Skill UI

This skill runs a read-only audit to assess production deployment readiness. It reviews release artifacts, migration changes, configuration gaps, and potential risks (including rollback feasibility and launch blockers) before actual deployment or merging. It helps reduce coordination failures and ensures a safe release cycle.

This skill mints tamper-evident, post-quantum-signed receipts for consequential agent actions (e.g., deployment, payment, access grant). It records who, what, and under which policy the action occurred, ensuring the evidence cannot be altered later. By providing verifiable proof from the certificate alone, it is crucial for meeting regulatory compliance requirements, such as the EU AI Act, and building auditable, trustworthy systems.

A comprehensive skill for managing AWS operational excellence, cost governance, and observability. It provides guidance on pre-deployment cost estimation (TCO), detailed spending analysis using Cost Explorer, setup of CloudWatch alarms, auditing activity via CloudTrail, and overall security posture assessment. Essential for optimizing cloud spending and ensuring reliable infrastructure.

This comprehensive tool performs an architectural audit of a codebase, mapping module dependencies, checking layering integrity, and flagging structural decay. Drawing on classic engineering principles (like Conway's Law and Testability Seams), it provides a deep assessment of the project's health and structure. Ideal for large-scale code reviews or onboarding new developers.

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 advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

This skill performs a comprehensive usability audit and heuristic evaluation of interactive user interfaces (UIs). It goes beyond mere visual aesthetics, assessing whether a system is intuitive, easy to learn, and functional for a first-time user. Use it when auditing complex tools, editors, or apps to ensure the user can complete primary tasks without needing extensive instructions or guidance.

This specialized guide assists service-area businesses (e.g., plumbing, HVAC, appliance repair) in mastering local SEO. It provides comprehensive strategies for auditing Google Business Profiles (GBP), generating high-ranking neighborhood service pages, ensuring NAP consistency, and implementing local schema markup to dominate the Google Map Pack.

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 library provides a comprehensive framework for managing bounded AI workflows (loops). Users can analyze existing codebases or work histories to discover opportunities for repeating, automated agent processes. It guides the user through finding published templates, auditing and repairing weak agents, adapting parameters, or crafting entirely new, robust automation designs, ensuring clear triggers, verification, and mandatory stopping conditions.

This skill guides the entire lifecycle of repeatable AI agent loops. Use it to discover recurring work patterns within codebases, find established solutions in a library, or interview users to craft bounded, effective loops. It supports auditing, repairing, running, and preparing AI workflows for publication, treating the loop as a robust feedback system.

This skill is designed for complex, multi-step tasks requiring coordination among multiple AI agents. It implements a robust workflow engine that first generates a detailed plan (task graph, topology) and then orchestrates parallel execution with adversarial verification. Use this when the task spans many files, requires multiple specialized roles, or involves an overall 'workflow' concept, ensuring structured, reliable, and auditable results.