ads-audit

Setup

Read and follow ../shared/preamble.md — it handles MCP detection, token, and account selection. If config is already cached, this is instant.

Google Ads Audit + Business Context Setup

This is the starting point for any Google Ads account. It does two things:

1. Audits the account — surfaces what's working, what's wasting money, and what to fix first
2. Builds business context — gathers and saves business information to {data_dir}/business-context.json so every other ads skill (copy, landing pages, competitive analysis) can use it without re-asking

Run this before anything else. If another ads skill finds business-context.json missing, it should point the user here.

Scope Detection

The user may pass arguments that narrow the audit to specific campaigns, services, or focus areas. Parse the arguments before starting data collection.

How to determine scope

User says	Scope	Behavior
No arguments / "audit my ads"	Full account	Audit all campaigns, all dimensions
"focus on grooming" / "grooming campaigns"	Service-scoped	Filter to campaigns matching the service keyword. Still pull account-level data for context (conversion tracking, account settings), but deep-dive analysis, scoring, and recommendations focus on the matched campaigns only
"campaign X" / specific campaign name	Campaign-scoped	Same as service-scoped but matched to exact campaign(s)
"just check wasted spend" / "impression share"	Dimension-scoped	Full data pull but report only the requested dimension(s) in depth. Scorecard still shows all 7 dimensions for context, but detailed findings and actions focus on the requested area

Scope threading rules

1. The audit call returns all campaigns — you need the full picture anyway to calculate account-wide metrics like total spend (the denominator for waste percentages)
2. Filter findings to scoped campaigns in memory — after the audit returns, filter campaigns[] and per-item findings.* arrays by the scope-matched campaign names/IDs before scoring. No extra API calls required.
3. Score dimensions relative to scope — If scoped to grooming campaigns, keyword health score reflects grooming keywords only, not the whole account. Make this explicit in the report header: "Scoped to: [Grooming campaigns]"
4. Account-wide dimensions still get scored — Conversion tracking and account settings are account-level regardless of scope. Score them normally but note when issues affect the scoped campaigns specifically
5. Persona discovery uses scoped data — Build personas from search terms within the scoped campaigns only
6. Business context is always full-account — business-context.json captures the whole business, not just the scoped segment. Don't narrow business context to the scope

Scope matching

Match campaign names, ad group names, and keyword themes using case-insensitive substring matching. For example, "grooming" matches campaigns named "Tukwila Grooming Search", "Grooming Test", etc. If no campaigns match, tell the user what campaigns exist and ask them to clarify.

Reference Documents

Read these reference documents during analysis for expert-level context:

• references/account-health-scoring.md — Detailed scoring rubrics for each dimension (0-5 scale with specific criteria)
• Read from ads skill: ../ads/references/industry-benchmarks.md — Compare account metrics to industry averages
• Read from ads skill: ../ads/references/quality-score-framework.md — QS diagnostics and component-level analysis
• Read from ads skill: ../ads/references/search-term-analysis-guide.md — Search term relevance scoring methodology
• Read from ads skill: ../ads/references/campaign-structure-guide.md — Account structure best practices

Read these before starting Phase 2 analysis. They contain the numeric thresholds that separate a generic audit from an expert one.

Step 0: Policy Freshness Check

Before auditing, verify that the policy assumptions underpinning this audit are current.

1. Read ../shared/policy-registry.json and check each entry: if last_verified + stale_after_days < today's date, the entry is stale.
2. High-volatility stale entries: Use WebSearch to check for recent Google Ads changes related to each stale entry's area (e.g., "Google Ads broad match behavior changes 2026"). Compare findings against the assumption field. If discrepancies are found:
   • Display a warning banner at the top of the audit: ⚠️ Policy drift detected: [area] — [brief description of what changed]. Recommendations in this area may need manual verification.
   • Suggest updating policy-registry.json with corrected assumptions and today's date.
3. Moderate-volatility stale entries: Note them in the audit output as an informational line (e.g., "ℹ️ [area] last verified [date] — may warrant a check") but do not block the audit.
4. Stable stale entries: Skip — these rarely change.

If no high-volatility entries are stale, proceed directly to Phase 1 with no output from this step.

Phase 1: Pull Account Data

One call does it all. audit runs ~17 queries in parallel server-side and returns pre-computed findings.

Single-call data pull

Call audit(accountId, days=30) (max 90 days, capped by impression-share data limit). The response shape:

{
  account: { name, currency, timezone, autoTagging, trackingTemplate },
  dateRange: { start, end, days },
  summary: { totalSpend, totalConversions, totalConversionValue, totalClicks,
             totalImpressions, cpa, ctr, conversionRate, roas, activeCampaigns },
  pulse: { wasteRate, wasteUsd, demandCaptured, cpa },  // pre-computed metrics
  campaigns: [{                                          // per-campaign detail
    id, name, type, status, spend, conversions, cpa, ctr,
    impressionShare, budgetLostIS, rankLostIS, isMatrix,  // "healthy" | "capital_problem" | "relevance_problem" | "structural_problem"
    biddingStrategy, targetCpa, searchPartners, displayNetwork,
    weightedQS, lowQSSpendPct, negativeKeywordCount,
    adGroups: [...], topAds: [...], topKeywords: [...],
    deviceBreakdown: { MOBILE, DESKTOP, TABLET, ... }
  }],
  findings: {
    wastedKeywords: [...],       // top 10 by spend, 0 conversions, >10 clicks
    wastedSearchTerms: [...],    // top 10 by spend, 0 conversions
    brandLeakage: { detected, variants, totalSpend, terms },
    miningOpportunities: [...],  // top 10 converting search terms (≥2 conv)
    budgetConstrainedWinners: [...],  // good CPA + high budget-lost-IS
    negativeConflicts: [...],    // negatives blocking converters
    hasAudienceSegments, conversionActions, matchTypeDistribution,
    assetCoverage, landingPages
  },
  errors?: [...]                 // individual sub-query failures, non-fatal
}

If the call errors out, surface the error to the user and stop. Don't retry with helper tools — the audit tool is the source of truth for this skill.

After the audit call

1. Apply scope filtering. If the user specified a scope, identify matching campaigns from campaigns[]. Log the matched names+IDs. If no campaigns match, stop and ask the user to clarify. "In-scope campaigns" = scope-matched subset (or all campaigns if no scope).

2. Scope + account-wide totals. summary.* and pulse.* are account-wide and are NOT re-aggregated for scoped audits. The scorecard header already notes "Scoped to: X" — reuse those totals and make the scope explicit, don't recompute. Per-campaign scores (keyword health, ad copy, IS, spend efficiency) DO filter to in-scope campaigns only.

3. Kick off two parallel background calls. Both run in parallel with the rest of Phase 2 scoring — don't block on them:

   • Website crawl — pull ad final URLs from campaigns[].topAds[].finalUrl and start the crawl (Phase 3, Step 2).
   • Geo GAQL — the audit returns geoTargetType (enum) but not the actual location criteria list. Always run this via runGaqlQuery for in-scope campaigns; the Campaign Structure scoring rubric checks for multi-location geo structure and can't score without it:

   SELECT campaign.id, campaign.name,
          campaign_criterion.type, campaign_criterion.negative,
          campaign_criterion.location.geo_target_constant,
          campaign_criterion.proximity.radius,
          campaign_criterion.proximity.radius_units
   FROM campaign_criterion
   WHERE campaign.id IN (<in-scope campaign IDs>)
     AND campaign_criterion.type IN ('LOCATION', 'PROXIMITY')
   

   radius_units: 0 = meters, 1 = kilometers, 2 = miles.

Minimum data for a meaningful audit: summary.activeCampaigns > 0 and summary.totalSpend > 0. Otherwise skip to Phase 3 (business context) — there's nothing to score.

Phase 2: Analyze and Score

Work through each dimension. For each one, assign a numeric score (0-5) and a status label.

Scope-aware scoring: When the audit is scoped, score campaign-level dimensions (structure, keyword health, search terms, ad copy, impression share, spend efficiency) using only in-scope data. Account-level dimensions (conversion tracking) are scored account-wide but with notes about how issues affect the scoped campaigns. The overall health score reflects scoped performance — this gives the user a focused view of the area they care about.

Audit field → scoring dimension map

Most evidence for scoring is pre-computed in the audit response. Use this map before re-deriving anything by hand:

Dimension	Primary audit fields
Conversion tracking	account.autoTagging, findings.conversionActions, summary.totalConversions
Campaign structure	campaigns[].name (brand/non-brand split), campaigns[].adGroups[], findings.matchTypeDistribution, campaigns[].negativeKeywordCount
Keyword health	campaigns[].weightedQS, campaigns[].lowQSSpendPct, findings.wastedKeywords, pulse.wasteRate
Search term quality	findings.wastedSearchTerms, findings.miningOpportunities, findings.brandLeakage, findings.negativeConflicts
Ad copy	campaigns[].topAds[] (headlineCount, descriptionCount, adStrength), findings.assetCoverage
Impression share	campaigns[].impressionShare, campaigns[].budgetLostIS, campaigns[].rankLostIS, campaigns[].isMatrix (already classified)
Spend efficiency	pulse.wasteRate, pulse.wasteUsd, summary.cpa, campaigns[].cpa, findings.budgetConstrainedWinners

The isMatrix enum on each campaign already classifies the IS root cause — "healthy" | "relevance_problem" | "capital_problem" | "structural_problem". Use it directly instead of re-deriving the Impression Share Interpretation Matrix cell.

The pulse.wasteRate and pulse.wasteUsd values use the same formula the report references (keyword waste + search term waste). Don't recompute — report them directly.

Scoring Framework

Read references/account-health-scoring.md for the detailed rubric per dimension. Use this summary for quick reference:

Score definitions:

Score	Label	Meaning
0	Critical	Broken or missing entirely — actively losing money
1	Poor	Major problems — significant waste or missed opportunity
2	Needs Work	Below acceptable — several clear issues to fix
3	Acceptable	Functional but room for meaningful improvement
4	Good	Well-managed with minor optimization opportunities
5	Excellent	Best-practice level — maintain and scale

Overall Health Score: Sum all 7 dimension scores, multiply by (100/35), round to nearest integer. This gives a 0-100 score.

Overall Score	Label	Summary
0-25	Critical	Account has fundamental problems. Stop spending until fixed
26-50	Needs Work	Significant waste. Focus on top 3 issues before scaling
51-75	OK	Functional but leaving money on the table
76-90	Strong	Well-managed. Focus on scaling and marginal gains
91-100	Excellent	Top-tier account. Maintain and test incrementally

Account Health Dimensions

1. Conversion tracking (Score 0-5)

Score	Criteria
0	No conversion actions set up. Spending blind
1	Conversion actions exist but aren't firing (0 conversions recorded despite clicks)
2	Conversions tracked but auto-tagging disabled, or using only micro-conversions (page views, not leads/sales)
3	Primary conversion action firing, auto-tagging on, but multiple conversion actions counting duplicates or no value assigned
4	Clean conversion setup: primary action firing, auto-tagging on, values assigned, no duplicate counting
5	Full setup: primary + secondary actions, proper attribution window, enhanced conversions or offline conversion import

• Red flag: spending money with no conversion tracking = flying blind. Score 0-1 is a STOP condition — recommend pausing spend until tracking is fixed
• Check: is auto-tagging enabled? If not, Google Analytics integration breaks
• Check: are conversion actions using "Every" or "One" counting? Lead gen should use "One", e-commerce should use "Every"

2. Campaign structure (Score 0-5)

Score	Criteria
0	Single campaign with one ad group containing 50+ unrelated keywords
1	Some structure but ad groups have 30+ keywords with mixed intent (e.g., "plumber" and "plumbing school" in same group)
2	Campaigns exist per service/product but ad groups are too broad (15-30 keywords of mixed theme)
3	Campaigns per service, ad groups by theme (5-20 keywords), but missing brand campaign separation or geo structure
4	Clean structure: brand separated, services split, tight ad groups, appropriate geo targeting
5	Optimal: brand/non-brand split, service campaigns, geo-specific where relevant, ad groups of 5-15 tightly themed keywords, negative keyword lists at appropriate levels

• Red flag: one ad group with 200 keywords = poor relevance, QS will suffer
• Check: are brand and non-brand keywords in separate campaigns? Mixing them inflates brand CTR and hides non-brand problems
• Check: for multi-location businesses, is there geo-specific structure?
• Reference ../ads/references/campaign-structure-guide.md for the ideal structure patterns

3. Keyword health (Score 0-5)

Score	Criteria
0	No keywords with conversions. Average QS < 3. >50% of keywords are zombies (0 impressions 30+ days)
1	Average QS 3-4. >30% of spend on non-converting keywords. Heavy use of broad match without negatives
2	Average QS 4-5. 20-30% of spend on non-converting keywords. Some match type issues
3	Average QS 5-6. 10-20% wasted spend. Reasonable match type mix but gaps in negative coverage
4	Average QS 6-7. <10% wasted spend. Good match type strategy. Solid negative keyword lists
5	Average QS 7+. <5% wasted spend. Tight match types. Comprehensive negatives. Regular search term mining

• Calculate: what % of total keyword spend goes to keywords with 0 conversions and >10 clicks? This is your keyword waste rate
• Calculate: average QS weighted by spend (not by keyword count — a QS-3 keyword spending $2,000/month matters more than a QS-3 keyword spending $5/month)
• Check for zombie keywords: 0 impressions for 30+ days. These clutter the account and should be paused

4. Search term quality (Score 0-5)

Score	Criteria
0	>40% of search terms are irrelevant. No negative keywords in place
1	30-40% irrelevant terms. Minimal negative keyword coverage
2	20-30% irrelevant terms. Some negatives but obvious gaps
3	10-20% irrelevant terms. Decent negative coverage. Some converting terms not yet added as keywords
4	<10% irrelevant terms. Good negative lists. Most high-converting terms already added as keywords
5	<5% irrelevant terms. Comprehensive negative lists at account and campaign level. Active search term mining program

• Score search term relevance using the methodology in ../ads/references/search-term-analysis-guide.md
• Calculate: spend on irrelevant search terms (relevance score < 2) as % of total spend
• Flag converting search terms (2+ conversions) not yet added as keywords — these are free money
• Flag obvious negative keyword gaps: competitor names, "free" variants, "jobs"/"careers" variants, wrong service types

5. Ad copy (Score 0-5)

Score	Criteria
0	No active ads, or only legacy expanded text ads (no RSAs)
1	RSAs exist but only 1 per ad group. Headline/description variety is poor (repetitive messaging)
2	1-2 RSAs per ad group. Some variety but headlines don't include keywords or location
3	2+ RSAs per major ad group. Headlines include keywords. Pinning used on H1. Some CTR variation suggests testing is happening
4	2-3 RSAs per ad group with distinct messaging angles. Good headline variety (service, value prop, trust, CTA). CTR above industry average
5	Active A/B testing program. Multiple RSAs with measurably different angles. Regular losers paused, winners iterated. CTR consistently above benchmark

• Count RSAs per ad group: <2 means no testing is possible
• Check headline diversity: are the 15 headlines actually different, or are they minor variations of the same message?
• Check if keywords appear in headlines (direct QS and relevance impact)
• Check pin strategy: H1 should typically be pinned to the most relevant service+location headline
• Identify ad groups with CTR below industry benchmark — these need copy refresh

6. Impression share (Score 0-5) — Data limit: the audit tool caps lookback at 90 days because Google's impression share metrics only support 90 days (not 365).

Score	Criteria
0	Search IS < 20%. Missing >80% of potential traffic
1	Search IS 20-35%. Budget-lost IS > 40% OR rank-lost IS > 60%
2	Search IS 35-50%. Significant losses from both budget and rank
3	Search IS 50-65%. Moderate losses — budget-lost IS < 25% and rank-lost IS < 40%
4	Search IS 65-80%. Losses primarily from rank (fixable with QS improvements)
5	Search IS > 80%. Brand campaign IS > 95%. Losses are marginal and strategic (intentionally not competing on some queries)

Use the Impression Share Interpretation Matrix to diagnose the root cause:

	Rank-Lost IS < 30%	Rank-Lost IS 30-50%	Rank-Lost IS > 50%
Budget-Lost IS < 20%	Healthy — optimize at margins	QS/Bid Problem — improve ads, landing pages, or raise bids on high-QS keywords	Quality Crisis — QS is the bottleneck. Fix ad relevance and landing page experience before spending more
Budget-Lost IS 20-40%	Budget Problem — increase budget or narrow targeting. Check if the campaign is profitable enough to justify more spend	Mixed Problem — fix quality first (cheaper than adding budget), then reassess	Structural Problem — bidding on too-competitive keywords. Shift to long-tail and exact match
Budget-Lost IS > 40%	Severe Budget Gap — if CPA is good, this is the highest-ROI fix in the account. Double budget or cut keyword count by 50%	Priority: fix rank issues to get more from existing budget, then add budget	Fundamental Misalignment — pause, restructure, then restart. Current approach is burning money

7. Spend efficiency (Score 0-5)

Score	Criteria
0	No conversion data available. Flying blind on efficiency
1	CPA > 200% of industry average. >40% of spend on non-converting entities
2	CPA 150-200% of industry avg. 25-40% wasted spend. Major budget misallocation between campaigns
3	CPA 100-150% of industry avg. 15-25% wasted spend. Some misallocation
4	CPA within industry norms. <15% wasted spend. Budget roughly proportional to conversion share per campaign
5	CPA below industry avg. <5% wasted spend. Budget allocation optimized — each campaign's budget share matches its conversion share

• Calculate: % of spend going to converting keywords vs non-converting
• Calculate: per-campaign CPA and compare to account average. Flag any campaign with CPA > 150% of account avg
• Calculate: budget allocation efficiency — does each campaign's % of total budget match its % of total conversions?
• If one campaign gets 60% of budget but delivers only 30% of conversions, that's a $X reallocation opportunity

Wasted Spend Deep Dive

Waste is pre-computed in pulse.wasteUsd and pulse.wasteRate. The formula matches /ads:

• Keyword waste — spend on keywords with conversions = 0 AND clicks > 10 (top 10 in findings.wastedKeywords)
• Search term waste — spend on search terms with conversions = 0 AND clicks ≥ 10 (top 10 in findings.wastedSearchTerms)
• Structural waste — spend on campaigns with Display Network enabled (campaigns[].displayNetwork === true) and poor performance. The audit flags displayNetwork per campaign; cross-check against low conversion rate to call this out.

Report pulse.wasteUsd in the verdict paragraph. Individual wasteful keywords/terms from findings.wastedKeywords and findings.wastedSearchTerms appear as evidence under the relevant dimension (keyword health or search term quality) — max 3 examples per category, not exhaustive lists.

Phase 2.5: Persona Discovery

Discover 2-3 customer personas from the ad data. This runs in parallel with Phase 3 (business context questions) — it uses only the data already pulled in Phase 1.

Data Sources for Persona Construction

All signals come from the audit response already in memory — no extra calls needed.

Source	What it reveals	Audit field
Wasted + mining search terms	Customer language and intent — the terms they use and which convert	findings.wastedSearchTerms, findings.miningOpportunities
Per-campaign top keywords	What the business bids on and what actually gets clicks	campaigns[].topKeywords
Ad group names	How the business segments its services — each theme may serve a different persona	campaigns[].adGroups[].name
Landing page URLs	Where they land — different pages suggest different customer journeys	findings.landingPages, campaigns[].topAds[].finalUrl
Geographic signal	Where they are — metro vs rural	campaigns[].geoTargetType + the supplemental geo GAQL if you ran it
Device split	How they search — mobile-heavy suggests on-the-go/urgent need	campaigns[].deviceBreakdown

Persona Template

Use this full template for the persisted JSON file. In the report output, personas appear as a compact 3-column table (name, example searches, value) — see Phase 4. The JSON file has the full detail for downstream skills like /ads-copy:

Field	Description	Example
Name	Descriptive label capturing their defining trait	"The Emergency Caller"
Demographics	Role, context, location type	Homeowner, suburban, dual-income household
Primary goal	What they're trying to accomplish RIGHT NOW	Fix a burst pipe before it damages the floor
Pain points	What's driving them to search	Can't wait for regular business hours. Worried about cost. Doesn't know who to trust
Search language	Actual search terms from the data that this persona uses	"emergency plumber near me", "plumber open now", "burst pipe repair cost"
Decision trigger	What makes them click the ad and convert	Seeing "24/7" and "Same Day" in the headline. Phone number in the ad. Reviews mentioned
Value to business	Estimated revenue or conversion value	High urgency = willing to pay premium. Avg ticket $350-800

Derivation Rules

• Each persona MUST be grounded in actual search term clusters from the data. If you can't point to 5+ search terms that this persona would use, the persona is speculative — drop it
• If all search terms look the same (single-intent account), identify 1-2 personas max. Don't force 3
• Name personas by their dominant behavior, not demographics: "The Comparison Shopper" is more useful than "Female 35-44"
• Include the actual search terms from the data that map to each persona — this directly informs ad copy decisions

Persist Personas

Save to {data_dir}/personas/{accountId}.json:

{
  "account_id": "1234567890",
  "saved_at": "2024-01-15T10:30:00Z",
  "personas": [
    {
      "name": "The Emergency Caller",
      "demographics": "Homeowner, suburban, any age",
      "primary_goal": "Fix an urgent problem right now",
      "pain_points": ["Can't wait", "Worried about cost", "Doesn't know who's reliable"],
      "search_terms": ["emergency plumber near me", "plumber open now", "burst pipe repair"],
      "decision_trigger": "24/7 availability, phone number visible, reviews",
      "value": "High — willing to pay premium for urgency"
    }
  ]
}

These personas feed directly into /ads-copy for headline generation and /ads for keyword strategy.

Phase 3: Build Business Context

Skip this phase for scoped audits if {data_dir}/business-context.json already exists and has a recent audit_date. A scoped audit (e.g., "focus on grooming") should deliver findings fast, not re-interview the user. Only run Phase 3 on the first full-account audit or if business-context.json is missing/stale (>90 days old).

Pull as much as possible from the data you already have — only ask the user for what you can't infer.

What to infer from account data

All fields below derive from the audit response already collected in Phase 1.

Field	Audit field
business_name	account.name
services	campaigns[].name, campaigns[].adGroups[].name, campaigns[].topKeywords
locations	campaigns[].geoTargetType + supplemental geo GAQL (if you ran it)
brand_voice	campaigns[].topAds[] (headlines/descriptions)
keyword_landscape.high_intent_terms	Converting keywords in campaigns[].topKeywords with conversions > 0
keyword_landscape.competitive_terms	Keywords in campaigns where isMatrix !== "healthy" and rankLostIS > 0.3
keyword_landscape.long_tail_opportunities	findings.miningOpportunities
website	Apex domain from campaigns[].topAds[].finalUrl or findings.landingPages[].url

Website crawl (kicked off right after the audit call, results used here)

The crawl starts in the background immediately after audit returns (see Phase 1, "After the audit call"). By the time you reach Phase 3, results should be ready.

Step 1: Resolve the website URL

Find the website URL from the audit response, in priority order:

1. campaigns[].topAds[].finalUrl — extract the root domain (e.g., https://example.com). Normalize to the apex domain (strip www. and subdomain prefixes) before frequency-counting across all ads. Use the most common domain.
2. Fall back to findings.landingPages[].url (same normalization) if topAds are sparse.
3. If no URL found, ask the user: "What's your website URL?"

Step 2: Crawl the website

Issue all WebFetch calls in a single tool-use turn so they run in parallel. If any individual fetch fails (404, timeout, blocked), skip that page and continue.

Page	URL pattern	Why
Homepage	{root_url}	Services overview, hero messaging, trust signals, brand voice
About page	{root_url}/about	Differentiators, history, team, social proof
Services page	{root_url}/services	Full service list, service descriptions
Top ad landing pages	Up to 3 unique final URLs from ads, excluding any URL that matches the homepage, about, or services pages already being fetched	What the ads actually link to — offers, CTAs, messaging

Fallback if /about or /services return 404: Try one fallback each:

• About: try /about-us (most common variant)
• Services: try /our-services (most common variant)

If the fallback also 404s, move on — don't spider the site.

Detecting unusable pages: If a fetched page has fewer than 50 words of visible text (excluding HTML tags, scripts, and navigation), or if the primary content is a login/auth form (email/password fields, "Sign In" as the main heading), treat it as a failed fetch and skip it for extraction.

Step 3: Extract business context from crawled pages

Scan the fetched page content for these signals. Merge with what you already inferred from account data — website data fills gaps, account data confirms what's active.

Field	What to look for on the website
services	Service names from navigation, headings, service cards. Merge with services inferred from campaigns — the website may list services not yet advertised
differentiators	"Why choose us" sections, hero subheadings, unique value claims (e.g., "Family-owned since 1998", "Same-day service guaranteed")
social_proof	Review counts, star ratings, award badges, "As seen in" logos, certifications, years in business, number of customers served
offers_or_promotions	Banner offers, hero CTAs with discounts, seasonal promotions, "Free estimate" or "X% off"
brand_voice	Tone of headlines and body copy — professional vs casual, technical vs approachable. Capture 3-5 literal phrases from the site that exemplify the tone
target_audience	Who the site speaks to — homeowners vs businesses, specific industries, demographic cues
locations	Footer addresses, "Areas we serve" pages, location-specific content
landing_pages	Map each ad final URL to a summary of what's on that page (headline, primary CTA, offer if any)
industry	What the business clearly does — confirm or refine what campaign names suggest
competitors	Look for comparison tables or "vs" pages linked from the nav

Important: Only extract from pages you actually retrieved with usable content. If the homepage is all you got, that's fine — it usually has the most signal. Extract in the site's original language — downstream skills handle translation when generating English ad copy.

If all pages failed or returned no usable content, skip website extraction entirely and proceed to the full question set below (do not skip any questions).

What to ask the user

Present what you inferred from both account data and the website crawl, then ask for what's still missing.

Always ask (these are rarely on websites):

• "What makes you different from competitors?" → differentiators (ask even if the website had a "why us" section — the owner's answer is often sharper than marketing copy)
• "Who are your main competitors?" → competitors
• "Is your business seasonal? When's your busiest time?" → seasonality

Ask only if not found in account data or website crawl:

• Industry
• Services
• Target audience
• Social proof (reviews, awards, years in business)
• Current offers or promotions

Save the context

Write the complete business context to {data_dir}/business-context.json:

{
  "business_name": "",
  "industry": "",
  "website": "",
  "services": [],
  "locations": [],
  "target_audience": "",
  "brand_voice": {
    "tone": "",
    "words_to_avoid": [],
    "words_to_use": []
  },
  "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": {},
  "notes": "",
  "audit_date": "",
  "account_id": ""
}

Include audit_date (today's date) and account_id so future skills know when this was last refreshed.

Phase 4: Deliver the Audit Report

The report follows an onion structure — lead with the verdict, then actions, then evidence. The reader should get the full picture in the first 10 lines, and only needs to keep reading if they want the supporting data.

The #1 rule: no duplication. Each finding appears in exactly one place. The scorecard summarizes, the actions tell you what to do, the evidence shows why. If something is in the scorecard's "Key Finding" column, don't repeat it in the evidence section.

Report structure

# [Business Name] — Ads Audit
**[Score]/100 · $X,XXX spent (30d) · XX conversions at $XX CPA**
[If scoped] Scoped to: [description]

[2-3 sentence verdict. What's working, what's broken, and the single biggest
opportunity in dollar terms. This paragraph should be enough for someone who
won't read further.]

## What to Fix (in order)

1. **[Specific action]** — [1-line why + expected dollar/conversion impact]
2. **[Specific action]** — [1-line why + expected impact]
3. **[Specific action]** — [1-line why + expected impact]

Run `/ads` to execute any of these.

## Scorecard

| Dimension | Score | Key Finding |
|-----------|-------|-------------|
| Conversion tracking | X/5 | [one line] |
| Campaign structure | X/5 | [one line] |
| Keyword health | X/5 | [one line] |
| Search term quality | X/5 | [one line] |
| Ad copy | X/5 | [one line] |
| Impression share | X/5 | [one line] |
| Spend efficiency | X/5 | [one line] |

## Evidence

[Only include dimensions scoring 0-2. Each dimension gets ONE compact block.
Do NOT repeat what's already in the scorecard or actions — add the supporting
data that explains the score.]

### [Dimension] (X/5)
[2-4 lines of data: the specific keywords, search terms, or metrics that
drove the score. Top 3 examples max — not exhaustive lists. End with the
fix if it wasn't already an action item above.]

## Personas

| Persona | Example searches | Value |
|---------|-----------------|-------|
| [name] | [2-3 terms] | [why they matter] |

## Questions for You

[Only if business context has gaps that matter for the recommendations.
Max 2-3 questions. Don't ask what you can infer from the data.]

What makes a good action item

• Specific: "Pause keyword 'free dog food' — $847 spent, 0 conversions" not "Review underperforming keywords"
• Quantified: Include the spend, impressions, or conversions at stake
• Prioritized: Highest-impact items first. Stopping waste > starting new things
• Actionable with /ads: Every recommendation should be something the user can execute immediately using the /ads skill
• Dollar-denominated: Express impact as "save $X/month" or "gain X conversions/month at $Y CPA"

Output discipline

These rules prevent the bloated, repetitive reports that make audits hard to read:

1. No standalone "Key Numbers" section. The headline already has spend, conversions, and CPA. Don't repeat them.
2. No standalone "Wasted Spend Analysis" section. Waste data belongs in the relevant Evidence dimension (keyword health or search term quality). Mention the total in the verdict paragraph.
3. No standalone "Impression Share Analysis" section. The diagnosis belongs in the scorecard line + Evidence block if IS scored 0-2.
4. Max 3 examples per finding. Show the top 3 by spend, not an exhaustive list. The user can drill deeper with /ads.
5. Evidence sections are for data, not narrative. Don't explain what QS is or how impression share works — just show the numbers that matter.
6. The entire report should fit in ~60-80 lines of markdown. If you're over 100 lines, you're duplicating or over-explaining.

Conditional handoff (pick the single most relevant one)

After the report, add ONE handoff based on the biggest issue found:

Condition	Handoff
Ad copy scored 0-2	Suggest /ads-copy for RSA variants
Impression share scored 0-2	Suggest /ads for bid optimization
3+ converting search terms not yet keywords	Offer to add them via /ads
Wasted spend > 15%	Offer to pause/negative via /ads
High CTR but low conversion rate	Suggest landing page audit

Don't list all possible handoffs — pick the one that matches the #1 action item.

Rules

1. Data first, questions second. Call audit (one tool call) before asking the user anything. Show up informed.
2. Infer before asking. Don't ask "what industry are you in?" if the campaigns clearly say "plumbing services."
3. Be specific. Name the campaigns, keywords, and dollar amounts. Vague advice is useless.
4. Prioritize by money. The biggest waste or biggest opportunity comes first.
5. Save the context. Always write business-context.json — this is the handoff to every other ads skill.
6. Don't fix things here. This skill diagnoses and recommends. The user executes fixes with /ads. Offer to switch to /ads for implementation.
7. Score everything. Every dimension gets a 0-5 score. The overall health score gives the user a single number to track over time. Re-auditing in 30 days should show improvement.
8. Name names. Every finding should reference specific campaigns, keywords, ad groups, or search terms. "Some keywords are underperforming" is not an audit finding — "$423 spent on 'free plumbing advice' with 0 conversions" is.