Skill UI

This utility collects comprehensive debug information for troubleshooting complex issues related to Clerk authentication. It generates an environment report, runtime health checks, client-side debug panel data, and middleware logs. Use this tool when preparing detailed support tickets or diagnosing intermittent, hard-to-reproduce bugs.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

Guides CEOs through strategic planning, capital allocation, culture shaping, and stakeholder leadership with frameworks for board prep, fundraising, and crisis thinking in fast-moving organizations.

This expert analyzes and refactors existing codebases to enhance quality, maintainability, and performance. It specializes in applying clean code principles, SOLID design patterns, and modern software engineering best practices. Use it to clean up technical debt, simplify complex modules, or prepare a codebase for new feature development.

Collects comprehensive diagnostic evidence for troubleshooting CodeRabbit integration issues. This bundle checks the App installation status, validates the `.coderabbit.yaml` configuration, reviews recent PR history, and verifies GitHub webhook deliveries. Essential for preparing support tickets and diagnosing complex SaaS integration failures.

Scans Cosmos SDK chains and CosmWasm contracts for nine consensus-critical vulnerabilities such as non-determinism, incorrect GetSigners, and ABCI panics, returning findings and fix guidance when auditing or preparing new launches.

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

Automates dataset loader creation within ML Training workflows, offering best-practice guidance, validation checks, and production-ready code to accelerate data prep and model tuning.

A comprehensive guide for diagnosing and fixing common Deepgram API errors. Covers HTTP error codes, WebSocket issues, optimal audio preparation using ffmpeg, and implementing robust retry patterns for reliable Speech-to-Text integration.

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.

Collects comprehensive, sanitized debug evidence for Deepgram support and troubleshooting. This bundle gathers detailed system environment information (Node.js, Python), performs live API connectivity tests, analyzes audio file metadata using ffprobe, and logs request/response data. Essential for preparing detailed support tickets and reproducing bugs related to Deepgram integration.