c-level-advisor/c-level-agents/skills/cco-review/SKILL.md
/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.
npx skillsauth add alirezarezvani/claude-skills cco-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
Not NRR. Gross. NRR can hide a leaky bucket behind expansion.
retention_decomposition_analyzer.pyIf you can't name it, you don't understand churn.
Long TTV signals different problems by segment.
If "none" — your segmentation is broken.
customer_segmentation_designer.py to surface kill listWrong model wastes capacity.
cs_coverage_calculator.py to size the teamMisalignment is the leading indicator of CS failure.
# 1. Retention decomposition (always start here)
python ../../../skills/chief-customer-officer-advisor/scripts/retention_decomposition_analyzer.py cohorts.json
# 2. Segmentation audit
python ../../../skills/chief-customer-officer-advisor/scripts/customer_segmentation_designer.py customers.json
# 3. Coverage sizing (if making CS team changes)
python ../../../skills/chief-customer-officer-advisor/scripts/cs_coverage_calculator.py book.json
# 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]
/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-review — for CS hires, comp, ladder/cs:decide — log the verdict/cs:freeze 30 — on multi-year CS comp plan changescs-cco-advisorchief-customer-officer-advisor../../../../business-growth/ (tactical CS execution)Version: 1.0.0
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
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development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.