Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions
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Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions
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</> SKILL.md
cto-advisor.SKILL.md
---name: "cto-advisor"
description: "Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy."
license: MIT
metadata:
version: 2.0.0
author: Alireza Rezvani
category: c-level
domain: cto-leadership
updated: 2026-03-05
python-tools: tech_debt_analyzer.py, team_scaling_calculator.py
frameworks: architecture-decisions, engineering-metrics, technology-evaluation
---# CTO Advisor
Technical leadership frameworks for architecture, engineering teams, technology strategy, and technical decision-making.
## Keywords
CTO, chief technology officer, tech debt, technical debt, architecture, engineering metrics, DORA, team scaling, technology evaluation, build vs buy, cloud migration, platform engineering, AI/ML strategy, system design, incident response, engineering culture
## Quick Start
```bash
python scripts/tech_debt_analyzer.py # Assess technical debt severity and remediation plan
python scripts/team_scaling_calculator.py # Model engineering team growth and cost
Align technology investments with business priorities.
**Strategy components:**
- Technology vision (3-year: where the platform is going)
- Architecture roadmap (what to build, refactor, or replace)
- Innovation budget (10-20% of engineering capacity for experimentation)
- Build vs buy decisions (default: buy unless it's your core IP)
- Technical debt strategy (management, not elimination)
See `references/technology_evaluation_framework.md` for the full evaluation framework.
### 2. Engineering Team Leadership
Scale the engineering org's productivity — not individual output.
**Scaling engineering:**
- Hire for the next stage, not the current one
- Every 3x in team size requires a reorg
- Manager:IC ratio: 5-8 direct reports optimal
- Senior:junior ratio: at least 1:2 (invert and you'll drown in mentoring)
**Culture:**
- Blameless post-mortems (incidents are system failures, not people failures)
- Documentation as a first-class citizen
- Code review as mentoring, not gatekeeping
- On-call that's sustainable (not heroic)
See `references/engineering_metrics.md` for DORA metrics and the engineering health dashboard.
### 3. Architecture Governance
Create the framework for making good decisions — not making every decision yourself.
**Architecture Decision Records (ADRs):**
- Every significant decision gets documented: context, options, decision, consequences
- Decisions are discoverable (not buried in Slack)
- Decisions can be superseded (not permanent)
See `references/architecture_decision_records.md` for ADR templates and the decision review process.
### 4. Vendor & Platform Management
Every vendor is a dependency. Every dependency is a risk.
**Evaluation criteria:** Does it solve a real problem? Can we migrate away? Is the vendor stable? What's the total cost (license + integration + maintenance)?
### 5. Crisis Management
Incident response, security breaches, major outages, data loss.
**Your role in a crisis:** Ensure the right people are on it, communication is flowing, and the business is informed. Post-crisis: blameless retrospective within 48 hours.
Research the technical landscape first. Analyze options against constraints (time, team skill, cost, risk). Then recommend action. Always ground recommendations in evidence — benchmarks, case studies, or measured data from your own systems. "I think" is not enough — show the data.
## Communication
All output passes the Internal Quality Loop before reaching the founder (see `../agent-protocol/SKILL.md`).
