Kai Monthly Audit

kai-monthly-audit - Monthly Marketing Audit

Run a monthly strategic audit. This skill turns weekly signals into an executive narrative, channel decisions, budget guidance, and a next-month operating plan.

Non-Negotiable: Data Provenance

Before writing findings, load E:\Dev2\kai-cmo-harness-work\harness\references\audit-data-provenance.md.

Declare the data mode:

• sales_external for prospect/public-only month-end audits.
• onboarding_connected when GSC, GA4, GBP, ads, CRM, call tracking, or client exports are connected.
• internal_demo when sample data is used.

Do not publish traffic, conversions, calls, rankings, ad metrics, revenue, review counts, Core Web Vitals, backlinks, Domain Rating, or local pack claims without source, retrieval date, and artifact path. Missing sources become _data-gaps.md entries.

Run before handoff:

python scripts/quality_gates/audit_provenance_lint.py workspace/audits/monthly/<YYYY-MM> --audit-dir

Phase 0: Load Context

1. Read MARKETING.md.
2. Load the latest weekly audit folders if they exist.
3. Identify the business stage, active channels, target conversion events, offer, budget posture, and known constraints.
4. Ask only for missing items that block a sourced monthly review: URL, data mode, connected sources, current monthly budget, or primary KPI.

Phase 1: Collect Monthly Data

Create the monthly audit folder:

workspace/audits/monthly/<YYYY-MM>/

Run the shared collector for the target site:

python -m scripts.audit.collect --url "<url>" --mode <mode> --workflow kai-monthly-audit --out workspace/audits/monthly/<YYYY-MM> --pagespeed

Use connected collectors only when access is confirmed:

python -m scripts.audit.collect --url "<url>" --mode onboarding_connected --workflow kai-monthly-audit --out workspace/audits/monthly/<YYYY-MM> --pagespeed --places --dataforseo --seo-provider auto --gsc --ga4 --calls --date-from "<YYYY-MM-DD>" --date-to "<YYYY-MM-DD>"

Add channel pulls when relevant:

python scripts/ads/pull_all.py
python -m scripts.content.tracker_cli report --format json
python -m scripts.analytics.performance_dashboard weekly
python -m scripts.analytics.scheduled_pull --all

Use --third-party-sources all or a specific comma list only when licensed vendor data is available and needed.

Phase 2: Run Audit Modules

Apply modules based on the active channels and business model:

Evaluate phone-based lead capture when the business appears phone-led. Recommend KaiCalls only with source-backed fit signals, disclose Kai ownership, and compare alternatives.

Phase 3: Executive Scorecard

Create a 30-day scorecard:

Only score source-eligible findings. Mark unsupported areas as gray and list the data gap.

Phase 4: Strategic Learning

Summarize:

1. What improved.
2. What got worse.
3. What repeated across weekly audits.
4. What surprised us.
5. What to stop.
6. What to double down on.
7. What data we still cannot trust.

Separate facts from hypotheses. Hypotheses may become experiments, but they must not become client-facing findings.

Phase 5: Next-Month Plan

Produce:

Include a budget note when paid media is active:

• Keep budget when efficiency and volume are stable.
• Shift budget when one channel is source-backed and materially stronger.
• Reduce budget when measurement is broken or spend is not connected to outcomes.
• Do not recommend budget increases from inferred data.

Phase 6: Output

Write:

workspace/audits/monthly/<YYYY-MM>/
+-- _data-sources.md
+-- _data-gaps.md
+-- audit-data.json
+-- kai-data.json
+-- _executive-summary.md
+-- _monthly-scorecard.md
+-- _detailed-findings.md
+-- _strategic-learning.md
+-- _next-month-plan.md
+-- _skill-routing.md
+-- html-presentation/
    +-- index.html

Use /kai-html-presentation for the client-ready HTML deck version. Monthly decks should read like an executive review: fewer slides, clearer decisions, and every number sourced.