c-level-advisor/c-level-agents/skills/ciso-review/SKILL.md
/cs:ciso-review <plan> — Risk-paranoid interrogation of any plan that touches data, compliance, or production access.
npx skillsauth add alirezarezvani/claude-skills ciso-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 ../../../skills/ciso-advisor/scripts/risk_quantifier.py
python ../../../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
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
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
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.