/cs:cdo-review <plan> — Decision-driven Chief Data Officer interrogation of any plan that touches training data, data architecture, data productization, or data team hiring. Use when validating training-data rights before model work, choosing warehouse vs lakehouse vs mesh, or valuing data assets for productization or M&A.
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Command: /cs:cdo-review <plan>
The decision-driven CDO pressure-tests any plan that touches data strategy. Six questions before any commitment to a data architecture, AI training run, data productization, or data team hire.
If no decision is unblocked, why are we collecting / training on / productizing it?
For each data source: origin, consent flow, data class, intended use.
ai_training_data_audit.py if there's any AI use case in scope.Drives the centralize-vs-embed and warehouse-vs-mesh decisions.
If an acquirer asks about this data corpus tomorrow, are we ready?
data_asset_valuator.py quarterly.Tests how much you depend on a specific data source.
Wrong hire (data scientist) when right answer (analytics engineer) is a 12-month productivity loss.
# 1. AI training audit (if any ML / AI use case)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/ai_training_data_audit.py sources.json
# 2. Architecture decision (if changing the stack)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/data_product_strategy_picker.py profile.json
# 3. Data asset valuation (if productizing or pre-M&A)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/data_asset_valuator.py corpus.json
# CDO Review: <plan>
**Date:** YYYY-MM-DD
## The Decision Being Made
[one sentence — which of the four CDO decisions: training | architecture | asset | hire]
## Training Audit (if applicable)
- NO-GO sources: N
- MITIGATE sources: N
- GO sources: N
- Top remediation: <one line>
## Architecture (if applicable)
- Recommended: WAREHOUSE / LAKEHOUSE / MESH
- Build-vs-buy summary: <one line>
- Kill criteria: <when to revisit>
## Asset Value (if applicable)
- Strategic value: X/10 | Moat: STRONG / MEDIUM / WEAK
- M&A multiplier: X.Xx – X.Xx ARR
- Recommended productization path: <name>
## Org (if applicable)
- Next hire: <role>
- Why this, not that: <one line>
- Prerequisite hires in place: yes/no
## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK
## Next Steps
[3 concrete actions]
/cs:gc-review — for any productization or licensing path/cs:ciso-review — for any architecture change touching customer data/cs:cfo-review — for build-vs-buy TCO and M&A valuation mathcs-chro-advisor agent — for data team hires (comp, ladder, leveling)/cs:decide — log the verdict/cs:freeze 90 — on multi-year infrastructure contractscs-cdo-advisorchief-data-officer-advisor../../../c-level-advisor/skills/general-counsel-advisor/ (contractual constraints), ../../../c-level-advisor/skills/cto-advisor/ (architecture capacity)Version: 1.0.0
まだレビューはありません。使ってみた感想をお寄せください。
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。