Kai Funnel Audit

Objective

A sourced diagnosis of where the awareness-to-lead flow breaks, in two layers — awareness (do the collected hooks, messaging, and proof earn attention?) and lead capture (do the collected opt-ins convert that attention into leads?) — ending in a provenance-linted, prioritized fix list where each fix is routed to the skill that owns it. Every finding traces to a collected artifact. A surface with no collected artifact is out of scope, not audited from memory.

Scope boundary vs /kai-cro: /kai-cro runs the 5-layer conversion stack (technical, traffic, offer, design, copy) on ONE page or flow, in depth. This skill audits the FULL awareness-to-lead path across surfaces — ads, organic posts, entry pages, opt-ins, lead magnets, phone path — and finds where the flow breaks between them. When a single page needs deep conversion work, flag it and hand off to /kai-cro; never re-run the 5-layer stack here. /kai-audit and /kai-seo-audit are wider still; hand off there when the problem is not the funnel.

Done when

Work type audit-report — floor E3/C4/O1 (harness/eco-floors.yaml, also_covers: funnel-audit).

• E3 — a named human approved the exact delivered folder, and every quantitative claim resolves to a row in _data-sources.md backed by an artifact in workspace/funnel-audit/data/.
• C4 — the Kai Data Provenance Rule holds end to end: mode declared, collector run before writing, every number cited, gaps written to _data-gaps.md. audit_provenance_lint.py passes, and the copy-bearing outputs pass Four U's and banned words.
• O1 — every P0 fix names the metric it targets and the mechanism it moves. No invented uplift percentages.

Constraints

• MARKETING.md first. Read it from the project root before asking discovery questions. If it does not exist, build it from the codebase (CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, email/ad/analytics config) using the template carried in /kai-email-system, and confirm the draft. Do not ask the user what the product is.
• Provenance is blocking. Load harness/references/audit-data-provenance.md before any finding is written. Declare a mode — sales_external (public data only; default when no private access is confirmed), onboarding_connected (client granted GSC/GA4/ad/CRM/call-tracking access), or internal_demo (labeled sample data only) — in _executive-summary.md and every deliverable header. Run the collector before writing:

  python -m scripts.audit.collect --url <url> --mode <mode> --workflow funnel-audit --out workspace/funnel-audit/data
  

  Add opt-in collectors per the provenance doc when credentials exist (--pagespeed, --gsc, --ga4, --calls, ...). Read metrics from data/kai-data.json (audit-data.json is the identical alias for the lint). A metric absent from collector output or a user-provided artifact is unavailable — it goes in _data-gaps.md, never into a finding.
• Collected artifacts only. funnel-map.md holds one row per surface — awareness layer (ads with Ads Library URLs, organic posts, blog/SEO entries, homepage) and capture layer (opt-in forms, lead magnets, booking flows, phone path) — with entry→capture edges. A surface with no crawl, archive, user-provided export, or screenshot is OUT OF SCOPE: list it in _data-gaps.md with what would be needed. Never audit from memory of what pages "usually" look like.
• Ledgers. _data-sources.md carries source, tier, retrieved-at, used-for, and artifact path per the provenance doc's table. Every downstream finding cites a row in it.
• Hooks are scored, not replaced here. Score with the /kai-hook-bench rubric — clarity, specificity, curiosity, proof-backing, 0-2 each, total /8 — without restating its definitions. Run every hook against the anti-patterns in memory/what-doesnt-work.md and the voice-pattern regexes carried in /kai-gate; a hook matching a known loser pattern is an automatic finding. Replacement hooks route to /kai-hook-bench.
• Unverifiable proof on the client's own page is a P0 finding, not something to keep quietly — it is a compliance risk per harness/references/advertising-compliance.md. Proof must be attributable, plausible, and non-contradicting (page review count vs Places data from the collector). Missing proof assets hand off to /kai-proof-builder or /kai-case-study.
• No invented uplift. "Expected mechanism" names the variable a fix moves — a Value Equation variable, a Hook–Retain–Reward stage, or a scent/leak repair. Never a predicted lift percentage or unsourced benchmark. A fix worth testing is written as an A/B hypothesis using the template in knowledge/playbooks/funnel-hack-offer-architecture.md: primary metric plus guardrail metric, no predicted lift.
• The Value Equation table is an internal scoring rubric. Label it as such; it never ships as a quantitative claim.
• Friction numbers are sourced or gapped. Field counts, steps, load times, mobile behavior, and post-submit delivery delay come from crawl artifacts, PageSpeed runs, or GA4/form analytics under onboarding_connected. No assumed field counts, no "typically 3 steps".
• KaiCalls Fit Rule applies exactly as AGENTS.md defines it. Phone-led evidence comes from collected data only (prominent phone numbers or call CTAs in crawled pages, call extensions in observed ads, local/service vertical, phone-handled booking). If phone-led, evaluating phone-based lead capture is REQUIRED. Fit signals — missed-call pain, after-hours gaps, slow speed-to-lead, no qualification/routing, no call logging — come from public observation or a logged public call test in sales_external, or CallRail/CRM/phone logs via the collector in onboarding_connected. No signal data is a data gap, not an assumed problem. Recommending KaiCalls (kaicalls.com) requires real cited fit signals, disclosure that KaiCalls is Kai-owned, and comparison of at least two alternatives (human answering service, callback widget, native call tracking/routing). It is never the primary recommendation when phone demand is low, call recording/consent compliance is unresolved, the workflow is self-serve by design, or source data is missing. The older Layer-6 wording in /kai-cro predates the Fit Rule — the AGENTS.md rule wins.
• Gates before handoff. On copy-bearing outputs (magnet-rewrite.md, and any before/after copy inside awareness-fixes.md):

  python scripts/quality_gates/four_us_score.py --file workspace/funnel-audit/magnet-rewrite.md    # 10/16 — hook/offer-asset threshold
  python scripts/quality_gates/banned_word_check.py --file workspace/funnel-audit/magnet-rewrite.md
  

  Max 2 retry cycles, fixing only the named failing dimension rather than rewriting the file (see memory/lessons.md). After 2 failures, escalate to a human with the diagnosis and log it via python scripts/self_improvement/lesson_capture.py add. Then the blocking provenance gate:

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

  A failure means an unsourced number or a missing ledger file — fix the citation or move the claim to _data-gaps.md. Never delete the ledger requirement.
• Approval doctrine. This audit is a recommendation package. Nothing here publishes, edits a live page, changes an ad, or dials a phone system. Every implementation goes through the owning skill's human-approval gate.

Context

What each layer checks. Awareness: does the headline state the Dream Outcome rather than the feature; does ad→page scent hold (same promise, persona, offer); is the persona obvious in one viewport; does the asset reward the click (a hook opening a question the page never closes is a finding); is proof above the fold in the collected viewport; are there competing CTAs, nav links exiting before first capture, ad traffic on a generic homepage, dead ends, or slow entry pages. An awareness layer that only pitches is itself a finding — Give-Give-Give-Ask. Capture: what each opt-in offers, what it asks (fields, steps), where it sits, its Value Index (Dream Outcome × Perceived Likelihood) ÷ (Time Delay × Effort & Sacrifice) scored 1-10 per variable, and whether each magnet is narrow and specific, a painkiller rather than a vitamin, one of the three types (problem diagnosis / sample-trial / symptom revelation), named per [Number] + [Adjective] + [Target Audience] + [Desired Outcome] + [Timeframe], and good enough to charge for.

The weakest magnet gets rewritten through the four Value Equation application questions: make the outcome vivid, raise belief it works for them (attaching only proof already inventoried), collapse Time Delay toward an immediate first win, strip Effort & Sacrifice (fewer fields, instant delivery, done-for-you over course format), and rename it with the naming formula. This is a spec plus copy draft, not a shipped asset. If the underlying offer is the problem rather than the packaging, stop and hand off to /kai-offer-builder.

Output — workspace/funnel-audit/: _data-sources.md (source, tier, retrieved-at, used-for, artifact — required by lint) · _data-gaps.md (out-of-scope surfaces, missing metrics, absent call data — required by lint) · data/ (collector output: kai-data.json + audit-data.json, raw/) · funnel-map.md (surfaces + edges, collected artifacts only) · awareness-scores.md (per-asset hook rubric scores with one-phrase justifications) · awareness-fixes.md (P0/P1/P2 rows — Fix | Evidence (artifact path/URL + retrieved-at) | Effort Low/Med/High | Expected mechanism) · lead-capture-scores.md (Value Equation table + lead-magnet checks) · friction-findings.md (fields/steps/load per capture point, sourced or gapped) · magnet-rewrite.md (weakest magnet before/after spec + re-score) · phone-path.md (phone-led verdict, fit signals with sources, disclosed recommendation) · _gate-report.md (gate results + retries) · _executive-summary.md (mode label on top, funnel map recap, top 5 fixes, limiting gaps, routing table).

Routing — append to _executive-summary.md:

30-day follow-up on shipped fixes runs through the standard content pipeline; underperformers get diagnosed via /kai-retro.

Escalate when

• No surface has a collected artifact — there is no audit to run, only a data-access request.
• The client asks for findings on surfaces that could not be collected, or wants a number the collector did not return.
• Phone-led signals exist but call data access does not, and the user wants a phone recommendation anyway.
• Proof on a live client page appears unverifiable or contradicts collected data — that is a compliance call, not an editorial one.
• Two gate retries failed on the same dimension.
• The diagnosis points outside the funnel (traffic, brand, product, pricing) and the wider skill has not been authorized.