google-ads-audit

# Google Ads Audit

Diagnose account health and persist business context for downstream skills (`/google-ads`, `/google-ads-copy`, `/google-ads-landing`). **Read-only** — never mutates the account. The user runs `/google-ads` to execute fixes you recommend.

## Setup

Follow `../shared/preamble.md` (MCP detection, account selection) and `../shared/analysis-principles.md` (evidence requirement, guardrails). Both apply throughout this skill.

## Filesystem contract (must persist)

| Artifact | Path | When |
|---|---|---|
| Business context | `{data_dir}/business-context.json` | First full audit, or refresh when `audit_date` is >90 days old. Skip on scoped audits if file is fresh. |
| Personas | `{data_dir}/personas/{accountId}.json` | Every full audit. |

These are the handoff to every other ads skill — write them even if the report is short. Otherwise `/google-ads-copy` and `/google-ads-landing` operate without business context and produce generic output.

**business-context.json schema:** `business_name, industry, website, services[], locations[], target_audience, brand_voice{tone, words_to_use[], words_to_avoid[]}, differentiators[], competitors[], seasonality{peak_months[], slow_months[], seasonal_hooks[]}, keyword_landscape{high_intent_terms[], competitive_terms[], long_tail_opportunities[]}, social_proof[], offers_or_promotions[], landing_pages{}, unit_economics{aov_usd, profit_margin, source}, notes, audit_date, account_id`.

**personas JSON schema:** `{account_id, saved_at, personas: [{name, demographics, primary_goal, pain_points[], search_terms[], decision_trigger, value}]}`. See `references/persona-discovery.md`.

## Policy freshness check (run first)

Read `../shared/policy-registry.json`. For each entry where `last_verified + stale_after_days < today`:
- **High-volatility** → WebSearch the `area` for recent Google Ads changes; compare to `assumption`. If drift, banner the report and suggest registry update.
- **Moderate-volatility** → one-line "may warrant a check" note.
- **Stable** → skip silently.

## Phase 1 — Pull the audit dataset

Use a single `runScript` call with `ads.gaqlParallel` to fan out the queries an audit needs. The server's `notfair://playbooks/audit-account` resource has a battle-tested baseline; extend it with what your specific question needs.

You decide the exact GAQL shape, but a defensible audit needs to see, at minimum:

- Account-level rollups (`customer`)
- Campaign performance with bidding strategy, network, and impression-share metrics (`campaign`, 90-day cap for impression-share data)
- Ad-group performance (`ad_group`)
- Keyword performance with Quality Score and components (`keyword_view`)
- Search terms (`search_term_view`)
- Negative keywords and shared lists (`campaign_criterion` + shared sets)
- Conversion actions (`conversion_action`) — including counting type, attribution model, primary/secondary
- Network segmentation (`segments.ad_network_type`) when diagnosing CPA/CVR shifts or Search Partners
- RSA assets (`ad_group_ad`)
- Geo targeting (`campaign_criterion` LOCATION + PROXIMITY)
- Recent change events (`change_event`, last 30 days) — for explaining regressions

Aggregate inside the script. Return summarized JSON, not raw rows. The agent narrates; the script does the math.

`getRecommendations` and `summarizeAccountSetup` are useful cross-checks against Google's own and the server's structural views — call them as a separate tool turn after the runScript pass when comparison would sharpen the report.

If a critical query errors out (auth, schema), surface the error and stop — don't fall back to a degraded audit.

**Skip scoring entirely if** `totalSpend == 0` or `activeCampaigns == 0`. Go straight to business context.

## Phase 2 — Scope handling

If the user narrows the audit ("focus on one campaign", "campaign X", "just check waste"):

- Match campaign names by case-insensitive substring. If no match, list available campaigns and ask.
- Filter the in-memory dataset before analysis — no extra API calls.
- Account-level dimensions (conversion tracking, account guardrails) stay account-wide. Note "Scoped to: X" in the report.
- Skip Phase 4 (business context refresh) on scoped audits if `business-context.json` is fresh.

## Phase 3 — Diagnose

The audit's headline output is **three pulse metrics** — Waste ($/mo), Demand captured (%), CPA ($) — each annotated with its top contributor and a pointer to the fix. Read `references/account-health-scoring.md` for the formula, annotation rules, signal-failure overrides, and `audit-history.json` schema. The pulse metric IS the verdict; you don't add a letter grade or 0–5 score on top.

To compute and back the pulse metrics, you'll need to look across these seven areas. They are diagnostic surface area, not graded dimensions:

1. **Signal Quality** *(account-level)* — measurement integrity. If broken, **STOP** here and recommend pausing spend until it's fixed. Pulse metrics are meaningless without measurement (apply the signal-failure override on the Waste line per the reference).
2. **Campaign Structure** — keywords per ad group, brand vs. non-brand separation, channel mixing, naming, budget logic.
3. **Keyword Health** — Quality Score weighted by spend, zombie keywords, match-type discipline.
4. **Search-Term Quality** — wasted spend, brand-leakage, negative coverage, conversion-worthy terms not yet keywords.
5. **Ad Copy & Creative** — RSA coverage, asset variety, sitelink/callout/structured-snippet completeness, PMax asset-group health.
6. **Impression Share** — read rank-lost vs budget-lost together (see the 2×2 matrix in `account-health-scoring.md`); they're different problems with different fixes.
7. **Spend Efficiency** — waste vs. headroom, brand vs. non-brand split, concentration risk.

For Signal Quality and network-mix questions, read `references/conversion-network-audit.md`. It adds the prerequisite checks for conversion-action integrity, Search Partners, Display leakage in Search c