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)

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

Choose available read capabilities for the requested audit scope. Batch related reads where useful and supported; consult current server guidance for schemas and limits.

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.

Use platform recommendations or account-setup diagnostics as optional cross-checks when available and relevant to the question.

If a read fails, follow actionable recovery guidance. Clearly report missing evidence; continue independent findings only when the available data supports them.

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:

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 + top contributor + fix pointer) → per-area findings (only those that surfaced something material) → Quick Wins section (per the rules in references/account-health-scoring.md). Cap at ~80 lines. Every claim cites a specific entity, number, and window.

End with a single closing line after the handoff to /google-ads:

State where any audit artifacts were actually saved. Do not claim hosted audit history unless a live result confirms it.

Guardrails

1. Read-only skill. Diagnose; don't mutate. Every fix routes through /google-ads (or /google-ads-copy, /google-ads-landing). End the report with one handoff tied to the #1 action.
2. STOP condition. If conversion tracking is broken, recommend pausing spend until it's fixed before recommending anything else.
3. Always persist business-context.json and personas/{accountId}.json even if the report is short — downstream skills depend on them.
4. Name names. Every finding cites specific campaigns, keywords, search terms, and dollar amounts. No generic verdicts.
5. Show the data, not the score. The pulse metrics are the verdict — three numbers with named contributors and pointers to the fix. No letter grades, no 0–5 ratings hiding the reasoning behind a label.