/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.
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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 ../../../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
# 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-revenuecs-chro-advisor agent — 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
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概要と使いどころ
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
日本語の概要は準備中です。原文の説明を表示しています。
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
日本語の概要は準備中です。原文の説明を表示しています。
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
日本語の概要は準備中です。原文の説明を表示しています。
Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
日本語の概要は準備中です。原文の説明を表示しています。