks through Composio’s Rube MCP by first discovering current tool schemas, managing the Codeinterpreter connection, and executing schema-compliant tool calls while reusing sessions and honoring memory parameters.

Assists ML Training workflows by automatically activating on confusion matrix generator requests, offering structured guidance, best-practice checks, and ready-to-use code snippets.

This skill details advanced techniques for enhancing the effective context capacity of LLMs. It outlines four core strategies—Compaction, Observation Masking, KV-Cache Optimization, and Context Partitioning—to prevent context limits from constraining task complexity. By strategically managing token usage, users can reduce operational costs, minimize latency, and implement robust, long-running agent systems for handling large documents or conversations.

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

This advanced workflow extends beyond basic search capabilities. It allows users to discover pages semantically similar to a given URL, retrieve detailed content, highlights, and summaries from known URLs (batch processing), and generate comprehensive, cited answers using AI. Ideal for deep academic research, competitive intelligence, and content enrichment pipelines.

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

Automates FlowiseAI workflows by orchestrating Rube MCP and Composio tools; always searches current tool schemas, validates active Flowiseai connections, and executes schema-compliant multi-tool calls with session reuse.

Automate Composio Gamma workflows by querying Rube MCP for current tool schemas, managing Gamma connections, and executing schema-compliant tool calls with session-aware workflows.

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

Automate Gong conversation intelligence by using Composio MCP natural-language commands to fetch call recordings, transcripts, analytics, speaker stats, and workspace metadata across date ranges.