Skill UI

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.

A comprehensive skill guide for designing, implementing, and running robust End-to-End (E2E) and integration tests in Flutter projects using Patrol 4.x. Use this to validate complex user journeys, capture UI regressions, and test native interactions (permissions, system dialogs) across multiple platforms (Android, iOS, Web).

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.

Test-Driven Development (TDD) is a software development methodology where tests are written before the actual code. It follows the Red-Green-Refactor cycle to ensure code quality and reliability. Use this skill when implementing new logic, fixing bugs, or modifying existing functionality to prove correctness. It helps prevent regressions and creates a robust codebase suitable for AI agents.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

A comprehensive guide to writing, structuring, and reviewing high-quality unit and widget tests for Flutter and Dart applications. Learn best practices for ensuring test validity, distinguishing between unit and widget scope, mastering mocking, and optimizing test files for robust regression detection.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

Aeon is a scikit-learn compatible Python toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search with specialized algorithms.

Comprehensive guidance for machine learning in Python using scikit-learn. Covers classification, regression, clustering, preprocessing, model evaluation, and pipelines for building production-ready ML workflows.

Provides guided statistical analysis for research data, covering test selection, assumption checking, effect sizes, and APA reporting. Supports frequentist and Bayesian methods including t-tests, ANOVA, regression, and correlation. Ideal for comparing groups, testing hypotheses, and analyzing experimental or survey data to produce robust, reviewer-ready results.

This guide details securing the CAST AI integration across multiple layers. It covers best practices for API key management (including rotation and secrets storage), implementing least-privilege RBAC using 'kubectl', deploying the Kvisor security agent for runtime scanning, and setting up Kubernetes Network Policies to restrict egress traffic. Use this when hardening production clusters or performing security audits.

Automates comprehensive regression testing for all enabled cloud Large Language Model (LLM) providers. This includes verifying core connectivity, standard chat completion, structured JSON output, and Server-Sent Events (SSE) streaming capabilities. Essential for ensuring the reliability and stability of multi-cloud AI integrations in a CI/CD pipeline.