Skill UI

This skill generates a comprehensive, role-assigned checklist for product launches, feature releases, or major updates. It guides cross-functional teams through pre-launch readiness (Engineering, Marketing, Support), critical Go/No-Go decision points, launch day execution, and post-launch monitoring, ensuring a structured and complete deployment process.

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 helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

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 comprehensive, multi-phase checklist for ensuring the quality, security, and deployment readiness of Django applications. It covers linting (ruff, mypy), unit testing (pytest), database migrations, security audits (bandit, pip-audit), and performance checks, making it an essential quality gate for CI/CD pipelines before merging or deploying to production.

A comprehensive framework for creating structured, clean, and highly accessible design systems. It defines foundational constraints, typography scales, color tokens, and component-level rules, ensuring consistency and usability across engineering and design teams. Focuses on achieving WCAG 2.2 AA compliance and implementation readiness.

A comprehensive 7-section readiness report generated for a repository, analyzing key metrics including risk score, test confidence, and publish readiness. It assesses architectural viability, agent topology recommendations, and potential risks, making it essential for pre-mint reviews, CI/CD gating, and detecting architectural drift in large codebases.

This skill executes the metaharness score to provide a comprehensive, multi-dimensional readiness scorecard for a codebase. It assesses critical dimensions like compilation confidence, task coverage, tool safety, and memory usefulness. Use it to evaluate if a repository is prepared for harness adoption before initiating development tools or framework setup.

This skill guides the planning and review process for complex home or small-lab network changes. It covers creating staged migration plans for VLAN segmentation, implementing local DNS resolvers (like Pi-hole), setting up secure VPN access, and ensuring network safety through detailed inventory checks and rollback procedures. It emphasizes security best practices and trust zoning.

This skill helps transform vague product or engineering ideas into structured, verifiable acceptance criteria (AC). It is crucial for high-impact changes involving security, data migration, or external APIs. By inspecting existing context and asking minimal, targeted questions, it produces clear, observable criteria, significantly de-risking the development process and ensuring handoff readiness.

A comprehensive guide providing production-grade YAML patterns and debugging commands for Kubernetes deployments. Covers best practices for Deployments, Services, Ingress, resource management (requests/limits), RBAC, probes, autoscaling (HPA), and detailed kubectl debugging cheatsheets.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.