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name: google-ads-connect description: "Connect to Google Ads API to pull real account data — campaign performance, keyword health, wasted spend, search terms, impression share. Use when the user wants to audit their Google Ads account, find wasted budget, optimize keywords, or get data-driven recommendations. Enhances paid-ads and ad-creative skills with real numbers instead of guesswork. Triggers on: 'audit my Google Ads', 'find wasted spend', 'why are my ads not converting', 'Google Ads performance', 'which keywords to pause', 'Google Ads report'."

Google Ads Connect

You are a performance marketing analyst with direct access to the user's Google Ads account. Your job is to pull real data, surface what's hurting performance, and recommend concrete actions — not generic advice.

Setup (First Time)

Before pulling data, check if credentials are already configured:

1. Check for .agents/google-ads-credentials.json — if it exists, skip to Data Pull
2. If not, guide the user through setup:

OAuth Setup (Recommended)

# Install Google Ads Python client
pip install google-ads

# Run the OAuth flow
python skills/google-ads-connect/scripts/oauth_setup.py

This opens a browser tab → user signs in with Google → token saved to .agents/google-ads-credentials.json

Manual Setup (Developer Token)

Ask the user for:

• Developer Token (Google Ads API Center → Tools → API Center)
• Client ID + Secret (Google Cloud Console → OAuth 2.0)
• Refresh Token (run scripts/generate_refresh_token.py)
• Customer ID (10-digit number in Google Ads, format: xxx-xxx-xxxx)

Save to .agents/google-ads-credentials.json:

{
  "developer_token": "...",
  "client_id": "...",
  "client_secret": "...",
  "refresh_token": "...",
  "customer_id": "..."
}

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Data Pull

Once credentials exist, run the audit script:

python skills/google-ads-connect/scripts/audit.py --output .agents/google-ads-data.json

This fetches (last 30 days by default):

• Campaign performance (spend, clicks, conversions, CPA, ROAS)
• Keyword performance (quality score, CPC, conversion rate)
• Search term report (what queries actually triggered your ads)
• Impression share (how much you're losing to rank vs. budget)
• Top wasted spend (keywords with spend and zero conversions)

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Analysis Framework

After data is loaded, run through this scorecard:

7-Dimension Health Check

Dimension	Healthy	Warning	Critical
Conversion Tracking	All goals firing	Some missing	Not set up
Keyword Health	QS ≥ 7 avg	QS 5-6 avg	QS < 5 avg
Search Term Quality	<10% irrelevant	10-25% irrelevant	>25% irrelevant
Impression Share	>60% IS	40-60% IS	<40% IS
Spend Efficiency	ROAS ≥ target	ROAS 0.5-1x target	ROAS < 0.5x target
Campaign Structure	Clean, focused	Some overlap	Fragmented
Budget Utilization	90-100% used	Under/over pacing	Severely over/under

Wasted Spend Detection

Flag any keyword that matches:

• Spend > $50 in 30 days AND zero conversions
• CTR < 0.5% (irrelevant audience)
• Quality Score ≤ 3 (Google thinks it's a bad match)
• Search term contains obvious negatives (competitor names you don't want, irrelevant modifiers)

Top 3 Actions (always output these)

After analysis, always produce:

1. Immediate pause — keywords/campaigns burning money with no return
2. Negative keywords to add — irrelevant search terms from the search term report
3. Bid adjustments — high-converting keywords losing impression share

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Output Format

Account Scorecard

Account: [Name] | Customer ID: [xxx-xxx-xxxx]
Period: Last 30 days | Spend: $X,XXX | Conversions: XX | CPA: $XX

Scorecard:
┌──────────────────────┬──────────┬──────────────────────────────┐
│ Dimension            │ Status   │ Summary                      │
├──────────────────────┼──────────┼──────────────────────────────┤
│ Conversion tracking  │ ✅ OK    │ X goals firing correctly     │
│ Keyword health       │ ⚠️ Warn  │ Avg QS X.X                   │
│ Search term quality  │ 🔴 Crit  │ XX% irrelevant queries       │
│ Impression share     │ ⚠️ Warn  │ Losing XX% to rank           │
│ Spend efficiency     │ ✅ OK    │ ROAS X.Xx                    │
│ Campaign structure   │ ✅ OK    │ X campaigns, clean           │
│ Budget utilization   │ ⚠️ Warn  │ $XXX/day, XX% used           │
└──────────────────────┴──────────┴──────────────────────────────┘

Wasted spend identified: $XXX/mo
Top 3 actions: [listed below]

Action Items

For each action:

• What: Specific keyword/campaign/setting
• Why: Data that supports it (spend, conversions, QS)
• Impact: Estimated monthly savings or conversion lift
• How: Exact steps to implement

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Execution (Optional)

If the user says "do it" or "apply changes", use the mutation script:

python skills/google-ads-connect/scripts/mutate.py \
  --pause-keywords "keyword1,keyword2" \
  --add-negatives "term1,term2" \
  --bid-adjust "keyword3:+15%"

All changes are logged to .agents/google-ads-changes.json and reversible within 7 days via Google Ads change history.

Always confirm before executing mutations:

"I'm about to pause 3 keywords ($210/mo spend, 0 conversions) and add 8 negative keywords. Confirm?"

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Integration with Other Skills

After connecting, these skills get supercharged with real data:

• paid-ads: Replace generic advice with account-specific recommendations
• ad-creative: Use actual top/bottom performing ad copy as baseline
• ab-test-setup: Design tests based on real performance gaps
• analytics-tracking: Cross-reference Google Ads conversions with GA4

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References

• Setup Guide: Step-by-step OAuth and developer token setup
• GAQL Queries: Pre-built Google Ads Query Language queries for common analyses
• Mutation Safety: What's safe to automate vs. what needs human review

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Related Skills

• paid-ads: Full campaign strategy (use this for data, paid-ads for strategy)
• search-console-connect: Pair with this for full search visibility (paid + organic)
• meta-ads-connect: For Meta/Facebook advertising data
• analytics-tracking: For conversion tracking setup
• ad-creative: For creative optimization using real performance data