Skill UI

A comprehensive 7-stage super workflow designed for advanced GitHub repository management. This tool automates deep audits to detect security secrets, junk files, and code quality issues. It further provides structured PR review capabilities, competitor benchmarking against similar projects, and synthesizes all findings into a prioritized, actionable plan, ensuring your repository is optimized and production-ready.

This skill reviews code differences (diffs) to identify opportunities for improvement in clarity, reusability, efficiency, and overall quality. It employs multiple specialized sub-agents (e.g., Code Reuse, Efficiency, Quality) to audit changes. It can provide high-confidence, behavior-preserving fixes when requested, ensuring the code adheres to best practices without introducing regressions. Best used when refactoring, cleaning up, or auditing a specific code scope.

This tool meticulously measures whether AI coding agents adhere to predefined skills, rules, or architectural guidelines. It automatically generates expected behavioral sequences and runs scenarios with varying prompt strictness. It deeply analyzes tool call traces, classifies behaviors using LLMs, and generates detailed compliance reports, ensuring robust quality control and adherence to defined workflows.

GAN-inspired Generator-Evaluator harness orchestrates planner, generator, and evaluator agents to turn single-line prompts into production-ready applications, iterating with structured sprints and Playwright-backed QA for uncompromising quality.

A comprehensive guide covering best practices for video creation using Remotion within React. This resource provides domain-specific rules and detailed examples for handling complex multimedia elements, including 3D rendering (Three.js), advanced animations, audio synchronization, data charting, captioning, and scene transitions. Learn how to structure professional, high-quality videos entirely through React components.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

Context Engineering Protocol optimizes agent sessions by curating persistent rules, specs, source files, and feedback so outputs stay focused, accurate, and aligned with conventions whenever you start work or the quality drops.

This comprehensive guide outlines best practices for writing idiomatic, high-quality Dart and Flutter code. It covers detailed conventions for naming (e.g., UpperCamelCase, lowerCamelCase), proper use of types (final, sealed classes), structuring files, managing imports, and writing accurate doc comments. Use this skill when developing new Dart features, refactoring existing code for consistency, or reviewing pull requests for style compliance.

This skill provides comprehensive guidance for setting up production-grade Continuous Integration/Continuous Deployment (CI/CD) pipelines for Go projects using GitHub Actions. It covers all critical stages, including rigorous unit testing with race detection, advanced linting, static application security testing (SAST) via tools like CodeQL and govulncheck, automated dependency management (Dependabot/Renovate), container image building, and automated binary release using GoReleaser. Use this when building a robust quality gate for a Go codebase.

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 skill simulates a Go modernization engineer, continuously updating Go codebases to comply with the latest language features, standard library improvements, and idiomatic patterns. It scans for deprecated APIs, outdated Go patterns, and suggests best practice refactorings (e.g., using `slog`, advanced testing patterns). Use it during code review, large-scale refactoring, or when upgrading Go versions to ensure high code quality and maintainability.

A sophisticated meta-skill designed for autonomous, rigorous development cycles. It implements a multi-stage 'Gate Protocol' using an external 'Oracle Hook' that independently verifies the completion status, preventing the AI from self-deceiving. Features include Failure Memory (ASI), Stall Detection for forced reflection, and an escalating 'Pressure Upgrade' mechanism to ensure deep root cause analysis. This system runs until external verification confirms success, guaranteeing maximum code quality and robustness.