Agent skill

Thermo Nuclear Code Quality Review

by diyanbogdanov in diyanbogdanov/emend

An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth.

MITAuto-check passedDevelopment

Install Thermo Nuclear Code Quality Review

skills CLI
$ npx skills add diyanbogdanov/emend --skill thermo-nuclear-code-quality-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install diyanbogdanov/emend thermo-nuclear-code-quality-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/diyanbogdanov/emend.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/thermo-nuclear-code-quality-review .claude/skills/thermo-nuclear-code-quality-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
thermo-nuclear-code-quality-review
GitHub stars
58
Token cost
~1.9k tokens
SKILL.md length
1,031 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth.

  • Works in 8 steps: Be ambitious about structural… → **Do not let a change push a file from… → Do not allow random spaghetti growth in… → …
  • Tasks that involve Code quality
  • SKILL.md covers Core Prompt, Non-Negotiable Additional…, Primary Review Questions and What to Flag Aggressively, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Thermo Nuclear Code Quality Review is an agent skill from diyanbogdanov/emend. An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Emend's default review skill.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code quality, Code migrations and Dependency management. It works with npm, OpenAPI and TypeScript. The repository describes itself as: Verified AI dependency migrations. Finds which of your lines a version bump actually breaks, fixes them, and proves the result still compiles and passes your tests. The licence is MIT.

When your agent uses it

  • Tasks that involve Code quality
  • Tasks that involve Code migrations
  • Tasks that involve Dependency management

Example prompts

  • “/thermo-nuclear-code-quality-review”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Be ambitious about structural simplification. Do not stop at "this could
  2. **Do not let a change push a file from under 1k lines to over 1k lines
  3. Do not allow random spaghetti growth in existing code. Be highly
  4. Bias toward cleaning the design, not just accepting working code. If
  5. Prefer direct, boring, maintainable code over hacky or magical code. Treat
  6. **Push hard on type and boundary cleanliness where they affect
  7. Keep logic in the canonical layer and reuse existing helpers. Call out
  8. **Treat unnecessary sequential orchestration and non-atomic updates as design

What it can do on your machine

Read from SKILL.md and the folder at commit f8b72b0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Thermo Nuclear Code Quality Review loads about 1.9k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 1,031 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from diyanbogdanov/emend at commit f8b72b0, republished under its MIT licence (© diyanbogdanov). 1,031 words, ~1,903 tokens.

Download SKILL.mdSave it as .claude/skills/thermo-nuclear-code-quality-review/SKILL.md (or your agent's skills folder).
name
thermo-nuclear-code-quality-review
description
An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Emend's default review skill.
source
https://github.com/cursor/plugins/blob/main/cursor-team-kit/skills/thermo-nuclear-code-quality-review/SKILL.md
license
MIT, Copyright (c) 2026 Cursor — full notice in /THIRD-PARTY-NOTICES.md

Thermo-Nuclear Code Quality Review

Use this skill for an unusually strict review focused on implementation quality, maintainability, abstraction quality, and codebase health.

Above all, be ambitious about code structure. Do not merely identify local cleanup opportunities. Actively search for "code judo" moves: restructurings that preserve behavior while making the implementation dramatically simpler, smaller, more direct, and more elegant.

Core Prompt

Perform a deep code quality audit of the current branch's changes. Rethink how to structure / implement the changes to meaningfully improve code quality without impacting behavior. Work to improve abstractions, modularity, reduce spaghetti code, improve succinctness and legibility. Be ambitious: if there is a clear path to improving the implementation that involves restructuring, go for it. Be extremely thorough and rigorous. Measure twice, cut once.

Non-Negotiable Additional Standards

  1. Be ambitious about structural simplification. Do not stop at "this could be a bit cleaner." Look for opportunities to reframe the change so that whole branches, helpers, modes, conditionals or layers disappear entirely. Prefer the solution that makes the code feel inevitable in hindsight. If you see a path to delete complexity rather than rearrange it, push hard for that path.

  2. Do not let a change push a file from under 1k lines to over 1k lines without a very strong reason. Treat it as a strong code-quality smell. Prefer extracting helpers, subcomponents or modules. Only waive this if the resulting file is still clearly organised.

  3. Do not allow random spaghetti growth in existing code. Be highly suspicious of new ad-hoc conditionals, scattered special cases or one-off branches inserted into unrelated flows. If a change adds weird if statements in random places, treat that as a design problem, not a stylistic nit. Prefer pushing the logic into a dedicated abstraction, helper, state machine or policy object.

  4. Bias toward cleaning the design, not just accepting working code. If behavior can stay the same while the structure becomes meaningfully cleaner, push for the cleaner version. Strongly prefer simplifications that remove moving pieces over refactors that spread the same complexity around.

  5. Prefer direct, boring, maintainable code over hacky or magical code. Treat brittle, ad-hoc or "magic" behavior as a code-quality problem. Flag thin abstractions, identity wrappers and pass-through helpers that add indirection without buying clarity.

  6. Push hard on type and boundary cleanliness where they affect maintainability. Question unnecessary optionality, unknown, any or cast-heavy code when a clearer type boundary could exist. If a branch relies on silent fallback to paper over an unclear invariant, ask whether the boundary should be made explicit instead.

  7. Keep logic in the canonical layer and reuse existing helpers. Call out feature logic leaking into shared paths, or implementation details leaking through APIs. Prefer existing canonical utilities over bespoke one-offs.

  8. Treat unnecessary sequential orchestration and non-atomic updates as design smells when the cleaner structure is obvious. If independent work is serialised for no good reason, ask whether it should run in parallel. If related updates can leave state half-applied, push for a more atomic structure.

Primary Review Questions

  • Is there a "code judo" move that would make this dramatically simpler?
  • Can this be reframed so fewer concepts, branches or helper layers are needed?
  • Did the diff add branching complexity where a better abstraction should exist?
  • Did a previously cohesive module become more coupled, more stateful, or harder to scan?
  • Is this logic living in the right file and layer?
  • Are there repeated conditionals that signal a missing model or helper?
  • Is this abstraction actually earning its keep, or is it just a wrapper?
  • Did the diff introduce casts, optionality or ad-hoc object shapes that obscure the real invariant?
  • Is this orchestration more sequential or less atomic than it needs to be?
Show full SKILL.md (413 more words)Show less

What to Flag Aggressively

  • A complicated implementation where a cleaner reframing could delete whole categories of complexity.
  • Refactors that move code around but fail to reduce the number of concepts a reader must hold in their head.
  • New conditionals bolted onto unrelated code paths.
  • One-off booleans, nullable modes or flags that complicate existing control flow.
  • Feature-specific logic leaking into general-purpose modules.
  • Generic "magic" handling that hides simple structure.
  • Thin wrappers or identity abstractions that add indirection without simplifying.
  • Unnecessary casts, any, unknown or optional params that muddy the contract.
  • Copy-pasted logic instead of extracted helpers.
  • "Temporary" branching that is likely to become permanent debt.
  • Bespoke helpers where a canonical utility already exists.
  • Sequential async flow where independent work could run in parallel.
  • Partial-update logic that leaves state less atomic than necessary.

Preferred Remedies

Delete a layer of indirection rather than polishing it. Reframe the state model so conditionals disappear. Turn special-case logic into a simpler default flow. Extract a helper or pure function. Split a large file into focused modules. Replace condition chains with a typed model or explicit dispatcher. Separate orchestration from business logic. Collapse duplicate branches into one clearer flow. Reuse the existing canonical helper. Make type boundaries explicit so the control flow gets simpler.

Do not be satisfied with "maybe rename this" when the real issue is structural. Do not be satisfied with a cleaner version of the same messy idea if there is a plausible path to a much simpler idea.

Tone

Be direct, serious and demanding about quality. Do not be rude, but do not soften major maintainability issues into mild suggestions. If the code is making the codebase messier, say so clearly. If the implementation missed an opportunity for a dramatic simplification, say that clearly too.

Output Priority

  1. Structural code-quality regressions
  2. Missed opportunities for dramatic simplification
  3. Spaghetti / branching complexity increases
  4. Boundary, abstraction and type-contract problems
  5. File-size and decomposition concerns
  6. Modularity and abstraction issues
  7. Legibility and maintainability concerns

Do not flood the review with low-value nits if there are larger structural issues. Prefer a small number of high-conviction changes over a long list of cosmetic notes.

Approval Bar

Do not approve merely because behavior seems correct. The bar is: no clear structural regression; no obvious missed opportunity to make the implementation dramatically simpler; no unjustified file-size explosion; no spaghetti-growth from special-case branching; no hacky or magical abstraction; no unnecessary wrapper, cast or optionality churn; no architecture-boundary leak or avoidable canonical-helper duplication.

© diyanbogdanov, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/thermo-nuclear-code-quality-review of diyanbogdanov/emend.

Open the folder on GitHubat commit f8b72b0

Compare with similar skills

Thermo Nuclear Code Quality Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Thermo Nuclear Code Quality Review

What does Thermo Nuclear Code Quality Review do?

An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Thermo Nuclear Code Quality Review is an agent skill from diyanbogdanov/emend. An extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth.

When should I use Thermo Nuclear Code Quality Review?

Thermo Nuclear Code Quality Review fits situations like: tasks that involve Code quality; tasks that involve Code migrations; tasks that involve Dependency management.

How do I install Thermo Nuclear Code Quality Review in Claude Code?

Run `npx skills add diyanbogdanov/emend --skill thermo-nuclear-code-quality-review -a claude-code`. Or copy the skill folder (skills/thermo-nuclear-code-quality-review in diyanbogdanov/emend) into .claude/skills/thermo-nuclear-code-quality-review in your project. Claude Code loads it when a task matches its description.

How do I install Thermo Nuclear Code Quality Review in Codex?

Run `npx skills add diyanbogdanov/emend --skill thermo-nuclear-code-quality-review -a codex`. Or copy the skill folder (skills/thermo-nuclear-code-quality-review in diyanbogdanov/emend) into .agents/skills/thermo-nuclear-code-quality-review in your project. Codex loads it when a task matches its description.

Can I use Thermo Nuclear Code Quality Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add diyanbogdanov/emend --skill thermo-nuclear-code-quality-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/thermo-nuclear-code-quality-review, .gemini/skills/thermo-nuclear-code-quality-review, .github/skills/thermo-nuclear-code-quality-review and .opencode/skills/thermo-nuclear-code-quality-review in your project.

What does Thermo Nuclear Code Quality Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Thermo Nuclear Code Quality Review is instructions for the agent only.

Does Thermo Nuclear Code Quality Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Thermo Nuclear Code Quality Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Thermo Nuclear Code Quality Review use?

Thermo Nuclear Code Quality Review is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Thermo Nuclear Code Quality Review use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Thermo Nuclear Code Quality Review?

Skills that share tags, products or a category with Thermo Nuclear Code Quality Review: Migrate to Deno (denoland/skills, 100 stars), Install Anti-Slop Oxlint Rules (dmmulroy/anti-slop, 5.4k stars), Linea Dependency Maintenance (Consensys-Incorporated/linea-attestation-registry, 177 stars) and Dependabot Alerts Update (livesession/xyd, 114 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Thermo Nuclear Code Quality Review?

diyanbogdanov (a GitHub user) maintains it in diyanbogdanov/emend, which has 58 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 8, 2026.

Source: diyanbogdanov/emend on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.