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Thermo Nuclear Code Quality Review

Community
AnastasiyaW
thermo-nuclear-code-quality-review

Run an opt-in strict maintainability review for giant files, spaghetti growth, misplaced logic, weak boundaries, unnecessary abstractions, and missed structural simplifications. Use for a thermonuclear review, harsh code-quality audit, code-judo review, or a file approaching 1000 lines. Do not use as an automatic rewrite mandate or for cosmetic cleanup.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill namethermo-nuclear-code-quality-review
Stars
212
Forks
32
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Thermo Nuclear Code Quality 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/thermo-nuclear-code-quality-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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/development/thermo-nuclear-code-quality-review .claude/skills/thermo-nuclear-code-quality-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thermo Nuclear Code Quality 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 Thermo Nuclear Code Quality 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 Thermo Nuclear Code Quality 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.

Strict Code Quality Review

This is an unusually demanding review mode, not an instruction to rewrite code by taste. Inspect the current diff and surrounding architecture, then report only high-conviction findings grounded in a contract, reachable behavior, or a material maintainability risk.

Review Bar

Ask:

  • Can a simpler ownership boundary remove whole branches or concepts?
  • Did the diff add ad-hoc flags, special cases, or feature logic to a shared path instead of using the canonical layer?
  • Did a cohesive module become more coupled or cross a meaningful shape limit?
  • Are wrappers, casts, optionality, and sequential orchestration earning their complexity?
  • Does a proposed decomposition reduce what a reader must hold in mind, rather than merely move the same complexity into more files?

A file crossing roughly 1000 lines is a strong signal to inspect decomposition, not an automatic failure. Apply the project's actual shape policy and explain why a split would improve ownership. Do not manufacture a rewrite when the current structure is coherent and the change is small.

Output

Report findings in this order:

  1. structural regressions;
  2. missing simplifications with a concrete alternative;
  3. spaghetti or branching growth;
  4. boundary and type-contract problems;
  5. file shape and decomposition;
  6. lower-severity readability issues.

For each finding include file/line, contract or evidence, user/operator impact, and a concrete remedy. Separate BLOCKER, ADVISORY, and ACCEPTED decisions. If no finding survives reachability, materiality, and evidence screening, say so.

Independence and scope

Use a fresh reviewer for non-trivial changes. This review does not replace focused tests, security proof, or proof-verify; it evaluates maintainability and architecture. It must not silently edit production code or turn an advisory concern into a rewrite.

Gotchas

  • Ambition without a contract becomes over-engineering. Keep the selection gate: a sufficient implementation is a reason to stop expanding.
  • A line count is a probe, not a universal design law.
  • Moving code across files without reducing coupling is not a successful decomposition.
  • Passing tests do not erase a material architecture regression, but a style preference without impact is not a blocker.

Troubleshooting

SymptomLikely causeAction
Review proposes a rewrite for a tiny diffScope or trigger was too broadLimit the review to changed behavior and material risks
Many low-value nitsNo severity/materiality screenKeep only findings with evidence and an actionable remedy
File is large but coherentThreshold treated as a verdictRecord ACCEPTED with rationale and keep the stable boundary
Suggested split adds more indirectionComplexity moved, not removedReject it and compare the reader's concepts before/after

Source

Adapted from Cursor Team Kit's MIT-licensed thermo-nuclear-code-quality-review: https://github.com/cursor/plugins/tree/main/cursor-team-kit/skills/thermo-nuclear-code-quality-review

Frequently asked questions

What does the Thermo Nuclear Code Quality Review AI skill do?

Run an opt-in strict maintainability review for giant files, spaghetti growth, misplaced logic, weak boundaries, unnecessary abstractions, and missed structural simplifications. Use for a thermonuclear review, harsh code-quality audit, code-judo review, or a file approaching 1000 lines. Do not use as an automatic rewrite mandate or for cosmetic cleanup.

Why use Thermo Nuclear Code Quality Review on TypingMind?

Because you install it once and use it with any model. Thermo Nuclear Code Quality 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 Thermo Nuclear Code Quality Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/thermo-nuclear-code-quality-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thermo Nuclear Code Quality 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 Thermo Nuclear Code Quality Review?

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

Is the Thermo Nuclear Code Quality Review AI skill free?

Yes. It is published on GitHub by AnastasiyaW 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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