Skill UI

This guide provides detailed strategies for optimizing Qdrant's operational performance. It covers critical areas such as search speed (latency and throughput), efficient vector indexing strategies, and advanced memory management techniques. Use this when you need to improve the speed, scalability, and resource efficiency of your vector database deployment.

A comprehensive guide for diagnosing and resolving slow search performance in Qdrant. This resource covers issues related to low QPS (throughput), high latency for single queries, and slow filtered searches. Solutions include tuning HNSW parameters, managing memory pressure, implementing quantization, optimizing indexing, and configuring cluster settings for maximum efficiency.

Provides comprehensive strategies for minimizing API credit consumption when using SerpApi. Techniques include implementing aggressive caching with Redis, utilizing the free Archive API for historical data retrieval, and selecting the appropriate engine (like Google Light API) to balance cost and speed. Includes cost calculation models and usage monitoring scripts.

A state-of-the-art solution for real-time object detection using the advanced YOLO 2026 model. This skill detects 80+ COCO object classes (people, vehicles, animals) and outputs bounding boxes with high precision. It features automatic hardware acceleration, optimizing the model for various platforms (CUDA, MPS, OpenVINO, ROCm) to ensure maximum speed and efficiency, whether running on CPU, GPU, or Neural Engine.

This skill provides real-time object detection capabilities by leveraging the Google Coral Edge TPU accelerator. It operates natively within Windows Subsystem for Linux (WSL), allowing high-speed, low-latency inference on live camera frames. By utilizing hardware acceleration, it achieves impressive performance (e.g., ~4ms inference at 320x320), making it ideal for edge computing projects requiring rapid visual analysis and resource efficiency.

Discipline that subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when assumptions need cross-examination, plans need stress-testing for hidden failure modes, or correctness matters more than speed. Suited for unfamiliar code, security-sensitive logic, and high-stakes migrations.

Direct access to Google's SEO APIs including Search Console, PageSpeed Insights, CrUX field data, Indexing API, and GA4 organic traffic. Provides real-time field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use for SEO analysis, website performance monitoring, and search optimization.

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

A comprehensive reference guide detailing best practices, rules, and optimizations for building high-performance React Native and Expo applications. It covers critical areas such as list virtualization, complex animations, UI patterns, state management, and monorepo structure, helping developers avoid common pitfalls and maximize app speed and stability.

A structured, multi-phase skill that systematically audits and provides actionable fixes for all four Lighthouse pillars: Performance, Accessibility, Best Practices, and SEO. It analyzes Core Web Vitals, render-blocking resources, security headers, and ARIA compliance to significantly boost site speed and quality.

An open-source, advanced tool for professional post-production of screen recordings. It automatically speeds up idle segments, applies auto-zoom on click clusters, overlays keystroke chips, and renders smooth synthetic cursors. It specializes in generating high-quality, vertical (9:16) videos optimized for social media and public demos.

Chainsaw is a fast, Rust-based forensic tool designed for first-response threat hunting. It analyzes collected Windows EVTX files using the comprehensive Sigma rule corpus, built-in detection rules, and high-speed keyword/regex searching. Furthermore, it uniquely analyzes critical artifacts such as shimcache, SRUM, and event-log gaps, providing robust, offline detection capabilities without needing a SIEM pipeline. It is essential for digital forensic triage and hypothesis confirmation.