Build production ML systems with PyTorch 2.x, TensorFlow, and

Build knowledge graphs for support systems, connecting concepts, articles, and solutions

Build retrieval-augmented generation systems that ground LLM responses in your data

Best practices for Turborepo monorepo build system configuration and optimization

フロントエンドUIデザインを洗練された独自性のあるスタイルで生成します。ランディングページ、ダッシュボード、Webアプリケーションのデザイン、UIコンポーネント作成時に使用してください。「AIっぽい」汎用デザインを避け、プロフェッショナルで記憶に残るUIを実現します。

Quick reference for operating within jonmagic's second-brain workspace. Use when working with files in the brain repository—provides directory structure, naming conventions, append-only norms,...

Analyze meeting transcripts to extract decisions, action items, blockers, sentiment, and generate follow-up emails. Use when user provides meeting notes, transcripts, or recordings and needs...

Implement feature flags (toggles) for controlled feature rollouts, A/B testing, canary deployments, and kill switches. Use when deploying new features gradually, testing in production, or managing...

GNU Make automation and build system guidance

Use when building UIs leveraging the WordPress Design System (WPDS) and its components, tokens, patterns, etc.

Use when tempted to add features "for later". Use when building "production-ready" systems before needed. Use when adding flexibility that isn't required yet.

Expert in resilience testing, fault injection, and building anti-fragile systems using controlled experiments.

Technical documentation and knowledge management expert. Use when creating comprehensive documentation systems, improving developer knowledge sharing, or building documentation-driven development...

Build accessible, responsive, and performant frontend components with design system best practices, modern CSS, and framework-agnostic patterns.

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.

Use when creating or modifying dbt Semantic Layer components including semantic models, metrics, and dimensions leveraging MetricFlow.

Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.