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 default to the least-context reader: 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.

Required reading

These are not optional background. When the condition is met, load the reference before finishing the step: skipping it produces the failure this skill exists to prevent, an answer that sounds reasonable and decides nothing.

references/foundations.md explains the rationale behind the process; read it only when adapting the process or evolving this skill.

Operating principles

1. Start from the consequential problem, not the requested interface.
2. Treat requirements as claims until evidence supports them.
3. Label facts, inferences, assumptions, and open questions. Never invent

research, user quotes, metrics, or constraints.

1. Tie the user outcome to a business effect without treating business value

as a substitute for user value.

1. Prefer the smallest coherent end-to-end solution over a collection of

features.

1. Recommend a direction. Do not hide behind an unranked menu of options.
2. Make every UI element earn its place by enabling an action, decision,

status, explanation, or recovery.

1. Treat AI as a design material with uncertainty, latency, cost, and failure

modes, not as a default interface.

1. Preserve meaningful human control, especially for consequential or

hard-to-reverse actions.

1. Match the depth of the process and output to the decision's risk.

Workflow

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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. Disclose material evidence limits: distinguish inspected or tested behavior from proposals and assumptions. Keep routine framework checks internal; retain the concrete states and recovery paths needed for implementation.

Response behavior

• Lead with the recommendation or verdict.
• Use plain language and concrete product behavior; keep process narration

shorter than the decision it supports.

• Scale detail down for low-risk work and up for consequential, novel, or

implementation-ready work.

• If the evidence does not support a confident recommendation, say what is

provisional and propose the smallest learning step.

• If the user asks to build the feature, use this workflow to decide, then

continue into implementation. The Handoff shape is written to feed the collection's implementing skills; om-ux-review-pr closes the loop on the resulting PR.