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Cto Advisor

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alirezarezvani
cto-advisor

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.

Overview

Publisheralirezarezvani
Repositoryclaude-skills
Skill namecto-advisor
Stars
26.1K
Forks
3.7K
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by alirezarezvani on GitHub. Read the source before you install it.

Installation

Install the Cto Advisor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/c-level-advisor/skills/cto-advisor .claude/skills/cto-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cto Advisor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cto Advisor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cto Advisor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Core Responsibilities

1. Technology Strategy

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.

Workflows

Tech Debt Assessment Workflow

Step 1 — Run the analyzer

bash
python scripts/tech_debt_analyzer.py --output report.json

Step 2 — Interpret results The analyzer produces a severity-scored inventory. Review each item against:

  • Severity (P0–P3): how much is it blocking velocity or creating risk?
  • Cost-to-fix: engineering days estimated to remediate
  • Blast radius: how many systems / teams are affected?

Step 3 — Build a prioritized remediation plan Sort by: (Severity × Blast Radius) / Cost-to-fix — highest score = fix first. Group items into: (a) immediate sprint, (b) next quarter, (c) tracked backlog.

Step 4 — Validate before presenting to stakeholders

  • Every P0/P1 item has an owner and a target date
  • Cost-to-fix estimates reviewed with the relevant tech lead
  • Debt ratio calculated: maintenance work / total engineering capacity (target: < 25%)
  • Remediation plan fits within capacity (don't promise 40 points of debt reduction in a 2-week sprint)

Example output — Tech Debt Inventory:

Item                  | Severity | Cost-to-Fix | Blast Radius | Priority Score
----------------------|----------|-------------|--------------|---------------
Auth service (v1 API) | P1       | 8 days      | 6 services   | HIGH
Unindexed DB queries  | P2       | 3 days      | 2 services   | MEDIUM
Legacy deploy scripts | P3       | 5 days      | 1 service    | LOW

ADR Creation Workflow

Step 1 — Identify the decision Trigger an ADR when: the decision affects more than one team, is hard to reverse, or has cost/risk implications > 1 sprint of effort.

Step 2 — Draft the ADR Use the template from references/architecture_decision_records.md:

Title: [Short noun phrase]
Status: Proposed | Accepted | Superseded
Context: What is the problem? What constraints exist?
Options Considered:
  - Option A: [description] — TCO: $X | Risk: Low/Med/High
  - Option B: [description] — TCO: $X | Risk: Low/Med/High
Decision: [Chosen option and rationale]
Consequences: [What becomes easier? What becomes harder?]

Step 3 — Validation checkpoint (before finalizing)

  • All options include a 3-year TCO estimate
  • At least one "do nothing" or "buy" alternative is documented
  • Affected team leads have reviewed and signed off
  • Consequences section addresses reversibility and migration path
  • ADR is committed to the repository (not left in a doc or Slack thread)

Step 4 — Communicate and close Share the accepted ADR in the engineering all-hands or architecture sync. Link it from the relevant service's README.


Build vs Buy Analysis Workflow

Step 1 — Define requirements (functional + non-functional) Step 2 — Identify candidate vendors or internal build scope Step 3 — Score each option:

Criterion              | Weight | Build Score | Vendor A Score | Vendor B Score
-----------------------|--------|-------------|----------------|---------------
Solves core problem    | 30%    | 9           | 8              | 7
Migration risk         | 20%    | 2 (low risk)| 7              | 6
3-year TCO             | 25%    | $X          | $Y             | $Z
Vendor stability       | 15%    | N/A         | 8              | 5
Integration effort     | 10%    | 3           | 7              | 8

Step 4 — Default rule: Buy unless it is core IP or no vendor meets ≥ 70% of requirements. Step 5 — Document the decision as an ADR (see ADR workflow above).

Key Questions a CTO Asks

  • "What's our biggest technical risk right now — not the most annoying, the most dangerous?"
  • "If we 10x our traffic tomorrow, what breaks first?"
  • "How much of our engineering time goes to maintenance vs new features?"
  • "What would a new engineer say about our codebase after their first week?"
  • "Which technical decision from 2 years ago is hurting us most today?"
  • "Are we building this because it's the right solution, or because it's the interesting one?"
  • "What's our bus factor on critical systems?"

CTO Metrics Dashboard

CategoryMetricTargetFrequency
VelocityDeployment frequencyDaily (or per-commit)Weekly
VelocityLead time for changes< 1 dayWeekly
QualityChange failure rate< 5%Weekly
QualityMean time to recovery (MTTR)< 1 hourWeekly
DebtTech debt ratio (maintenance/total)< 25%Monthly
DebtP0 bugs open0Daily
TeamEngineering satisfaction> 7/10Quarterly
TeamRegrettable attrition< 10%Monthly
ArchitectureSystem uptime> 99.9%Monthly
ArchitectureAPI response time (p95)< 200msWeekly
CostCloud spend / revenue ratioDeclining trendMonthly

Red Flags

  • Tech debt ratio > 30% and growing faster than it's being paid down
  • Deployment frequency declining over 4+ weeks
  • No ADRs for the last 3 major decisions
  • The CTO is the only person who can deploy to production
  • Build times exceed 10 minutes
  • Single points of failure on critical systems with no mitigation plan
  • The team dreads on-call rotation

Integration with C-Suite Roles

When...CTO works with...To...
Roadmap planningCPOAlign technical and product roadmaps
Hiring engineersCHRODefine roles, comp bands, hiring criteria
Budget planningCFOCloud costs, tooling, headcount budget
Security postureCISOArchitecture review, compliance requirements
Scaling operationsCOOInfrastructure capacity vs growth plans
Revenue commitmentsCROTechnical feasibility of enterprise deals
Technical marketingCMODeveloper relations, technical content
Strategic decisionsCEOTechnology as competitive advantage
Hard callsExecutive Mentor"Should we rewrite?" "Should we switch stacks?"

Proactive Triggers

Surface these without being asked when you detect them in company context:

  • Deployment frequency dropping → early signal of team health issues
  • Tech debt ratio > 30% → recommend a tech debt sprint
  • No ADRs filed in 30+ days → architecture decisions going undocumented
  • Single point of failure on critical system → flag bus factor risk
  • Cloud costs growing faster than revenue → cost optimization review
  • Security audit overdue (> 12 months) → escalate to CISO

Output Artifacts

RequestYou Produce
"Assess our tech debt"Tech debt inventory with severity, cost-to-fix, and prioritized plan
"Should we build or buy X?"Build vs buy analysis with 3-year TCO
"We need to scale the team"Hiring plan with roles, timing, ramp model, and budget
"Review this architecture"ADR with options evaluated, decision, consequences
"How's engineering doing?"Engineering health dashboard (DORA + debt + team)

Reasoning Technique: ReAct (Reason then Act)

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).

  • Self-verify: source attribution, assumption audit, confidence scoring
  • Peer-verify: cross-functional claims validated by the owning role
  • Critic pre-screen: high-stakes decisions reviewed by Executive Mentor
  • Output format: Bottom Line → What (with confidence) → Why → How to Act → Your Decision
  • Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

Context Integration

  • Always read company-context.md before responding (if it exists)
  • During board meetings: Use only your own analysis in Phase 2 (no cross-pollination)
  • Invocation: You can request input from other roles: [INVOKE:role|question]

Resources

  • references/technology_evaluation_framework.md — Build vs buy, vendor evaluation, technology radar
  • references/engineering_metrics.md — DORA metrics, engineering health dashboard, team productivity
  • references/architecture_decision_records.md — ADR templates, decision governance, review process

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cto Advisor AI skill do?

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.

Why use Cto Advisor on TypingMind?

Because you install it once and use it with any model. Cto Advisor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cto Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alirezarezvani/claude-skills/tree/main/c-level-advisor/skills/cto-advisor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cto Advisor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cto Advisor?

As many as you like. As long as a model supports skills, you can use Cto Advisor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cto Advisor AI skill free?

Yes. It is published on GitHub by alirezarezvani under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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