Agentic setup — follow : repo-local override contract, the design contract as constraints when present, and the untrusted-content boundary. Shared communication and reporting rules live in .

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 (§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: (§1, §3); choosing signals that mean people are better off: .

Test the proposed mechanism. When AI is involved, run the necessity gate in and explicitly consider a rules-based alternative; a design that passes the gate is then checked against , and its preferred-mistake decision and first-contact framing against . For any feature, rate the four product risks (value, usability, feasibility, viability) per (§4), adding trust, safety, privacy, and model-quality risks for AI.

Choose a direction. Generate two or three meaningfully different mechanisms, compare them per (§5), and select one, explaining the decisive trade-off. Tag the claims that carry the argument with their honest tier from . 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 ( §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 ( §7). Then fill the matching shape in , and apply 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.