kai-monthly-audit - Monthly Marketing Audit

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.

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 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Do not publish traffic, conversions, calls, rankings, ad metrics, revenue, review counts, Core Web Vitals, backlinks, Domain Rating, or local pack claims without source,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:

Module	Trigger
/kai-audit	Always for full marketing health.
/kai-seo-audit	Website, organic search, local visibility, AEO, or technical SEO.
/kai-cro	Landing pages, checkout, booking, demos, forms, or phone-led conversion.
/kai-daily-ad-review summary	Paid media active this month.
/content-report and /content-retro	Published content this month or aging content due for review.
/kai-analytics	Tracking gaps, attribution conflicts, or missing KPI definitions.
/kai-growth-plan	Strategic uncertainty, budget allocation, or stage mismatch.

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:

Area	Score	Trend	Evidence	Decision
Demand	/100	up/down/flat	sourced	keep/change/stop
Conversion	/100	up/down/flat	sourced	keep/change/stop
Search and AEO	/100	up/down/flat	sourced	keep/change/stop
Paid media	/100	up/down/flat	sourced	keep/change/stop
Lifecycle	/100	up/down/flat	sourced	keep/change/stop
Reputation and calls	/100	up/down/flat	sourced	keep/change/stop
Measurement	/100	up/down/flat	sourced	keep/change/stop

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:

Priority	Action	Owner	Skill	Due	Source
P0
P1
P2

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.