Skill UI

Accelerates CPU-bound Python workflows by translating NumPy, pandas, ML, graph, image, simulation, and IO code into NVIDIA GPU kernels using CuPy, Numba CUDA, Warp, cuDF/cuML/cuGraph, KvikIO, cuSpatial, and RAFT for dramatic speedups.

Extracts business domain knowledge from a codebase, optionally reusing an existing knowledge graph, and generates an interactive horizontal flow graph for dashboard visualization, with lightweight scanning and validation steps.

Guides creation of persona-targeted VS Code CodeTour .tour files, covering repo exploration, intent inference, step composition, and validation so each narrative walkthrough is anchored to real files and tailored depth.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

This skill covers the advanced language features introduced in Dart 3, including Pattern Matching, Records, sealed classes, and enhanced control flow structures. Learn how to use `if-case` and switch expressions to write safer, more concise, and idiomatic Dart code, and how to manage complex data shapes using records and patterns.

A comprehensive guide to structuring robust Flutter applications using industry-standard layered architecture (UI, Domain, Data). It details patterns like MVVM, Repository pattern, and Use Cases, ensuring clean separation of concerns, manageable state flow (unidirectional data flow), and testability. Ideal for scaffolding new, large-scale projects or refactoring legacy codebases.

This skill provides comprehensive guidelines for writing clean, idiomatic, and maintainable Go code. It covers critical best practices for variable declaration, control flow structuring (e.g., early returns, switch statements), function design (e.g., parameter limits, context usage), and ensuring correct data structure initialization. Use this skill when writing new Go features, reviewing existing code for clarity, or establishing team coding standards.

A comprehensive guide and utility for applying idiomatic Go patterns, serving as an architectural guide for building robust, production-ready Go applications. It covers best practices for functional options, graceful shutdown, error flow management, resource cleanup, concurrency, and dependency injection, helping developers avoid common pitfalls like using `init()` or unbounded resources. Ideal for code reviews and initial system design.

This guide provides comprehensive best practices for Go code quality using the industry-standard `golangci-lint` tool. It covers the full development workflow, including initial setup and configuration of `.golangci.yml`, running linters (like govet, staticcheck), automatically fixing issues, correctly suppressing warnings using `//nolint` directives, and interpreting complex linter output to ensure high code maintainability and security.

This meta-skill provides a comprehensive guide through the entire software development lifecycle (SDLC). It teaches senior-level engineering discipline, covering everything from vague idea refinement and specification design to planning, implementation, testing, code review, and deployment. It emphasizes core behaviors like surfacing assumptions, managing ambiguity, maintaining scope discipline, and enforcing technical simplicity.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

This workflow guides developers through generating standardized, descriptive commit messages using the Conventional Commits specification. It provides a structured XML template (including type, scope, description, body, and footer) and integrates directly into the Git workflow, ensuring project history is clean, traceable, and machine-readable. Ideal for maintaining large, collaborative codebases.