Skill UI

Scans codebases for debt, scores severity, tracks trends, produces prioritized remediation plans, and reports progress for engineering teams balancing refactoring with feature delivery.

Analyze frameworks, platforms, and cloud providers with weighted scoring, ecosystem health, TCO, security, and migration insights so decision makers can confidently compare stacks before committing.

A comprehensive guide to building functional and scalable Telegram bots. Covers bot architecture design, mastering the Telegram Bot API, optimizing user experience (UX), implementing monetization strategies (Freemium, Subscriptions), and deploying production-ready bots using webhooks. Learn to transform complex ideas into engaging, real-world automated assistants.

This skill provides a comprehensive collection of quick-start templates for developing applications across diverse tech stacks. Users can quickly scaffold full-stack web apps (Next.js), SaaS products, REST APIs (Express, FastAPI), mobile apps (Flutter, React Native), and specialized tools like CLI utilities. It ensures the initial project setup adheres to modern best practices, saving significant time during the initial development phase.

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

This skill provides comprehensive, structured guidance for testing Temporal workflows written in Python. It covers best practices for unit testing (using time-skipping), integration testing (mocking dependencies), and determinism validation through replay testing. Ideal for ensuring high code coverage and robust CI/CD pipelines for complex asynchronous workflows.

TensorBoard delivers a unified interface to track training metrics, debug network behavior, compare experiments, inspect model graphs, and profile performance for PyTorch or TensorFlow workflows.

Assists with tensorboard visualizer tasks for ML training by guiding setup, best practices, and experiment tracking whenever tensorboard visualization is mentioned, producing production-ready configs and validated outputs.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.