c-level-advisor/c-level-agents/skills/cto-review/SKILL.md
/cs:cto-review <plan> — Architecture and scaling interrogation. Tech debt, scaling cliffs, team scaling, build-vs-buy.
npx skillsauth add alirezarezvani/claude-skills cto-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 ../../../skills/cto-advisor/scripts/tech_debt_analyzer.py
For each open req, what's the ramp time and contribution model?
python ../../../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
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.