Skill UI

This assistant provides comprehensive performance profiling for complex computing systems. It analyzes various targets, including Python scripts, running processes, GPU utilization, and memory bottlenecks. Utilizing both external tools (like nvidia-smi, cProfile) and advanced code instrumentation, it measures CPU, memory, and interconnect performance, generating a structured report with actionable insights and a mandatory changelog of all modifications.

This expert tool provides deep, systematic auditing of datasets to assess completeness, consistency, accuracy, and structural validity. It performs full data profiling, detects anomalies (outliers), analyzes missing data mechanisms (MCAR/MAR/MNAR), and calculates a Data Quality Score (DQS). Use it when validating data for model training, debugging pipelines, or establishing ongoing monitoring checks.

This skill acts as a Consumer Psychologist, building deep psychographic profiles by analyzing desires, fears, identity commitments, and underlying worldview. It moves beyond basic demographics to uncover the emotional drivers and core needs that shape consumer behavior. Ideal for pre-positioning, developing persuasive copy, and designing effective funnels.

This tool enables deep inspection and validation of web applications by connecting to the browser's DevTools Protocol (MCP). It allows agents to perform tasks like DOM inspection, capturing console logs, monitoring network requests, profiling performance, and taking screenshots in a live environment. Essential for debugging UIs, validating API integrations, and performing end-to-end automated testing.

A comprehensive guide and toolkit for measuring Go application performance. Covers the entire workflow: writing benchmarks, running tests with statistical rigor, profiling hot paths using pprof, comparing results with `benchstat`, and tracking regressions in CI. Helps engineers ensure reliable and measurable performance improvements.

This skill provides comprehensive best practices for monitoring Go services in production. It covers the five critical pillars of observability: structured logging (slog), Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling, and Real User Monitoring (RUM). Use this skill to proactively instrument new features, set up advanced alerting, correlate logs with traces, and ensure full visibility into system health and user experience.

A comprehensive guide and skill set for optimizing Go (Golang) application performance. It establishes a core philosophy of profiling before optimizing, focusing on reducing allocations, improving CPU efficiency, and addressing structural anti-patterns. Provides systematic modes for deep review (hot path, architecture review) and follows an iterative methodology (Benchmark -> Diagnose -> Improve -> Compare) to ensure measurable and stable performance gains.

A comprehensive guide for systematically debugging Golang programs. This skill helps users find and fix the root cause of bugs, crashes, deadlocks, race conditions, and performance bottlenecks. It covers advanced techniques like pprof profiling, using Delve, implementing test-driven debugging, and follows a rigorous methodology to ensure deep root cause analysis before proposing any fix. Ideal for production environments and complex concurrency issues.

This skill covers optimizing application performance across frontend, backend, queries, and databases. It involves measuring performance using tools like Lighthouse and RUM, identifying bottlenecks such as N+1 queries or slow server responses, and implementing fixes to improve Core Web Vitals and load times. Use this when performance requirements exist, regressions are suspected, or profiling reveals inefficiencies.

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

Accelerate scientific Python code on NVIDIA hardware using CUDA and RAPIDS libraries. Optimizes NumPy, pandas, scikit-learn, and ML workloads for GPU execution. Includes profiling, memory management, and kernel optimization to ensure correctness and performance gains.

This skill creates structured competitor profiles from a list of URLs by combining live site scraping with SEO and market data. It supports two data source stacks (Firecrawl/DataForSEO or WebFetch/alternative SEO MCPs), saves raw data for auditability, and emphasizes factual, current, and honest assessment. Use it for competitive intelligence, deep dives, and landscape analysis.