/cs:cto-review <plan> — Architecture and scaling interrogation. Tech debt, scaling cliffs, team scaling, build-vs-buy. Use when committing to an architecture, planning for 10x load, or weighing a rebuild against a vendor.
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Command: /cs:cto-review <plan>
Pressure-tests architecture and engineering scaling decisions. Six questions to surface the next scaling cliff before you hit it.
Where does the current architecture break, in terms of users / requests / data volume?
What's the top tech debt item, what's it costing per week, and when does it become blocking?
python ../../../c-level-advisor/skills/cto-advisor/scripts/tech_debt_analyzer.py
For each open req, what's the ramp time and contribution model?
python ../../../c-level-advisor/skills/cto-advisor/scripts/team_scaling_calculator.py
Why are we building this instead of buying it — and what's the 3-year TCO of each?
What are the SLOs for this system and what's the current error budget burn?
engineering/slo-architect for SLO design.What does this expose, and has cs-ciso-advisor signed off?
# CTO Review: <plan>
**Date:** YYYY-MM-DD
## Scaling Cliff
- Current capacity: <metric>
- Break point: <metric>
- Headroom: X months at current growth
## Tech Debt
- Top item: <description>
- Cost per week: $X or N eng-hours
- Blocking date estimate: <date>
## Team
- Open reqs: N
- Median ramp: X months
- Contribution model: <pairing / squad / area>
## Build vs Buy
- 3-year build TCO: $X
- 3-year buy TCO: $X
- Strategic fit: <core / context>
- Decision: BUILD | BUY
## Reliability
- SLO defined: yes / no
- Error budget burn: X% (target < Y%)
## Security
- cs-ciso sign-off: ✅ / ❌
## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK
## Next Steps
[3 concrete actions]
/cs:ciso-review — mandatory if data surface changes/cs:cfo-review — for build-vs-buy > $100K/cs:execute — quarterly plan/cs:boardroom — for architecture pivotscs-cto-advisorcto-advisor../../../engineering/slo-architect/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。