| name | kai-daily-ad-review |
| description | Daily ad performance check-in across platforms. Pulls live metrics from Meta, Google, and LinkedIn via deterministic scripts, compares against benchmarks and previous period, flags overspend/underperformers/policy issues, and outputs a quick daily summary with action items. Use when "daily ad review", "how are my ads doing today", "ad check-in", "morning ad report", "daily ad summary", "check ad performance", "ad dashboard", "daily ads", or any request for a recurring or quick-glance ad performance review. |
Daily ad performance check-in. Pull live data via scripts, compare against benchmarks, flag problems, output a summary with action items.
This is NOT the same as /kai-ad-campaign (which creates/evaluates campaigns end-to-end). This is a fast daily pulse check — meant to run every morning or on-demand.
Phase 0: Pull Ad Data
Run the unified pull script. It auto-detects which platforms have credentials and pulls everything.
python scripts/ads/pull_all.py
This writes structured JSON to workspace/ads/pulls/YYYY-MM-DD/:
meta.json — Full Meta/Facebook/Instagram data (if META_ACCESS_TOKEN configured)google.json — Full Google Ads data (if GOOGLE_ADS_* configured)linkedin.json — Full LinkedIn Ads data (if LINKEDINADS_* configured)summary.json — Cross-platform totals
To pull a specific platform only: python scripts/ads/pull_all.py --platforms meta
What the scripts pull (comprehensive)
Meta (scripts/ads/meta.py pull):
- Account insights: today, yesterday, 7d, 14d, 28d
- Campaign insights: 7d aggregate + 14d daily breakdown
- Ad set insights: 7d aggregate + 14d daily + targeting spec with LAL/audience classification
- Ad insights: 7d with creative details
- Breakdowns: age/gender, platform/position, device
- Fields include: impressions, reach, frequency, spend, clicks, unique_clicks, ctr, unique_ctr, cpc, cpm, actions, cost_per_action_type, conversions, quality_ranking, engagement_rate_ranking, conversion_rate_ranking, video_p25/50/75/100, video_thruplay
- Mutations:
meta.py pause/activate/budget/create-campaign/create-adset/create-ad/upload-image/upload-video/duplicate-adset (dry-run by default, --execute to apply)
Google (scripts/ads/google.py pull):
- Campaign performance: 30d daily breakdown
- Ad group performance: 14d daily
- Ad performance: 14d aggregate with RSA headline/description details
- Search terms report: top 100 by spend (7d)
- Audience segments: campaign-level
- Keyword quality scores: quality_score, creative_quality, post_click_quality, predicted_ctr
- Mutations:
google.py pause/activate/budget/add-negative (dry-run by default, --execute to apply)
LinkedIn (scripts/ads/linkedin.py pull):
- Campaign group and campaign insights: 7d, 14d daily, 28d
- Creative-level insights: 7d
- Audience breakdowns: company size, industry, job function, seniority, country
- Audience classification from targeting criteria
- Mutations:
linkedin.py pause/activate/budget (dry-run by default, --execute to apply)
If a platform isn't configured
The script will skip it and log which env vars are missing. At minimum, Meta should be configured:
META_ACCESS_TOKEN=<long-lived token>
META_AD_ACCOUNT_ID=<numeric, without act_ prefix>
Phase 1: Read and Analyze Pull Data
Read the JSON files from today's pull:
import json
from pathlib import Path
date = "YYYY-MM-DD"
pull_dir = Path(f"workspace/ads/pulls/{date}")
> **Kai root note:** `knowledge/`, `harness/`, and `scripts/` paths in this skill live in the Kai install, not the user's project. Resolve them against the first ancestor directory of this SKILL.md that contains a `knowledge/` folder (the Kai plugin root, `~/.claude/kai`, or the kai-cmo-harness repo). `MARKETING.md`, `memory/`, and any output files live in the current project. If a referenced `scripts/` command is not available in this install, say so, skip it, and continue with the file-based guidance — never fabricate its output.
for f in pull_dir.glob("*.json"):
with open(f) as fh:
data = json.load(fh)
# Analyze...
Key metrics to extract per platform
From Meta (meta.json):
account_insights.last_7d → spend, impressions, reach, frequency, ctr, cpccampaigns[].insights_7d → per-campaign performanceadsets[].audience_type → LAL vs custom vs interest vs advantage+ vs broadadsets[].insights_daily → day-by-day trends for fatigue detectionads[].insights_7d → per-ad creative performanceads[].insights_7d.quality_ranking → Meta's diagnostic: ABOVE_AVERAGE_35, AVERAGE, BELOW_AVERAGE_35breakdowns.age_gender → demographic performancebreakdowns.platform_position → FB Feed vs IG Reels vs Stories etc.
From Google (google.json):
campaigns[].insights_daily → day-by-day CPC/CPA trendssearch_terms → wasted spend on irrelevant querieskeyword_quality → quality score distribution (flag anything < 5)audience_segments → which audiences are converting
From LinkedIn (linkedin.json):
campaigns[].insights_7d → engagement rate (higher baseline than Meta/Google)breakdowns.industry + breakdowns.job_function → who's engagingcampaigns[].audience_type → matched audience vs professional targeting
Phase 2: Cross-Reference with PostHog
Load: harness/references/posthog-marketing-queries.md
If PostHog is connected, pull:
- Today's ad traffic — pageviews with UTM breakdown (query #2)
- Conversion events — campaign attribution (query #8)
- Landing page bounce — for pages receiving ad traffic
This connects ad spend to actual on-site behavior.
Phase 3: Benchmark Comparison
Performance Benchmarks
| Metric | Poor | OK | Good | Great |
|---|
| CTR | < 0.5% | 0.5-1% | 1-2% | > 2% |
| CPC | > $5 | $3-5 | $1.50-3 | < $1.50 |
| CPL | > $50 | $30-50 | $15-30 | < $15 |
| ROAS | < 1x | 1-2x | 2-4x | > 4x |
| Frequency | > 4.0 | 3.0-4.0 | 1.5-3.0 | 1.0-1.5 |
Adjust benchmarks to the vertical (from MARKETING.md if available).
Trend Detection
Compare today vs 7-day average:
- Spend: flag if today's pace > 120% of daily average (overspend)
- CTR: flag if today < 70% of 7-day avg (creative fatigue)
- CPC: flag if today > 130% of 7-day avg (competition spike or audience saturation)
- Conversions: flag if today < 50% of daily avg (broken funnel or tracking issue)
- Frequency: flag if > 3.0 (audience seeing ads too often — fatigue incoming)
- Quality ranking: flag if BELOW_AVERAGE_35 on any diagnostic dimension
Audience Performance (from LAL tagging)
Compare ad set performance by audience_type:
- LAL audiences should have lower CPL than broad
- Custom retarget audiences should have highest CTR
- If Advantage+ is outperforming manual targeting, note it
- If LAL is underperforming interest targeting, flag for investigation
Phase 4: Issue Detection
Flag these automatically:
| Issue | Trigger | Severity |
|---|
| Overspend | Daily spend pace > 120% of budget | HIGH |
| Zero impressions | Active ad with 0 impressions today | HIGH |
| CTR crash | CTR < 50% of 7-day avg | HIGH |
| CPC spike | CPC > 150% of 7-day avg | MEDIUM |
| No conversions | Spend > $50 today with 0 conversions | MEDIUM |
| Creative fatigue | CTR declining 3+ consecutive days | MEDIUM |
| Frequency overload | Frequency > 3.5 on any ad set | MEDIUM |
| Quality warning | Any ad with BELOW_AVERAGE quality ranking | MEDIUM |
| Budget underspend | < 50% of daily budget used by midday | LOW |
| Learning phase | Ad set in learning phase > 7 days | LOW |
| LAL underperform | LAL CPL > broad/interest CPL | LOW |
| Wasted search spend | Google search term with > $20 spend, 0 conversions | MEDIUM |
Phase 5: Daily Summary Output
Output format — keep it scannable:
# Daily Ad Review — [Date]
## Snapshot
| Metric | Today | 7-Day Avg | Trend |
|--------|-------|-----------|-------|
| Spend | $X | $X/day | up/down/flat |
| Impressions | X | X/day | |
| Reach | X | X/day | |
| Frequency | X.X | X.X | |
| Clicks | X | X/day | |
| CTR | X% | X% | |
| CPC | $X | $X | |
| Conversions | X | X/day | |
| CPL | $X | $X | |
## Flags
- [HIGH] Overspend: Campaign "X" pacing 140% of daily budget
- [MEDIUM] Frequency: Ad set "Y" at 3.8 — rotate creative or expand audience
- [MEDIUM] Quality: Ad "Z" has BELOW_AVERAGE engagement ranking
## Audience Performance
| Audience Type | Ad Sets | Spend | Leads | CPL | CTR |
|---------------|---------|-------|-------|-----|-----|
| Lookalike 1% | 2 | $X | X | $X | X% |
| Custom Retarget | 1 | $X | X | $X | X% |
| Interest | 1 | $X | X | $X | X% |
| Advantage+ | 1 | $X | X | $X | X% |
## Campaign Breakdown
| Campaign | Spend | Clicks | CTR | CPC | Conversions | CPL | Status |
|----------|-------|--------|-----|-----|-------------|-----|--------|
## Top Performers
1. [Ad name] — [metric that makes it stand out]
2. [Ad name] — [metric]
## Underperformers
1. [Ad name] — [what's wrong] — **Action:** [pause/adjust/replace creative]
2. [Ad name] — [what's wrong] — **Action:** [specific fix]
## Quality Diagnostics
| Ad | Quality | Engagement | Conversion |
|----|---------|------------|------------|
(only show non-ABOVE_AVERAGE entries)
## Action Items
- [ ] [Specific action with campaign/ad name]
- [ ] [Specific action]
- [ ] [Specific action]
Write output to workspace/ads/daily-reviews/[YYYY-MM-DD]-daily-review.md.
Phase 6: Historical Tracking
If previous daily reviews exist in workspace/ads/daily-reviews/, compare:
- Week-over-week spend trend
- Which flags are recurring (persistent issues need
/kai-ad-campaign evaluation)
- Which action items from yesterday were addressed
If previous pull data exists in workspace/ads/pulls/, diff today's metrics against yesterday's for precise trend detection.
If the same flag appears 3+ days in a row, escalate: recommend running /kai-ad-campaign in evaluation mode for a deeper audit.
Scheduling
This skill is designed to run daily. Recommend the user set up a schedule:
/schedule to create a recurring morning trigger- Or run manually with
/kai-daily-ad-review
