Results for “product-design”

Design and review Salesforce sandbox topology, refresh cadence, data masking, and environment purpose to keep production data protected and refreshes manageable.

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA standards, generating semantic ARIA for web and accessibility traits for iOS and Android.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Identify and design growth loops (flywheels) for sustainable traction, evaluating five proven mechanisms: viral, usage, collaboration, user-generated, and referral.

Builds production-ready frontend pages with premium UI design, cinematic animations, AI-generated media assets, persuasive copywriting, and generative art.

Identify growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.

Expert strategy advisor for products built on LLMs or agents — not general product strategy (see `product-strategy` for that). Use this — proactively and without waiting to be asked — whenever choosing where to apply AI in a product, deciding between RAG and fine-tuning, designing how much autonomy an AI feature should have, evaluating whether an AI feature is actually defensible, or deciding whether to add AI to a feature at all. Also triggers for: "should this be an agent or a simple LLM call", "how much autonomy should this feature have", "RAG vs fine-tuning", "is this AI feature defensible", "our AI feature keeps hallucinating and users don't trust it", "should we build this with AI or just ship it deterministic", "AI product wedge", "what happens to this feature when the models get better", "human-in-the-loop design for AI features". Produces a decision-focused brief: the wedge, the architecture choice, the autonomy level, and the defensibility bet — each with an explicit trade-off.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment). Use when the user mentions "users arent coming back", "engagement loops", "habit formation", "push notifications", "variable rewards", "daily active users", "habit zone", or "user retention loops". Also trigger when designing notification strategies, building streaks or progress systems, or analyzing why users stop using a product after initial signup. Covers ethics evaluation and onboarding for habits. For friction reduction and B=MAP, see improve-retention. For viral sharing, see contagious.

Design products and pricing around validated willingness to pay, using the framework from Ramanujam & Tacke's "Monetizing Innovation" to avoid common monetization failures.

Build evaluation frameworks for agent systems with deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, and outcome measurement.

Production patterns and non-obvious traps for Prisma ORM in TypeScript backends, covering schema design, query optimization, transactions, pagination, and serverless connection handling.

Retention and recurring-value design for Nick's products. Use when deciding how users come back, what keeps value recurring, how to reduce churn, and how to create stronger habit or workflow retention.

Use when designing or implementing product-grade web motion like Stripe, Linear, Apple, and Vercel. Covers motion principles, easing/duration defaults, choreography patterns, scroll/hover interactions, performance, accessibility (reduced motion), and implementation guidance.

Pixel-perfect Figma to React conversion using coderio. Generates production-ready code (TypeScript, Vite, TailwindCSS V4) with high visual fidelity. Features robust error handling, checkpoint recovery, and streamlined execution via helper script.

Build, review, and refactor Java backend services (Spring Boot). Use for tasks like REST API design, controllers/services/repositories, PostgreSQL persistence (JPA/MyBatis), migrations, configuration, security (Spring Security), observability, testing, performance tuning, and production hardening.

Identifies and designs growth loops (flywheels) for sustainable traction — evaluating 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms or reducing reliance on paid acquisition. Triggers on: growth loop, flywheel, viral loop, referral program, product-led growth, PLG, user acquisition, growth strategy, retention loop, compounding growth.

A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.

Lazyweb is the design-evidence skill for AI coding agents. Use it when designing, critiquing, or changing product UI — it provides real app screenshots, competitor references, best practices, quick examples, creative cross-category ideas, paywall optimization guidance, and mobile growth + monetization A/B test context. Routes to the right Lazyweb mode (design workflow, quick search, update, flowchart, or A/B test research) using MCP tools and the Lazyweb server at https://www.lazyweb.com/mcp. Use before designing any screen when you need design evidence instead of training-data vibes.

Design and ship production-ready MCP servers from OpenAPI contracts instead of hand-written tool wrappers. Supports Python and TypeScript, schema validation, and safe evolution.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Provides production-grade Django architecture patterns covering project structure, split settings, ORM best practices, REST API design with DRF, caching, signals, and middleware.

Use this skill for brand direction, color systems, typography, tone, reusable visual themes. Trigger when the task involves web design work related to Brand Theme Factory, production implementation, audits, debugging, strategy, or validation.

Use this when the project needs a clear strategy for local, test, staging, production, cloud, serverless, or hybrid environments. Trigger on requests about environment layout, runtime differences, environment-specific behavior, or deployment-context planning.

Plan the frontend application structure for a product or feature. Use when the team needs route structure, feature modules, state boundaries, design-system needs, and frontend delivery sequencing based on defined screens and flows.

Design a detailed value proposition using a 6-part Jobs-to-be-Done template covering who, why, current situation, solution, improved outcome, and alternatives.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Use this skill for forms, checkout flows, validation, error recovery, payment UX, friction reduction. Trigger when the task involves web design work related to Forms Checkout UX, production implementation, audits, debugging, strategy, or validation.

Use this skill when creating, redesigning, critiquing, reducing, polishing, hardening, or making design decisions for a user-facing interface, brand surface, product UI, component, dashboard, flow, app shell, onboarding, empty state, or visual system. Applies Mies van der Rohe-inspired restraint, the Interface Craft workflow, and anti-generic design discipline. Not for backend-only work.

Guides users through a structured, conversational process to generate a production-ready Terraform stack and OCI Resource Manager schema for OKE clusters on Oracle Cloud Infrastructure.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.

Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems.

Creates a comprehensive end-to-end user journey map with stages, touchpoints, emotions, pain points, and opportunity areas for product design and UX analysis.

shadcn/ui expert guidance — CLI, component installation, composition patterns, custom registries, theming, Tailwind CSS integration, and high-quality interface design. Use when initializing shadcn, adding components, composing product UI, building custom registries, configuring themes, or troubleshooting component issues.

Use when designing AI product first-run or blank-slate UX — chat empty state, example prompts, capability discovery, templates, nudges, or follow-ups. Trigger on "users don't know what to ask", onboarding, suggestion chips, or discoverability.

Review a built frontend against its chosen direction, catch drift back to generic AI defaults, enforce state coverage, ethical patterns, UX heuristics, and polish, and check contrast with APCA (not the legacy WCAG ratio). Produces specific, prioritized fixes — never vibes-based feedback. Works with pasted screenshots or Playwright MCP automated capture. Load when the user asks to review a UI, audit a design, check if a frontend looks generic or vibecoded, evaluate visual quality or polish, says "review this UI", "is this design good", "audit my frontend", "does this feel like [product]", "design QA", or when frontend-design routes here. Sub-skill of frontend-design.

Set a deliberate visual direction before any UI is built — the single biggest lever against generic AI output. Derives a posture from product-soul/PRD/specs, scores a curated archetype palette, then generates 2-3 GENUINELY DISTINCT directions and compares them side-by-side before committing to one. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, explore visual options, says "what should this look like", "make it feel like [Linear/Apple/ Duolingo]", "give me design directions", "explore some looks", or when frontend-design routes here. Replaces design-archetype. Sub-skill of frontend-design.