Noibu Checkout Performance & Health Analysis

Surfaces where shoppers drop off in checkout and what to do about it, as a ranked
triage board built from Noibu session, value, and error data.

How it works

Run Setup, then pick one of two behaviors from the user's prompt:

Quick answer (one focused question) → run 1–2 queries, answer directly, offer to go deeper.
Full analysis (broad request, bare invocation, or "yes" to the offer) → the four-step workflow below.

Full analysis flow: (1) broad overview (Q1–Q7, current + prior window) → (2) cross-reference + period-over-period → (3) pick the top 3 regressions → (4) run targeted follow-ups → render the board (one widget). Signals are flagged on change vs the prior window, not absolute level — see references/queries.md "Signal model".

Setup — before any query

Work quietly. Don't narrate plumbing — resolving the domain, loading reference files, and reading the board format all happen silently, with no "let me…" commentary. The first thing the user sees is the Step 1 overview line ("Starting with a broad look…"); after that, narrate only real analytical progress (what the data shows), never file reads or tool setup.

Resolve the domain first; keep the company id it returns. If the user gave a domain (name or UUID), use it. If not, ask which one via AskUserQuestion populated from the user's domains — don't interrogate for anything else; take the default window and proceed. If the account has exactly one domain, skip the question and use it. Domain resolution returns a company id alongside the domain UUID; some tools (priority errors, data-connection checks) need that company id too — carry both.
Load the Noibu context reference (the querying-noibu-data skill/reference). It maps the role-based names used here ("session query tool", "funnel depth field", etc.) to the real Noibu tools/columns and documents query constraints. If it isn't available, discover tools and field names from the live Noibu API / tool schema instead — don't stop or guess.
Confirm every field name by role before using it (from the context reference, else the live schema). Steps below name fields by role, never by hard-coded column, so when the API changes only the lookup moves.
Default analysis window: the one in the context reference; if none, last 30 days.
Call list_scheduled_tasks now — note whether any task's prompt references this domain; this sets the action-bar Schedule button label later ("Schedule insights" vs "Edit scheduled insights") without blocking rendering.

Session tool — what it can group and filter by

Group by: device, country, browser, OS, UTM source/medium, landing URL, bounced, discount-applied, and funnel depth (how far the session got).
Filter on: device, country, bounced, checkout-completed, landing URL, UTM source.
Funnel depth — confirm the exact field name; the prefix matters. It is groupable (working name ~`CONVERSION_FUNNEL_DEPTH`), but near-variants are rejected. If a group-by is rejected, look up the exact name and retry; only fall back to per-stage count measures if it truly isn't available.
Payment and delivery method are NOT groupable. Attempt those slices only if the reference/schema exposes such a field; if not, skip them (see Principle 1).

Two principles that govern everything

Principle 1 — analyze what's present, never flag what's missing. Which fields are populated varies by platform, integration, and account. An empty/null result is normal — that signal just isn't available for this domain, not broken.

Silently skip any empty/null slice and build from the data you do have. Omit its table entirely — no zero-rows, no "no data" placeholder.
Never create a finding, action, chip, or caveat about missing data, tracking, capture, or coverage. That's out of scope.
There's always a useful story in the present data (funnel progression, device/country splits, conversion, errors, populated value/method fields). Lead with it confidently, as if it's the full picture.

Principle 2 — trust cart → completion when intermediate events are uninstrumented. If checkout-started and/or payment-submitted are near-zero while completions are healthy, those events aren't instrumented here. Don't treat the apparent "drops" as real — use cart → completion as the headline conversion metric and lean on segment splits (device, country, discount) for the drop story.

Exception to Principle 1 — the whole domain is empty. If the entire dataset is near-zero (total sessions 0 or a handful), there's no story to tell. Say plainly the domain has essentially no traffic in this window and offer to widen the range or pick another domain. This is a "no data in the window" statement (fine to surface), not a tracking-coverage complaint (still out of scope).

Quick answer

A specific, focused question ("where do people drop off?", "what payment methods are used?", "errors on my checkout pages?"):

Load references/queries.md and run only the 1–2 query specs needed (e.g. funnel → Q1, payment methods → Q3, errors → Q5).
Answer in a few sentences + a small table if useful.
Offer a deeper analysis; if yes, run Full analysis.

Don't load the triage-board or scheduling references for a quick answer.

Investigate click

A triage-board Investigate button arrives as a new chat prompt ("Investigate this checkout signal: …") via sendPrompt — handle it as a focused follow-up, not a re-render of the board. The follow-up specs are in references/queries.md (Step 4).

Don't re-run the breakdown that built the card. The Step 4 follow-up for this signal already ran to rank and headline it — reuse that evidence; only query again if the needed data wasn't pulled.
Go one level deeper for root cause: e.g. error detail on the affected page, or live page inspection — genuinely new work beyond the breakdown. A single Investigate may surface more than one cause, and of mixed type (e.g. a UX/form issue plus two priority errors, or a slow page).
Route the what-next by cause type — decide this after diagnosing, never on the button click:
- UX / funnel / market / discount / ops causes → write the what-next inline: one concrete, owner-routed step + the check that confirms resolution.
- Priority error or performance/CWV causes → if tech-diagnosis is available, hand each one off to it (it owns root-cause-to-fix, platform guidance, and the ticket/share flow). Emit one handoff per issue (carrying its own humanId or page), so multiple causes become multiple handoffs the user can pick from:
  - Fixable error → /tech-diagnosis fix #[humanId] on [domain]
  - Vendor/third-party error → /tech-diagnosis share with vendor #[humanId] on [domain]
  - Performance → /tech-diagnosis fix [LCP|INP|CLS] on [page-url] for [domain]
  - If tech-diagnosis is NOT available, don't emit a dead /tech-diagnosis … command. Handle the cause inline: name the issue (humanId / page), give a one-line likely root cause and a concrete next step. Don't mention or recommend installing another skill.
Answer in chat with: the why (the breakdown already gathered, ≤8 rows, plus any deeper root-cause finding) and, per cause, either the inline what-next or the tech-diagnosis handoff. Keep it tight; don't re-render the board.

Full analysis

A broad request, a bare invocation, or "yes" to the quick-answer offer. Run the whole workflow, then render the board in one show_widget:

Broad overview — fire Q1–Q7 for the current window plus the prior-window counterparts for the change-detection metrics (queries.md, Step 1).
Cross-reference + period-over-period — compute current rates and their delta vs the prior window (Step 2).
Pick the top 3 regressions to dig into, using the routing table (Step 3). The only non-regression card is a qualified active error (Q5).
Follow-up queries — run only the follow-ups those signals call for (Step 4).
Render the board — load references/triage-board.md and render it as a single show_widget (title: "checkout_board"): the Overview card (with its 'Save as artifact' button), then the ranked Priority cards (cap 3) with Investigate buttons, then the action bar (Download report + Schedule insights). Use the list_scheduled_tasks result from setup for the Schedule button label.

After the board, add one short closing line so the turn ends with visible text — a show_widget with no following text can be read as "no visible output" and trigger a duplicate re-render. Keep it to a single sentence naming what it is, e.g. "Here's your checkout triage board for [domain] — last 30 days vs. the prior 30." Do not recap the findings, list the priorities, or add a prose scheduling offer; the board and action bar carry those. The same goes for any turn that ends with a show_widget (the schedule card included): follow it with one short line.

Create live dashboard

Triggered by the Overview card's 'Save as artifact' button (arrives as "Save checkout overview as dashboard for [domain]").

Read references/triage-board.md (already in context if full analysis just ran) and references/live-dashboard.md, then follow the instructions in live-dashboard.md to build and save the artifact.

Export PDF

Triggered by the action bar (arrives as "Export checkout analysis as PDF for [domain]").

Export-only — do not re-run the analysis or any queries. Reuse the results already in this conversation. Read references/export-pdf.md and follow the instructions there.

Schedule report

Triggered by the action bar (arrives as "Schedule checkout analysis for [domain]" or "Edit schedule for [domain]").

Read references/schedule-widget.md and render it as a show_widget.

Ignore issue

Triggered by an error card's Ignore link (arrives as "Ignore issue #[humanId] on [domain]").

Call noibu_update_issue to set that issue's status to Ignored/Closed (confirm the exact status value from the tool schema), then confirm in one line (e.g. "Ignored #445 — it won't surface on future runs"). Don't re-render the board. If noibu_update_issue isn't available, say so briefly rather than failing.

Noibu Checkout Performance & Health Analysis