/cs:ciso-review <plan> — Risk-paranoid interrogation of any plan that touches data, compliance, or production access. Use when launching features that handle customer data, before a SOC 2 / ISO audit, or after any incident or near-miss.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Command: /cs:ciso-review <plan>
The risk-paranoid threat-modeler. Six questions before any production change that touches customer data or compliance scope.
What's the STRIDE threat model for this system, and which threat is most likely?
If this is fully compromised, what data is exposed and how many users are affected?
What signals indicate compromise, and how long until they're triggered (MTTD)?
Is there an IR runbook for this scenario, and has it been tabletop-tested?
What's the regulator notification window if this scenario occurs?
Which third-party vendors are in scope, and what's their security posture?
python ../../../c-level-advisor/skills/ciso-advisor/scripts/risk_quantifier.py
python ../../../c-level-advisor/skills/ciso-advisor/scripts/compliance_tracker.py
# CISO Review: <plan>
**Date:** YYYY-MM-DD
## Threat Model
- Top threat: <STRIDE category> — <description>
- Likelihood: H/M/L | Impact: H/M/L
- ALE: $X / year
## Blast Radius
- Data exposed (worst case): <description>
- Users affected: N
- Estimated cost: $X
## Detection
- MTTD target: X hours
- Current MTTD: X hours
- Detection rule: <name>
## Response
- IR runbook: ✅ / ❌
- Last tabletop: <date>
## Regulatory
- Frameworks in scope: SOC 2 / ISO 27001 / HIPAA / GDPR
- Notification window: X hours/days
## Vendors
- New vendors added: N
- DPAs signed: N / N
- Security reviews complete: N / N
## Verdict
🟢 SHIP | 🟡 MITIGATE THEN SHIP | 🔴 BLOCK
/cs:cto-review — architecture alignment/cs:gc-review — DPA, regulatory implications/cs:decide — log risk acceptance/cs:boardroom — for CRITICAL riskscs-ciso-advisorciso-advisor../../../ra-qm-team/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。