TypingMindTypingMindThe best frontend for AI
Cto Review logo

Cto Review

CommunityPopular
alirezarezvani
cto-review

/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.

Overview

Publisheralirezarezvani
Repositoryclaude-skills
Skill namecto-review
Stars
28K
Forks
3.9K
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Cto Review 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.
    https://github.com/alirezarezvani/claude-skills/tree/main/c-level-agents/skills/cto-review
  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-agents/skills/cto-review .claude/skills/cto-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cto Review 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 Review 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 Review 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.

/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 ../../../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?

bash
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

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

Version: 1.0.0

Frequently asked questions

What does the Cto Review AI skill do?

/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.

Why use Cto Review on TypingMind?

Because you install it once and use it with any model. Cto Review 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 Review in TypingMind?

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

Which AI models can use Cto Review?

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 Review?

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

Is the Cto Review 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.

Related AI Skills

View all
openclaw logo

Openclaw Refactor Docs

openclaw
OrganizationPopular

Refactor an existing OpenClaw docs page with source-audited preservation, restructuring, and verification.

82.2K
391K
Instructions
View
openclaw logo

Openclaw Repair Sweep

openclaw
OrganizationPopular

Run scoped OpenClaw issue/PR repair campaigns: coordinate workers, prove root causes, and land or close verified work under the requested authority.

82.2K
391K
1 files
View
openclaw logo

Openclaw Test Performance

openclaw
OrganizationPopular

Benchmark, diagnose, and optimize OpenClaw test and plugin-suite runtime, import hotspots, CPU/RSS, heap growth, and slow coverage paths.

82.2K
391K
1 files
View
openclaw logo

Openclaw Testing

openclaw
OrganizationPopular

Choose proportional OpenClaw tests and checks, diagnose failures, and route environment-sensitive or release proof to its owner.

82.2K
391K
2 files
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇