Skill UI

This skill acts as a comprehensive maintenance orchestrator for the brooks-lint plugin repository. It executes a rigorous, sequential subagent pipeline—including authoring, evaluation, QA, trigger auditing, and release management. Use this when adding, editing, or updating skills to ensure all related components (manifests, README, CHANGELOG) are synchronized and adhere to strict quality gates throughout the development lifecycle.

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

This comprehensive skill guides the creation of high-quality technical content specifically tailored for developers. It covers the entire devrel content lifecycle: from researching topic validation (community signals, competitor gaps) to selecting the ideal content type (tutorial, guide, case study). The framework emphasizes deep technical accuracy, structured outlines (Hook, Meat, CTA), and writing runnable, copy-paste ready code examples to build trust and drive adoption.

This skill provides an autonomous, repository-wide pipeline for auditing and fixing complex logical inconsistencies. It systematically executes stages including health checks, deep review, locating divergences, generating fixes, and performing diff-verification until the codebase is deemed clean. Ideal for maintaining high code quality and resolving deep, systemic bugs across entire projects.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.

This skill guides users through the complete lifecycle of data quality incident management using Monte Carlo MCP tools. It performs detailed investigation, including root cause analysis and blast radius assessment. It then coordinates various external tools (APIs, CLI) to propose and execute appropriate remediation actions, ensuring all data incidents are resolved with full context.

Rewrites overly formal or vague code review comments generated by AI or corporate tools. It cuts out filler phrases like "I noticed..." or "It might be worth considering...", transforming them into direct, actionable feedback. The output uses a clear format (Location: Severity: Observation. Fix.) that sounds like it came from a helpful, experienced teammate, significantly improving the quality and reception of PR feedback.

This skill applies rigorous, F1-inspired standards to software quality assurance. It executes comprehensive test plans, classifies failures across five critical tiers (Brake, Engine, Aero, Tyre, Pit), audits Continuous Integration (CI) signal integrity, and generates detailed WJTTC reports. Use it to validate functionality, reproduce complex bugs, or determine the readiness of a release.

A sophisticated template designed for creating long-form, magazine-quality articles. Ideal for thought leadership pieces, detailed case studies, or in-depth essays. It includes structural elements like mastheads, pull quotes, figures, and author bios to enhance readability and professionalism across various professional domains.

Generate highly cinematic and vintage-styled opening frames or chapter cards for films and documentaries. This template simulates the authentic look of analog film using light leak effects, 35mm grain noise, and classic letterboxed aspect ratios (2.39:1 or 16:9). Perfect for giving your video project an immediate, professional, and nostalgic film quality.

This advanced trading journal captures the full context of a trade, recording not only the fill price and P&L, but also the initial thesis, the defined plan (stop/target), and the emotional state at the time of entry. It integrates with Notion to log and review trades, grading the quality of decisions and execution against the user's own plan, ensuring users learn from process, not just profit.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.