OM UX Shape by @openmercato · Skillet

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OM UX Shape

Turn a vague product, UI/UX, or AI feature idea into a decided direction. Use when shaping a feature, simplifying an overcomplicated flow, deciding whether and how to use AI, defining screen states, planning validation, or preparing a design handoff for engineering.

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SKILL.md

om-ux-shape

Turn a vague product, UI/UX, or AI feature idea into a decided direction. Use when shaping a feature, simplifying an overcomplicated flow, deciding whether and how to use AI, defining screen states, planning validation, or preparing a design handoff for engineering.

Shape Useful Features

Turn ambiguity into a clear product decision before turning it into screens. Connect user value, business value, interaction quality, AI behavior, delivery constraints, and evidence in one lightweight process.

Input — a feature idea, an existing concept or product area, or a decided direction that needs implementation detail. Output — one filled shape from references/report-templates.md.

Choose the mode

Shape for a vague opportunity, request, or feature idea. The default.
Review for an existing concept, flow, design, prototype, or product area (including a whole module handed over by om-ux-review-pr).
Handoff when the direction is decided and implementation-ready behavior is what is missing.

Combine modes only when the request genuinely spans them, and never make a small task carry the full process.

Who reads the result

Establish this before writing, because it decides how concrete the output must be. Ask when it is unclear, otherwise : someone who will build or draw this, does not know the design system, and was not in the conversation. Write for them. A senior designer can skim a concrete answer; nobody can build an abstract one.

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Condition	Load
Shape or Review mode	`references/decision-framework.md`
AI is proposed, implied, or already present	`references/ai-interaction.md`, then `references/hai-guidelines.md` and `references/reward-and-mental-models.md` for whatever passed the gate
Outcomes or validation metrics are being defined	`references/human-value-metrics.md`
Before writing any result	`references/report-templates.md`
Before delivering any result	`references/quality-rubric.md`

Agentic setup — follow references/agentic-setup.md: repo-local override contract, the design contract as constraints when present, and the untrusted-content boundary. Shared communication and reporting rules live in references/rules.md.
Establish the decision. State the decision being made, the primary actor and situation, the intended user and business outcomes, and the mode and depth. Ask only questions whose answers could materially change the direction; otherwise proceed with clearly marked assumptions.
Build the evidence ledger. Separate what is known, inferred, assumed, and unknown, following references/decision-framework.md (§2). Prioritize unknowns by decision risk, not curiosity.
Diagnose. Write a one-sentence diagnosis naming the main obstacle to progress, distinguishing the underlying job from the requested feature. Then define one primary behavioral outcome, its plausible business effect, and a guardrail against harmful optimization. Framing and outcome discipline: references/decision-framework.md (§1, §3); choosing signals that mean people are better off: references/human-value-metrics.md.
Test the proposed mechanism. When AI is involved, run the necessity gate in references/ai-interaction.md and explicitly consider a rules-based alternative; a design that passes the gate is then checked against references/hai-guidelines.md, and its preferred-mistake decision and first-contact framing against references/reward-and-mental-models.md. For any feature, rate the four product risks (value, usability, feasibility, viability) per references/decision-framework.md (§4), adding trust, safety, privacy, and model-quality risks for AI.
Choose a direction. Generate two or three meaningfully different mechanisms, compare them per references/decision-framework.md (§5), and select one, explaining the decisive trade-off. Tag the claims that carry the argument with their honest tier from references/evidence-tiers.md. In Review mode, rank findings by impact × frequency × reach, never by ease of fix.
Shape the smallest coherent feature. One primary job and happy path, the minimum states and recovery paths trust requires, and an explicit now, later, and not-doing split (references/decision-framework.md §6). A thin but broken slice is not an MVP: the smallest coherent feature completes a real job end to end and survives its likely failures.
Specify the interaction contract, concretely. Entry point and trigger, information required, system response, primary decisions and actions, the relevant empty, loading, partial, success, error, and permission states, and the edit, undo, dismiss, retry, fallback, or escalation paths, plus accessibility and content requirements. For AI, also specify capability framing, uncertainty, explanations, data use, feedback, control, and behavior when the model cannot help.

Concrete means: name the screens, name the components (from the contract registry when one exists), and write the actual labels, headings, empty-state sentences, and error messages rather than describing them. "Add a helpful empty state" is unfinished work; the finished version says what the screen shows, in the words the user will read. If a reader could not build or draw it from your output, the step is not done.
De-risk and deliver. Name the riskiest unverified belief, choose the smallest test that could change the decision, and state what each result triggers (references/decision-framework.md §7). Then fill the matching shape in references/report-templates.md, and apply references/quality-rubric.md before delivering: a zero in diagnosis, user outcome, coherent scope, AI necessity, or AI control and recovery means the result is not ready. Close with the one-line Applied note the template defines, so the reader can see which checks ran and which did not apply instead of guessing what was considered.