cto-review
$
npx mdskill add alirezarezvani/claude-skills/cto-reviewPressure-tests architecture and engineering scaling decisions with six critical questions
- Evaluates scaling cliffs, tech debt, team scaling, and build-vs-buy tradeoffs
- Uses scripts like tech_debt_analyzer.py and team_scaling_calculator.py
- Analyzes inputs against CTO-level criteria for reliability and cost
- Returns structured feedback via JSON or console output for actionable insights
SKILL.md
.github/skills/cto-reviewView on GitHub ↗
--- name: "cto-review" description: "/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." --- # /cs:cto-review — CTO Forcing Questions **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?** ```bash python ../../../skills/cto-advisor/scripts/tech_debt_analyzer.py ``` ### 3. Team Scaling **For each open req, what's the ramp time and contribution model?** ```bash python ../../../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 ```markdown # 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 - Agent: [`cs-cto-advisor`](../../../../agents/c-level/cs-cto-advisor.md) - Skill: [`cto-advisor`](../../../skills/cto-advisor/SKILL.md) - SLO: `../../../../engineering/slo-architect/` --- **Version:** 1.0.0
More from alirezarezvani/claude-skills
- a11y-auditAccessibility 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.
- ab-test-setupWhen 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.
- ad-creativeWhen 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.
- adversarial-reviewerAdversarial 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.
- aeoAnswer 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.
- agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
- agent-protocolInter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
- agent-workflow-designerDesign production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
- agenthubMulti-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
- agile-product-ownerAgile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.