Feature Briefs for Discovery: A SERP→Spec MVP Template You Can Ship in One Sprint

High‑intent SERPs are measurable demand: people are actively searching for a solution right now. Framing discovery around one real query collapses ambiguity—what the user expects is explicit in the query, and your brief can aim directly at intent rather than vague possibilities.

Starting from search forces tight scope: you choose one outcome a user wants, define the success metric for the listing and product, and draft the acceptance criteria that prove you solved the query. That discipline makes a one‑sprint deliverable realistic and rankable.

Keep the brief to one page plus attachments. Frontload the essential fields so a contractor, designer, or small team can start immediately: Query & intent, one‑sentence value hypothesis, user story, acceptance tests, telemetry hooks, ASO copy, three mock screenshots, and a 2‑phase rollout plan.

The acceptance tests should be explicit and executable by QA or CI (examples below). Telemetry hooks name events and critical attributes so product and growth can measure real impact from day one.

Acceptance tests must be concrete. Instead of “exports correctly,” write: “Given a channel with 100 messages including code blocks and emojis, when user requests ‘Export → CSV’, then file downloads within 10s, rows = 100, emojis preserved as Unicode, and CSV columns match [timestamp, author, message, attachments].”

Telemetry should be part of the spec, not an afterthought. Define events (e.g., export_started, export_succeeded, export_failed) and the required attributes (user_id, channel_id, message_count, latency_ms, error_code). That lets analytics and growth validate that the feature reached and converted the target query.

Treat ASO fields as part of the product deliverable. A listing that ranks and converts reduces acquisition friction much faster than feature launches that rely on ads. For Google Play and the App Store, map the target query to store metadata (title, short description/ subtitle, and the first three screenshots) used to both rank and convert.

Design three screenshot frames that tell the user’s story end‑to‑end: trigger, core action, and result. Use caption text that mirrors the search intent and keeps the first slide as the clearest promise—the rest should show workflow and trust signals (e.g., ‘export in one click’, ‘preserve formatting’).

Ship small and measurable. Phase A (week 1): internal dogfood + closed beta with 5–20 users, monitor telemetry and acceptance tests. Phase B (week 2–4): staged public rollout, run store listing experiment if available, and watch conversion/engagement metrics tied to the query.

Define the decision gates: if primary metric improves by X% and error rate < Y, scale; if not, iterate on listing copy, screenshot messaging, or the acceptance criteria. Use the telemetry you built into the feature to run these experiments confidently.