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Kai Monthly Audit

Community
cgallic
kai-monthly-audit

Monthly marketing audit and executive review. Pulls the last 30 days of source-backed marketing, analytics, SEO, CRO, content, paid media, lifecycle, reputation, and pipeline data; compares it to the previous period; summarizes strategic learning; and produces an executive report plus next-month plan. Use when "monthly audit", "monthly marketing review", "monthly report", "executive marketing review", "board-ready marketing report", "month-end audit", or any request for a 30-day marketing audit.

Overview

Publishercgallic
Repositorykai-cmo-harness
Skill namekai-monthly-audit
Stars
61
Forks
8
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cgallic on GitHub. Read the source before you install it.

Installation

Install the Kai Monthly Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-monthly-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cgallic/kai-cmo-harness.git /tmp/kai-cmo-harness
mkdir -p .claude/skills
cp -r /tmp/kai-cmo-harness/harness/skills-v2/kai-monthly-audit .claude/skills/kai-monthly-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kai Monthly Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kai Monthly Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kai Monthly Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/kai-monthly-audit — A Month of Signal Turned Into Decisions

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.

Objective

An executive review of the last 30 days where every number resolves to a source, every channel gets a keep/change/stop decision, and the month ends with a prioritized next-month plan that names owners and skills. Weekly signals become a narrative; the narrative becomes budget and channel decisions. An unsourced finding is not a finding — a scorecard with a guessed number is worse than one with a gray cell.

Done when

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

  • E3 — a named human approved the exact delivered file, hash-pinned, and every quantitative claim in it resolves to a collector source inside the month's audit folder.
  • C4 — the Kai Data Provenance Rule is satisfied end to end: collector run before writing, data mode declared, every number carrying source plus retrieval date plus artifact path, banned_word_check clean, and audit_provenance_lint passing on the folder.
  • O1 — every P0 recommendation in the next-month plan names the metric it targets, its baseline, its threshold, and its owner. An audit whose top recommendation has no metric is not finished. Adoption is read at 60 days.

Constraints

Data provenance is the non-negotiable. Load harness/references/audit-data-provenance.md before writing any finding.

  • Declare the data mode: sales_external for prospect and 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, labeled as such.
  • Never 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. They never become estimates.
  • 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.

Collector before writing, connected collectors only when access is confirmed, provenance lint before handoff:

bash
python -m scripts.audit.collect --url "<url>" --mode <mode> --workflow kai-monthly-audit --out workspace/audits/monthly/<YYYY-MM> --pagespeed
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 --revenue --date-from "<YYYY-MM-DD>" --date-to "<YYYY-MM-DD>"
python scripts/quality_gates/audit_provenance_lint.py workspace/audits/monthly/<YYYY-MM> --audit-dir

Other binding rules:

  • Score only source-eligible findings. Mark unsupported areas gray and list the data gap next to them.
  • Separate facts from hypotheses. A hypothesis may become an experiment; it may not become a client-facing finding.
  • Budget guidance rules: 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 · never recommend a budget increase from inferred data.
  • Phone Capture Fit Rule. Evaluate phone-based lead capture when the business appears phone-led. Recommend a phone answering service or AI receptionist only on source-backed fit signals (missed calls, after-hours, speed-to-lead, qualification, routing, call logging), and compare alternatives. Do not lead with it when phone demand is low or the workflow is self-serve by design.
  • The audit recommends. It does not change budgets, pause campaigns, or mutate any live channel.

Know these before collecting (read MARKETING.md and the latest weekly audit folders first; ask only for what blocks a sourced review): the target URL · the data mode · which sources are actually connected · current monthly budget · the primary KPI. Also establish business stage, active channels, target conversion events, the offer, budget posture, and known constraints.

Revenue lens

Load the Revenue Lens section of harness/references/audit-data-provenance.md with the provenance rule.

  • Open with the funnel, not activity. Traffic source → intent → lead → follow-up → booked/paid, one row per stage, before any channel or technical finding. Sessions and lead counts are inputs, never the headline.
  • Rank gaps by the dollars each stage leaks. High-intent traffic with no lead path, leads with no follow-up, and follow-up with no booking outrank traffic volume.
  • Technical setup is one line. Schema, llms.txt, AI crawler access and sitemap get a sourced pass/fail; they become findings only when a measured check fails.
  • Unverified means check. Any claim or funnel stage not verified in this run is labeled check with what would verify it, is not scored, and is mirrored in _data-gaps.md.

Context

NeedLoad
Provenance rule, modes, collector contractharness/references/audit-data-provenance.md
ECO floor and craft note for auditsharness/eco-floors.yaml
Prior-period comparisonLatest folders under workspace/audits/
Business stage, channels, offer, constraintsMARKETING.md (project root)
Phone-led conversion diagnosisknowledge/checklists/cro-audit-checklist.md
Client-ready deck version/kai-html-presentation

Audit modules — run the ones the month's activity justifies:

ModuleTrigger
/kai-auditAlways, for full marketing health.
/kai-seo-auditWebsite, organic search, local visibility, AEO, or technical SEO.
/kai-croLanding pages, checkout, booking, demos, forms, or phone-led conversion.
/kai-daily-ad-review summaryPaid media active this month.
/content-report and /content-retroContent published this month, or aging content due for review.
/kai-analyticsTracking gaps, attribution conflicts, missing KPI definitions.
/kai-growth-planStrategic uncertainty, budget allocation, or stage mismatch.

Executive scorecard — every row scored /100 with a trend, sourced evidence, and a decision:

AreaScoreTrendEvidenceDecision
Demand/100up/down/flatsourcedkeep/change/stop
Conversion/100up/down/flatsourcedkeep/change/stop
Search and AEO/100up/down/flatsourcedkeep/change/stop
Paid media/100up/down/flatsourcedkeep/change/stop
Lifecycle/100up/down/flatsourcedkeep/change/stop
Reputation and calls/100up/down/flatsourcedkeep/change/stop
Measurement/100up/down/flatsourcedkeep/change/stop

Strategic learning answers seven questions: what improved · what got worse · what repeated across weekly audits · what surprised us · what to stop · what to double down on · what data we still cannot trust.

Next-month plan is a P0/P1/P2 table with columns: Priority · Action · Owner · Skill · Due · Source.

Output — workspace/audits/monthly/<YYYY-MM>/ holds _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, and html-presentation/index.html. Monthly decks read like an executive review: fewer slides, clearer decisions, every number sourced.

Escalate when

  • Connected-source access is claimed but the collector cannot retrieve it — do not fall back to public data while presenting it as connected.
  • The month's most important channel has no measurable data and the scorecard would be mostly gray.
  • Attribution across sources conflicts materially and no single source can be named authoritative.
  • The obvious recommendation requires spend or a channel change the user has not authorized.
  • Weekly audits and this month's collector data disagree on the same metric.
  • The business is phone-led but call data is unavailable, so conversion cannot be honestly scored.

Frequently asked questions

What does the Kai Monthly Audit AI skill do?

Monthly marketing audit and executive review. Pulls the last 30 days of source-backed marketing, analytics, SEO, CRO, content, paid media, lifecycle, reputation, and pipeline data; compares it to the previous period; summarizes strategic learning; and produces an executive report plus next-month plan. Use when "monthly audit", "monthly marketing review", "monthly report", "executive marketing review", "board-ready marketing report", "month-end audit", or any request for a 30-day marketing audit.

Why use Kai Monthly Audit on TypingMind?

Because you install it once and use it with any model. Kai Monthly Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kai Monthly Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-monthly-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kai Monthly Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kai Monthly Audit?

As many as you like. As long as a model supports skills, you can use Kai Monthly Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kai Monthly Audit AI skill free?

It is published on GitHub by cgallic. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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