Skill UI

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

This skill acts as a goal-integrity governance layer for complex, high-risk, and multi-phase tasks. It ensures the agent remains strictly aligned with the user's original objective by generating formal Goal Contracts, Phase Checks, Deviation Notices, and Final Audits. Essential for robust backend development, API calls, and validation workflows, preventing silent goal drift.

Atlas Ledger acts as a crucial memory companion to atlas-contract. It automatically triggers when a deviation ('drift') is caught during audits or reviews. Its core function is to distill the observed error or mistake into structured, actionable IF/WHEN/DON'T/INSTEAD clauses. These clauses are written to a permanent ledger (Atlas.md), ensuring that subsequent interactions are governed by learned lessons, thereby strengthening the project's overall governance and reliability.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.

Luna serves as the final quality gate, rigorously reviewing code for objective correctness, security vulnerabilities, and overall reliability. It checks for critical flaws such as injection vulnerabilities, authorization bypasses, race conditions, and adherence to the architectural blueprint. It ensures that only high-quality, safe code progresses to QA and deployment.

Max is a specialized agent designed for safe, non-behavioral code improvement. It systematically refactors existing, working code by optimizing time complexity (Big-O), abstracting duplicated logic, removing dead code, and enhancing readability. It operates under a strict constraint: no change may alter the original functionality, requiring all refactored code to pass existing test suites flawlessly.

Quinn specializes in writing and executing comprehensive test suites to rigorously prove system functionality. This role goes beyond simple pass/fail checks, focusing on mapping acceptance criteria, identifying functional gaps, and covering critical edge cases (e.g., nulls, boundary values, error paths). It validates the system's reliability across Unit, Integration, and End-to-End layers.

This skill acts as a critical quality gate, verifying claims ("done," "shipped," "fixed") made by AI agents against irrefutable ground truth. Instead of relying on the agent's self-report, it uses the DOS kernel to audit commits and check git ancestry, ensuring that claimed effects actually materialized in the code. Ideal for integrating AI workflows with robust CI/CD practices.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This guide helps developers audit and clean up Vite + React projects initialized using the Lovable scaffolding tool. It systematically removes all boilerplate artifacts, including placeholder assets, dedicated dependencies (like lovable-tagger), generated documentation, and unnecessary components (e.g., unused Radix deps). The goal is to deliver a clean, fully owned, and production-ready codebase.

This skill simulates a world-class Senior System Design Engineer with 20+ years of experience at top tech companies. It helps users design, review, and scale complex, highly available, and resilient distributed systems. It covers every aspect from database sharding and microservices decomposition to caching strategies, observability stacks, and CI/CD pipelines, providing detailed blueprints and mathematical scaling estimates.