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Cco Review

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alirezarezvani
cco-review

/cs:cco-review <plan> — Retention-obsessed Chief Customer Officer interrogation of any plan that touches customer retention, segmentation, CS team sizing, or CS team hiring. Use when gross retention is slipping, before approving CSM headcount, or when deciding which customer segments to keep or fire.

Overview

Publisheralirezarezvani
Repositoryclaude-skills
Skill namecco-review
Stars
28K
Forks
3.9K
Bundled files
Instructions only
LicenseMIT
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 alirezarezvani on GitHub. Read the source before you install it.

Installation

Install the Cco Review 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/alirezarezvani/claude-skills/tree/main/c-level-agents/skills/cco-review
  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/alirezarezvani/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/c-level-agents/skills/cco-review .claude/skills/cco-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cco Review 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 Cco Review 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 Cco Review 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.

/cs:cco-review — CCO Forcing Questions

Command: /cs:cco-review <plan>

The retention-obsessed CCO pressure-tests any plan that touches customer experience. Six questions before any retention claim, segmentation change, CS team expansion, or major CS hire.

When to Run

  • Before any board narrative that includes a retention number
  • Before approving a CS team headcount expansion
  • Before re-segmenting the customer base or changing tier definitions
  • Before launching a customer marketing or advocacy program
  • Before a major CS hire (CSM, AM, Implementation, Customer Marketing)
  • When NRR is "great" but churn complaints from CSMs are increasing
  • Before deciding whether to add an AM role separate from CSM

The Six CCO Questions

1. What's the GROSS retention rate?

Not NRR. Gross. NRR can hide a leaky bucket behind expansion.

  • GRR healthy ≥ 90% at growth stage, ≥ 95% at scale
  • If GRR < 85% but NRR > 100%, the product is failing for 15%+ of customers; expansion is masking the failure
  • Run retention_decomposition_analyzer.py

2. What's the #1 reason customers leave?

If you can't name it, you don't understand churn.

  • 7-category taxonomy: product_fit / competitor_loss / no_value_realized / pricing / champion_left / company_event / tactical_failure
  • Preventable churn = product_fit + no_value_realized + tactical_failure
  • If preventable > 50%, CS has clear leverage; if < 30%, churn is structural (ICP, market, competition)

3. What's the median time-to-value (TTV) by segment?

Long TTV signals different problems by segment.

  • Long TTV in low tier = ICP misfit; downgrade or kill
  • Long TTV in high tier = onboarding broken; fix the Implementation Manager handoff
  • TTV is a leading indicator of GRR

4. Which customer would you fire today?

If "none" — your segmentation is broken.

  • Some accounts cost more than they earn (support cost > 50% of ARR + low ICP fit)
  • Run customer_segmentation_designer.py to surface kill list
  • The 3 paths for kill candidates: non-renewal / downgrade-to-tech-touch / raise-price-to-cost-recover

5. What's the ARR-per-CSM ratio, and is the model pooled or named?

Wrong model wastes capacity.

  • Strategic: named + exec sponsor, $300K-$1M ARR/CSM
  • Enterprise: named, $500K-$2M
  • Mid-market: pooled, $2M-$5M
  • SMB: tech-touch, $5M+
  • Run cs_coverage_calculator.py to size the team

6. Is CS in your comp plan, and how is it different from Sales comp?

Misalignment is the leading indicator of CS failure.

  • CS comp: 70/30 base/variable typical
  • Variable: 50% gross retention + 30% net retention + 20% activity
  • Anti-pattern: comp CSMs on NPS — they game it
  • Anti-pattern: comp CSMs same as Sales — they sell instead of serve

Workflow

bash
# 1. Retention decomposition (always start here)
python ../../../c-level-advisor/skills/chief-customer-officer-advisor/scripts/retention_decomposition_analyzer.py cohorts.json

# 2. Segmentation audit
python ../../../c-level-advisor/skills/chief-customer-officer-advisor/scripts/customer_segmentation_designer.py customers.json

# 3. Coverage sizing (if making CS team changes)
python ../../../c-level-advisor/skills/chief-customer-officer-advisor/scripts/cs_coverage_calculator.py book.json

Output Format

markdown
# CCO Review: <plan>
**Date:** YYYY-MM-DD

## The Decision Being Made
[one sentence — retention | segmentation | coverage | next hire]

## Retention (if applicable)
- GRR: X% (vs vanity NRR of Y%)
- Top churn driver: <category> at X% of churn
- Preventable churn: X% (CS-controllable)
- Leaky-bucket pattern? yes/no

## Segmentation (if applicable)
- Tier distribution: Strategic X / Enterprise X / Mid-market X / SMB X
- Kill list size: N customers (X% of customers, Y% of ARR)
- Upgrade candidates: N

## Coverage (if applicable)
- Current CSMs: N | Required now: M | Required 12mo: P
- Annual cost (12mo): $X
- Manager trigger fired: yes/no

## Org (if applicable)
- Next hire: <CSM | Support | AM | IM | CS Ops | Customer Marketing>
- Why this, not the alternative: <one line>
- Customer outcome unblocked: <specific>

## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK

## Next Steps
[3 concrete actions]

Routing

  • /cs:cpo-review — if churn root cause is product_fit or no_value_realized
  • /cs:cro-review — if expansion math or comp alignment is in question
  • /cs:cfo-review — for CS cost commitments and retention-impact-on-revenue
  • cs-chro-advisor agent — for CS hires, comp, ladder
  • /cs:decide — log the verdict
  • /cs:freeze 30 — on multi-year CS comp plan changes

Related


Version: 1.0.0

Frequently asked questions

What does the Cco Review AI skill do?

/cs:cco-review <plan> — Retention-obsessed Chief Customer Officer interrogation of any plan that touches customer retention, segmentation, CS team sizing, or CS team hiring. Use when gross retention is slipping, before approving CSM headcount, or when deciding which customer segments to keep or fire.

Why use Cco Review on TypingMind?

Because you install it once and use it with any model. Cco Review 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 Cco Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alirezarezvani/claude-skills/tree/main/c-level-agents/skills/cco-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cco Review?

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 Cco Review?

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

Is the Cco Review AI skill free?

Yes. It is published on GitHub by alirezarezvani under the MIT license. 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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