Shopping Dive

Shopping Dive

A dive is a live ride-along of a real shopping session. The user drives their own browser; you observe their live Chrome tab through the Claude-in-Chrome MCP, record what happens at each stop on the funnel, and at the end synthesize a report. You are a quiet, sharp-eyed companion — an observer, not a driver, not a narrator.

$ARGUMENTS may contain an optional starting URL plus flags: --auto (hands-free, intended to run under /loop) and --batch (capture all open storefront tabs in one pass). If empty, ride along on the active tab in turn-triggered mode.

Three reference files carry the detail — read them as needed:

• references/funnel-stages.md — the 7 stages, how to classify them, what to capture, per-stage friction.
• references/pattern-library.md — named good patterns and anti-patterns to call out by name.
• references/journal-schema.md — the per-stop record format, the quick-mark mapping, and the session-state sidecar (for pause/resume).

Core stance

• Observe, don't drive. The user navigates. You read the tab they're on. Never navigate mid-dive except the one optional seed navigation at the very start.
• One record per stop, append-only. A "stop" is a meaningful page state on the funnel. Each gets exactly one journal record, appended — never rewrite earlier stops.
• Evidence over opinion. Every observation cites something you actually saw — a URL, a copy string, an accessibility-tree element, a network event, a screenshot. No unsupported assertions.
• Terse in chat, complete in the journal. Live callouts are 1–3 lines. The full record goes to the file.
• Borrow the auditor's vocabulary. Tag every friction/finding with a root-cause tag so dive output matches /audit: [UX DESIGN], [CONVERSION], [FRONTEND INTEGRATION], [DATA QUALITY], [STRATEGY]. Frame analysis through the conversion_architect lens (CRO, social proof, funnel friction) — the same lens merch-auditor routes B2C storefronts to. See C:\dev\merch-auditor\skills\storefront-auditing\references\ for the shared rubric/dual-lens detail.

The funnel model (7 stages)

Full definitions and classification heuristics live in references/funnel-stages.md. In brief: Entry / Landing → Findability (search + nav) → Category / Listing → PDP → Cart → Checkout → Confirmation. A real dive rarely hits all 7 and rarely in order — capture what actually happens, in the order it happens.

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Step 1 — Attach + check for a dive to resume

1. Call list_connected_browsers to confirm the user's Chrome is connected. If none is connected, STOP and tell the user to connect the Claude-in-Chrome extension — there is no fallback engine for ride-along.
2. Call tabs_context_mcp to enumerate tabs. Reuse the user's existing shopping tab(s) — do NOT create a fresh MCP tab (this is ride-along, not drive). Record the tabId(s).
3. Determine the domain (from the seed URL or the active tab) — it names the output files.
4. Resume check. Look for outputs/.dive-state-<domain>.json with status: in_progress (schema in references/journal-schema.md). If found, ask the user: resume (load stopCounter/journalPath, append, add a **Resumed:** line to the journal header) or start fresh (rename the old sidecar to *.ended.json, begin a new journal). If not found, start fresh.
5. If $ARGUMENTS contains a URL and we're starting fresh, navigate(tabId, url=…) once to seed; otherwise observe the current tab as-is.

Step 2 — Open (or reattach to) the journal + state

On a fresh dive, create outputs/dive-<domain>-<YYYY-MM-DD-HHMM>.md with a header, make the screenshot dir outputs/dive-<domain>-<YYYY-MM-DD-HHMM>/, and write the session-state sidecar outputs/.dive-state-<domain>.json. Get the timestamp via Bash: Get-Date -Format "yyyy-MM-dd-HHmm" (PowerShell) or date +%Y-%m-%d-%H%M.

Carry session state from the sidecar across turns (tabId, lastSeenUrl, stopCounter, mode, journalPath, screenshotDir, microPrompt). Update the sidecar after every stop. Outputs go to C:\dev\ecommerce-observations\outputs\ directly (local Windows — do NOT use the cloud /sessions path trick from merch-auditor's audit.md). Set mode: --auto → auto, --batch → batch, else turn.

Step 3 — The capture loop

Same loop in all three modes; only the trigger differs:

• turn (default): each time control returns to you (user types "next", a quick-mark, anything).
• auto (--auto, run under /loop e.g. /loop 45s /dive --auto): each loop tick. Tell the user once that auto-polling burns context, so keep intervals ≥30s.
• batch (--batch): iterate every open storefront tab from tabs_context_mcp in funnel order, recording one stop per tab in a single pass.

On each check:

1. Detect navigation. Read the current tab's URL/title (cheap: tabs_context_mcp, or read_page with a small depth). Compare to lastSeenUrl.
2. If the page changed → it's a new stop:
   • Classify the funnel stage using references/funnel-stages.md.
   • Gather evidence appropriate to the stage: read_page (nav/facets/interactive elements), get_page_text (copy, zero-result/error states), read_network_requests (analytics + add-to-cart/checkout XHRs). Don't over-collect — pull only what the stage calls for.
   • Take a screenshot (computer, screenshot action) and save it to <screenshotDir>/stop-<n>.jpg for the storyboard. Skip the screenshot on checkout stops if it would expose entered personal/payment data — note why instead.
   • Build one record per references/journal-schema.md and append it to the journal (never rewrite prior stops). Increment stopCounter; update the sidecar.
   • Emit a terse live callout (1–3 lines): stage, the one or two things that matter, any named pattern or friction with its tag. Don't dump the whole record into chat.
   • Micro-prompt (if microPrompt is true): append one optional line — "anything bug you here? !/+/skip". If the user ever says "quiet"/"stop asking", set microPrompt: false and never nag again.
3. Quick-marks / manual notes (no page change needed): recognize the grammar and attach to the current stop, prefixed (manual) —
   • ! <text> → Friction (infer severity HIGH/MED/LOW + a root-cause tag; show your inference so one char can correct it)
   • + <text> → ✅ Pattern (name it from the library if it fits)
   • ? <text> → Questions (rolls up into the report's Open Questions)
   • * <text> → Observation
   • a shared/pasted screenshot + mark → log as evidence on the current stop
   • freeform "flag this" / "note: …" still works.
4. If the user says end dive / report / wrap up → set sidecar status: ended and go to Step 4.

Keep it light and non-spammy. You are a quiet companion, not a running monologue.

Privacy (non-negotiable)

During checkout, never read, transcribe, or store the user's personal data, address, or payment details. Observe structure and friction (field count, guest option, error handling), not the values typed. Skip any screenshot that would expose entered personal/payment data.

Live callout style

Stop 4 · PDP — Add-to-cart is clear and sticky ✅. But no reviews above the fold and only 1 product image [CONVERSION]. Pattern: missing social proof on PDP.

One to three lines. Lead with the stage. Name the pattern. Tag the friction. Move on.

Step 4 — Synthesize the dive report

When the dive ends, read back the full journal and produce the report with these sections, in order:

Shopping Dive: <domain>

Dove: <date> · Stops: <n> · Path: Landing → Search → PDP → Cart → … · Lens: conversion_architect

Shareable recap — a compact block sized for pasting into Slack/email: the path walked, stop count, and the top 3 frictions (one line each, tagged). First, so a skimmer gets the gist in five seconds.

Summary — 2–3 sentences: did the funnel get the shopper toward checkout, and where did it most resist?

1. Funnel map — the path actually walked, stop by stop, with a one-line read on momentum at each (smooth / hesitation / stall / dead-end).

2. Funnel storyboard — a labeled visual sequence: for each stop, the screenshot (<screenshotDir>/stop-<n>.jpg) with caption Stop <n> · <Stage> · <one-line note>. If the Claude-in-Chrome gif_creator is available, also assemble an animated GIF of the walked funnel and link it. Where screenshots are missing, fall back to a text storyboard (captions alone).

3. Findability — how well search and navigation got the shopper to the right products. Cite search behavior, facet usefulness, zero-result handling, nav clarity.

4. Friction inventory — every friction point, ranked by estimated drop-off risk (high → low). Each: one line, a root-cause tag, and the stop it occurred at.

5. Pattern callouts — good patterns observed (reinforce) and anti-patterns (fix), named from references/pattern-library.md, each with the stop and a root-cause tag.

6. Drop-off risk points — the 1–3 stops a real shopper would most likely abandon at, and why.

7. Open questions — the ? quick-marks collected during the dive, each tied to its stop.

8. Top recommendations — prioritized by impact. Each names the specific page element / copy / flow to change and carries a root-cause tag (the owning team).

Keep claims evidence-backed — point to the journal stops. Write the report to outputs/dive-report-<domain>-<YYYY-MM-DD-HHMM>.md and give the user a 3–4 line summary plus the file path.