Skill UI

This guide establishes a comprehensive and rapid development loop for Obsidian plugins. It covers the entire workflow, from setup (cloning templates, symlinking) to advanced features, including running esbuild in watch mode, achieving hot-reloading (via Ctrl+R or automatic methods), deep debugging with Chrome DevTools, and running unit tests with vitest. Essential for developers seeking fast feedback and reliable plugin iteration.

This provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

A comprehensive guide and implementation for setting up a fast, cost-effective local development loop for the Perplexity Sonar API. It includes generating type-safe client wrappers, utilizing fixtures for reliable mocking, and establishing a complete test suite with hot-reloading capabilities. This ensures stable development and manages high API call costs during iteration.

This guide provides a comprehensive workflow for setting up a robust local development environment for PostHog integrations. It covers enabling debug mode for real-time event inspection, implementing mocking strategies for stable unit testing, and configuring integration tests using a dedicated development PostHog project. Use this when building or testing analytics tracking features with posthog-js or posthog-node to ensure fast, isolated development cycles.

Guides adding PyTorch FSDP2’s fully_shard integration to training loops, covering correct distributed init, bottom-up sharding, mixed precision/offload policies, DTensor-aware optimizers, and distributed checkpointing so oversized models or DeviceMesh sharding goals work reliably.

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.

Provides systematic methods for diagnosing complex, intermittent errors in the Replit environment. This guide covers advanced topics like container lifecycle issues, Nix build failures, memory leaks, and deployment crash loops using sophisticated shell scripting and runtime monitoring techniques. Use when standard troubleshooting steps fail to identify the root cause.

This comprehensive guide details how to optimize the Replit development environment. It covers setting up run commands, implementing hot reloading for various languages (Node.js, Python), configuring ports, managing development vs. production databases, and leveraging the Replit Agent for powerful AI-assisted coding. Use this guide when setting up a new project or refining your entire dev workflow.

This skill provides implementation patterns for developing local voice agents and automating phone calls using the Retell AI SDK. It guides developers through the necessary steps for integrating complex real-time voice interactions, telephony platforms, and building automated customer service agents, ensuring robust development and error handling practices.

This guide provides a comprehensive, environment-aware setup for Sentry in local development workflows. It details how to correctly route development events to separate DSNs, enable verbose debugging, and maximize data capture rates (e.g., setting sample rates to 1.0). Users learn to use `startSpan` for performance profiling and ensure that sensitive credentials are safely managed outside of source control.

Toolkit for assembling Slack-optimized animated GIFs and emoji loops, with size/dimension validators, reusable animation primitives, and helpers for text/colors to keep creations within Slack limits.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.