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 campaigns, and regression decomposition.

Per-area findings only show up in the report when the area surfaced something material. Cite specific entities, dollars, and time windows. "Some keywords are underperforming" is not a finding; "Campaign X has $1,840 in last-30-day spend on 12 keywords with 0 conversions and QS ≤ 4" is.

For unit-economics-aware framing: if business-context.json.unit_economics.aov_usd and profit_margin exist, frame waste and headroom in dollars saved / captured per month, not "above account average". See ../shared/ppc-math.md.

Phase 4 — Business context

Derive what you can from data already pulled:

| Field | Source | |---|---| | business_name | customer.descriptive_name | | services | Campaign + ad-group names, top converting keywords | | locations | campaign_criterion LOCATION + PROXIMITY | | brand_voice | Top-performing RSA headlines / descriptions | | keyword_landscape.high_intent_terms | Converting keywords with strong CVR | | keyword_landscape.competitive_terms | Keywords in campaigns with high rank-lost-IS | | keyword_landscape.long_tail_opportunities | Converting search terms not yet promoted to keywords | | website | Apex domain from ad final URLs |

Then crawl the website (homepage + about + services + top 3 ad landing pages, parallel WebFetch) and merge into the schema. See references/business-context.md.

Ask the user — it's faster than guessing — for: differentiators, competitors, seasonality, unit economics (AOV, margin). Ask for everything else only if the data + crawl can't answer it.

Phase 5 — Personas

Discover 2–3 personas from search terms, top keywords, ad-group themes, landing pages, geo, and device split — all from the dataset already in memory. Persist to {data_dir}/personas/{accountId}.json. Each persona must be grounded in 5+ actual search terms; if not, drop it. See references/persona-discovery.md.

Phase 6 — Report

Structure: pulse metrics (3 lines, each with number