| name | product-design |
| description | Use when designing, reviewing, validating, debugging, or improving products, product discovery, UI/UX, AI/ML product experiences, design systems, product metrics, business outcomes, assumption tests, user research plans, opportunity-solution trees, feature proposals, product requirements, UX reviews, launch/readiness plans, governance, adoption, and long-term product maintenance. |
Product Design
Use this skill to design or review products end to end. Treat product design as the connection between customer outcomes, business results, discovery evidence, usability, feasibility, viability, UI/UX quality, AI trust, design systems, metrics, release safety, and maintenance.
Core Rule
Do not start from a screen, component, model, or feature. First clarify the user, context, desired outcome, opportunity, evidence, assumptions, risks, success metrics, guardrails, fallback, ownership, and maintenance path.
Reference Routing
Read only the references needed for the current task.
- Always start with
references/00-index.md for broad product design work or to choose a route.
- For shared vocabulary, read
references/01-terminology.md.
- For organization, empowered teams, outcomes over output, product roles, and value/usability/feasibility/viability risks, read
references/02-product-operating-model.md.
- For product discovery, opportunity-solution trees, outcome -> opportunities -> solutions -> assumptions, and discovery anti-patterns, read
references/03-discovery-and-opportunity-solution-trees.md.
- For interview planning, research questions, story-based interviews, user evidence, and synthesis, read
references/04-user-research-and-interviewing.md.
- For validation plans, assumption tests, experiment selection, AI validation, UI validation, and readiness to build, read
references/05-validation-and-assumption-tests.md.
- For AI/ML product UX, AI-vs-non-AI decisions, data needs, explainability, automation, model errors, and feedback loops, read
references/06-ai-ml-product-ux.md.
- For calibrated trust, confidence, user control, correction, undo, fallback, granular feedback, and global controls, read
references/07-calibrated-trust-and-human-control.md.
- For UI/UX product quality, states, accessibility extensions, instrumentation, design system fit, and implementation quality, read
references/08-ui-ux-product-quality.md.
- For design systems as product infrastructure, design tokens, components, docs, users, ownership, and AI support, read
references/09-design-systems-as-product.md.
- For design system governance, contribution, adoption, buy-in, documentation, automation, and metrics, read
references/10-design-system-governance-adoption-metrics.md.
- For business outcomes, product metrics, guardrails, leading/lagging indicators, AI metrics, and design system metrics, read
references/11-business-outcomes-and-product-metrics.md.
- For concrete review and design checklists, read
references/12-agent-product-design-checklists.md.
- For red flags and failure modes, read
references/13-anti-patterns.md.
Workflow
- Classify the request:
- New product or feature: read
00, 01, 02, 03, 05, 11, then topic files.
- Product discovery: read
03, 04, 05, 11, 13.
- User research plan: read
04, then 03, 05, and 11 if the research drives prioritization.
- AI/ML product or assistant UX: read
06, 07, 05, 08, 11, 13.
- UI/UX review or frontend implementation: read
08, then 07 for AI flows, 09 for design-system fit, and 12 for checklists.
- Design system work: read
09, 10, 11, 13.
- Metrics, validation, or launch readiness: read
05, 11, 12, and topic files for the relevant surface.
- Fact-checking or source-sensitive work: read
14 and sources before making strong claims.
- Identify blocking unknowns. Ask only when missing information changes the decision; otherwise state assumptions.
- Separate outcome, opportunity, solution, and assumptions.
- Evaluate value, usability, feasibility, and viability before committing to a solution.
- Produce practical recommendations with validation, metrics, guardrails, fallback, ownership, and maintenance.
- Mark any recommendation not grounded in the references as
external extension.
Output For New Product Or Feature Design
Include:
- User, context, and job/problem.
- Business problem and desired outcome.
- Current evidence and source quality.
- Opportunities and selected target opportunity.
- Candidate solutions, including simpler non-AI/non-custom alternatives when relevant.
- Critical assumptions and validation plan.
- Value, usability, feasibility, and viability risks.
- UI/UX requirements, states, accessibility, instrumentation, and design-system fit.
- AI capabilities, limits, feedback, correction, fallback, and controls when AI is involved.
- Product metrics, business metrics, guardrails, and thresholds.
- Rollout, support, monitoring, ownership, maintenance, and future-change plan.
Output For Product Or UX Review
Lead with risks and missing decisions:
- Outcome and user-context gaps.
- Unsupported opportunities or solution-first thinking.
- Weak or missing value/usability/feasibility/viability validation.
- AI trust, uncertainty, correction, or fallback gaps.
- UI state, accessibility, instrumentation, and implementation gaps.
- Design-system governance, adoption, or component-fit risks.
- Metric and business-impact gaps.
- Concrete fixes, experiments, and questions to unblock the work.
Output For AI/ML Product UX
Prioritize:
- Decide whether AI is needed and compare non-AI baselines.
- Define user benefit, data needs, model-quality risks, and error costs.
- Design expectations: capabilities, limits, uncertainty, explanations.
- Preserve control: dismiss, correct, edit, undo, retry, manual path.
- Plan feedback loops, tuning, monitoring, behavior-change communication, and fallback.
- Connect model metrics to user and business outcomes.
Output For Design Systems
Include:
- System users and repeated product needs.
- Current artifacts: design, code, tokens, docs, examples, tests.
- Governance model and decision rights.
- Contribution path and quality gates.
- Adoption blockers and migration plan.
- Metrics across adoption, quality, speed, consistency, community, and business impact.
- Documentation, release notes, versioning, deprecation, support, and ownership.
Quality Bar
- Do not equate output with outcome.
- Do not call a need an opportunity until it is backed by customer evidence or clearly marked as an assumption.
- Do not jump from opportunity to one solution without alternatives and assumption tests.
- Do not add AI without proving unique AI value and designing uncertainty, correction, fallback, and control.
- Do not call a UI product-ready without non-happy-path states, accessibility basics, instrumentation, error handling, and ownership.
- Do not call a design system mature because it has a UI kit or component package; require adoption, governance, docs, release process, and metrics.
- Do not overclaim source coverage. Rosenfeld notes are based only on public pages/samples, and zeroheight survey data must keep its sample limitations.
