/cs:cto-review — CTO Forcing Questions

SkillAI & models

Once added, your AI can pressure-test an architecture plan with six CTO-style questions on scaling, tech debt, team growth, and build-versus-buy. It is meant for the moments when you are about to commit to an architecture, planning for a big jump in load, or weighing a rebuild against buying from a vendor.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

After adding it, share the architecture plan you want examined and ask for a cto-review. It fits best when you are committing to a design, planning for a big jump in load, or deciding between a rebuild and a vendor.

Then ask your AI: use the /cs:cto-review skill

What your AI can do with it

  • Ask six CTO-style questions about an architecture plan
  • Surface scaling risks before you commit to a design
  • Highlight tech debt a plan might be carrying
  • Test whether a plan holds up under ten times the load
  • Weigh rebuilding against buying from a vendor
  • Consider how the plan holds up as the team grows

What this skill tells your AI

The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/cto-review/SKILL.md and read by ahel’s review.

Command: /cs:cto-review <plan>

Pressure-tests architecture and engineering scaling decisions. Six questions to surface the next scaling cliff before you hit it.

When to Run

  • Before approving a major architecture change
  • Before doubling the engineering team
  • Before a build-vs-buy decision > $100K/year
  • When a system is showing reliability stress (SLOs missed)
  • Before committing to a new platform / language / DB

The Six CTO Questions

1. Scaling Cliff

Where does the current architecture break, in terms of users / requests / data volume?

  • Be specific. "It breaks at 10× current load because the primary DB writes saturate."
  • If you don't know, run a load test before deciding.

2. Tech Debt Inventory

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

3. Team Scaling

For each open req, what's the ramp time and contribution model?

python ../../../c-level-advisor/skills/cto-advisor/scripts/team_scaling_calculator.py

4. Build vs Buy

Why are we building this instead of buying it — and what's the 3-year TCO of each?

  • If "we want control" or "it's not that hard" — push back.
  • If the answer is "this is our core moat," build.

5. SLO / Reliability

What are the SLOs for this system and what's the current error budget burn?

  • Without an SLO, you can't reason about reliability tradeoffs.
  • See engineering/slo-architect for SLO design.

6. Security & Compliance Surface

What does this expose, and has cs-ciso-advisor signed off?

  • Architecture decisions are compliance decisions.
  • Loop in cs-ciso-advisor before commit.

Workflow

  1. Run the tech debt analyzer + team scaling calculator
  2. Define the scaling-cliff hypothesis explicitly
  3. Cross-check with cs-ciso-advisor for security implications
  4. Apply the verdict

Output Format

# 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]

Routing

  • /cs:ciso-review — mandatory if data surface changes
  • /cs:cfo-review — for build-vs-buy > $100K
  • /cs:execute — quarterly plan
  • /cs:boardroom — for architecture pivots

Related


Version: 1.0.0

Signals

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Item type
skill
Key
cto-review
Source
github.com/alirezarezvani/claude-skills