Kai 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.

One-click full marketing audit. Runs all relevant harness checklists and produces a health report.

Non-Negotiable: Kai Data Provenance

Before writing any finding, load harness/references/audit-data-provenance.md.

Every audit must declare one of these modes:

Default to sales_external if access is unclear.

Hard rules:

1. Do not publish numbers without a source. Review counts, ratings, rankings, traffic, conversions, calls, Core Web Vitals, Domain Rating, referring domains, AI Overview visibility, and local pack placement need source, retrieval date, and artifact/API note.
2. Do not score what was not measured. Missing GSC, GA4, GBP, call tracking, backlink, or ad-platform data becomes a data gap, not an invented estimate.
3. Do not turn inference into fact. Hypotheses must be labeled score_eligible: false and kept out of client-facing health scores.
4. Do not cite a tool unless it actually ran. If the report says Ahrefs, DataForSEO, PageSpeed Insights, BuiltWith, Google Places, GSC, GA4, GBP, CallRail, or CRM, include retrieved date and raw artifact path or response summary.
5. Every deck slide with a number needs a source footer. Every audit folder needs _data-sources.md and _data-gaps.md.

Run before handoff:

python scripts/quality_gates/audit_provenance_lint.py workspace/marketing-audit --audit-dir

Phase 0.5: Source-Backed Data Acquisition

If the collector scripts are not available in this install (skills-only or plugin install — no scripts/audit/ in the Kai root), run the audit in qualitative mode: browse the target's public pages directly, cite the URL and retrieval date for every observation, put every quantitative claim you could not measure into _data-gaps.md, and say plainly in the report header that collector-backed metrics require the full harness (github.com/cgallic/kai-cmo-harness). Never estimate a number the collector would have measured.

Before writing the audit, run the source-backed Kai collector. The collector is shared by all Kai workflows, not audit-only; this audit must consume its audit-data.json alias. Existing audit automations may keep using python -m scripts.audit.collect; non-audit Kai workflows should prefer python -m kai.source_data.collect and read kai-data.json.

python -m scripts.audit.collect --url "<url>" --firm-name "<firm_name>" --mode sales_external --workflow audit --out workspace/marketing-audit --pagespeed

Use --mode onboarding_connected only when the client has granted private access. Add optional collectors only when the workflow needs those facts:

python -m scripts.audit.collect --url "<url>" --firm-name "<firm_name>" --mode onboarding_connected --workflow audit --out workspace/marketing-audit --pagespeed --places --dataforseo --seo-provider auto --gsc --ga4 --calls --keywords "<kw1>,<kw2>" --location "<city, state>" --date-from "<YYYY-MM-DD>" --date-to "<YYYY-MM-DD>"

Add --third-party-sources all or a comma list such as serpapi,similarweb,builtwith,wappalyzer,brightlocal,yext,yelp,trustpilot,google-ads,meta-ads,tiktok-ads,linkedin-ads,twilio when the audit needs licensed vendor data. Treat API vendor values as third_party_estimate; treat supplied exports as user_provided.

The collector writes:

• workspace/marketing-audit/kai-data.json
• workspace/marketing-audit/audit-data.json
• workspace/marketing-audit/_data-sources.md
• workspace/marketing-audit/_data-gaps.md
• workspace/marketing-audit/raw/

All findings, health scores, and deck numbers must come from audit-data.json. Do not use numbers discovered conversationally, in snippets, or from model memory. If the metric is not present in audit-data.json, add it as a data gap instead of estimating it. Missing credentials for Places, DataForSEO, SEO platforms, GSC, GA4, call tracking, ads, or CRM must remain data gaps until the collector records a sourced metric.

Phase 0: Load Product Context

Check if MARKETING.md exists in the project root (same directory as CLAUDE.md, README.md, package.json).

If it exists: Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.

If it does NOT exist: Auto-explore the codebase to create it in the project root (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create MARKETING.md using the template from /kai-email-system. Present draft to user for confirmation.

Phase 1: Audit Scope

Read from MARKETING.md. Only ask about things not covered there:

1. Product/site — what are we auditing?

2. URL — main website

3. Active channels — which are you using? (SEO, email, ads, social, content, PR)

4. Known issues — anything already flagged?

5. Depth — quick (top-line scores, 30 min) or deep (detailed findings, 2-3 hours)?

6. Audit mode — sales_external, onboarding_connected, or internal_demo

7. Available data — public-only, Ahrefs/Semrush/DataForSEO, PageSpeed Insights, BuiltWith, Google Places, GSC, GA4, GBP, ads, CRM, CallRail, exports/screenshots

8. Business type — determines which industry-specific module(s) to load:

   Type	Indicators	Examples	Module
   Local Service	Serves geographic area, phone-based leads, local customers	Plumber, cleaner, HVAC, landscaper, electrician, painter, roofer	local-service-business-checklist.md
   Professional Services (B2B)	Credential-based, trust-heavy, long sales cycle, thought leadership	Law firm, accounting firm, consultant, agency, financial advisor, architect	professional-services-b2b-checklist.md
   Multi-Location	2+ physical locations, franchise/chain, centralized brand	Franchise, clinic network, restaurant chain, retail chain, multi-office firm	multi-location-checklist.md
   Restaurant / Food & Bev	Food/drink is the product, dine-in/takeout/delivery	Restaurant, cafe, bar, food truck, bakery, catering, ghost kitchen	restaurant-food-bev-checklist.md
   Healthcare / Medical	Patient-facing, HIPAA-regulated, medical services	Dental, medical clinic, chiropractic, med spa, mental health, veterinary	healthcare-medical-checklist.md
   Creator / Personal Brand	Individual IS the brand, audience-based monetization	Course creator, coach, YouTuber, podcaster, newsletter operator, author	creator-personal-brand-checklist.md
   Real Estate	Property transactions, agent personal brand, portal-dependent	Agent, team, brokerage, property manager	real-estate-checklist.md
   SaaS / Digital Product	Software product, online-first, subscription revenue	(default — existing harness modules cover this)	No additional module

   A business can match multiple types (e.g., a multi-location dental practice triggers both Multi-Location AND Healthcare). Load all applicable modules.

Phase 2: Checklist Execution

Run applicable checklists from knowledge/checklists/. Skip checklists for channels the user isn't using.

Audit Modules

Load each applicable checklist and evaluate. Use browse/gstack to view the live site if available.

For every check, record:

claim: ""
source_tier: connected | public_observed | user_provided | inferred | missing_data
source_name: ""
source_url: ""
retrieved_at: ""
confidence: high | medium | low
evidence_artifact: ""
score_eligible: true | false

Only connected, public_observed, and user_provided findings can affect health scores. inferred and missing_data findings are scope notes unless the user explicitly asks for internal hypotheses.

Phase 3: Health Scores

Score each module 0-100:

Grading: A (90+), B (75-89), C (60-74), D (40-59), F (<40)

Phase 4: Prioritized Fix List

Aggregate all findings across modules into one prioritized list:

P0: Fix This Week (high impact, low effort)

P1: Fix This Month (high impact, medium effort)

P2: Fix This Quarter (medium impact)

P3: Backlog (nice to have)

Phase 5: Recommendations

Map fixes to /kai skills:

Phase 6: Output

workspace/marketing-audit/
├── _data-sources.md             # Source inventory: tier, access, retrieved_at, artifact
├── _data-gaps.md                # Missing data and how sales/onboarding handles it
├── _executive-summary.md        # Health scores + top 5 fixes
├── _detailed-findings.md        # Module-by-module results
├── _prioritized-fixes.md        # Full fix list
├── _skill-recommendations.md    # Which /kai skills to run
└── per-module/
    ├── technical-seo.md
    ├── content.md
    ├── email.md
    ├── ads.md
    ├── social.md
    ├── landing-pages.md
    ├── cro.md
    ├── local-service-business.md
    ├── professional-services-b2b.md
    ├── multi-location.md
    ├── restaurant-food-bev.md
    ├── healthcare-medical.md
    ├── creator-personal-brand.md
    └── real-estate.md