thermo-nuclear-code-quality-review

thermo-nuclear-code-quality-review is a skill for Claude Code from ayberkcansever/ai-skills. It costs 62 tokens per session (3,551 once invoked), scanned A, original, MIT.

A branch-diff code review covering specification fit, unnecessary complexity, maintainability, merge safety, and feature-document updates.

In plain words
What is it for?
It is for reviewing agent-authored or manually authored branch changes after development and before a pull request, including required documentation synchronization.
Why use it?
It checks several common failure points before a pull request and reports findings before code changes are made.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions CLAUDE.md; mentions subagents; mentions AGENTS.md.

Good fit It is for reviewing agent-authored or manually authored branch changes after development and before a pull request, including required documentation synchronization.

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Install with agentmods
npx agentmods add skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ayberkcansever/ai-skills --skill thermo-nuclear-code-quality-review
Clone the repo
git clone --depth 1 https://github.com/ayberkcansever/ai-skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for thermo-nuclear-code-quality-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review/github.svg)](https://agentmods.dev/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review)
Your own site
<a href="https://agentmods.dev/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review"><img src="https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for thermo-nuclear-code-quality-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review"><img src="https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/thermo-nuclear-code-quality-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,551 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00062 $0.03551
Opus 5.5 $0.00025 $0.01420
Sonnet 5.5 $0.00012 $0.00710
Haiku 4.5 $0.00006 $0.00355

Measured 1mo ago against content hash 6635eee44007, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

thermo-nuclear-code-quality-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

sdlc/thermo-nuclear-code-quality-review/SKILL.md · 258 lines

How it starts

The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Thermo-Nuclear Code Quality Review

Review current branch changes through five lenses, in order (the checkout you were launched in — execute-plan launches the reviewer in the ticket worktree). Phases 0–3 are review-first: report findings, change code only if the user says "fix". Phase 4 (docs) is always applied where a feature-docs flow exists. If the conversation was summarized mid-review, re-read this file before continuing — never grade from a summary of the lenses.

Maker/checker is unconditional for agent-authored diffs: if any part of the diff was written by an agent in any session (execute-plan or otherwise — resumed agent-authored branches included), run the review as a read-only subagent — the reviewer must not share the implementer's context or be able to edit. Manual invocation on a human-authored diff may run inline. No subagent facility in this harness → stop and tell the user; proceed inline only on their explicit say-so, and record reviewer: inline (no subagent — maker/checker not independent) in the ## Review entry so the degraded review is visible at resume and retro. Number every finding N | file:line | severity | problem | fix so accepted findings can be appended to the plan file as structured remediation tasks (see the review loop below).

Pinned review model: the reviewer subagent is launched with a pinned review model. Independence comes from the reviewer's fresh, read-only context (maker/checker above), not from running a different model — the reviewer may use the implementer session's own model. Never drop to a weaker model just to differ from it. Resolution, in order:

  1. review-model: <slug> set on the line below → use exactly that slug. If the tool does not offer it, stop and ask the user; never silently substitute.
  2. Not set → pick the strongest reasoning/thinking model the tool can actually launch (newest, highest thinking tier), the session's own model included. If that model is listed but fails to launch (quota, credits, rate limit), take the next strongest and say so. Record the slug chosen, and whether it matches the implementer's model, in the ## Review entry.

review-model: (unset — fill to pin a fixed slug)

This section is the single source of truth for the slug — write-plan and execute-plan reference it instead of hardcoding it; when the model changes, update it here only.

Persist the verdict: when a plan file exists for the branch, the invoking session (orchestrator) owns the ## Review entry, one per cycle, written in two halves — the reviewer subagent is read-only and cannot write the plan; on standalone inline runs the session itself writes both halves:

  1. Before the reviewer runs: cycle <c> | reviewed @ <HEAD SHA> | base @ <base SHA> | suite: <cmd> → <result> — the full-suite evidence the ship rule below reads.
  2. After the verdict: the 3-line rollup, the model used, and the reviewer subagent link, appended to that same entry.

A review that leaves no ## Review entry did not happen — execute-plan's resume check depends on it. An entry with no Verdict: line is an in-flight cycle. A ship verdict is valid only for its recorded HEAD and base SHAs — any later commit invalidates it and requires a new cycle, except commits touching only docs/ paths that leave spec decisions unchanged (doc sync, plan/spec promotion, ## Review entries). A docs commit that edits decisions in spec.md / design.md un-reviews the code; if the base branch moved, re-review before merge.

Step 0 — preconditions, then the diff.

  1. Re-run guard: if the plan's last ## Review entry records Verdict: ship at the current HEAD (or differing from it only by docs-only commits that leave spec decisions unchanged), report "already reviewed at this commit" and stop — re-review only if the user explicitly forces it. A last entry with no Verdict: line is the in-flight cycle you are running — proceed.
  2. Clean tree required: git status --porcelain must be empty. Staged, unstaged, and untracked changes are invisible to git diff <base>...HEAD and would silently escape review. Dirty tree → stop and report; have the work committed first.
  3. Quirks doc: read the project quirks doc when one exists (e.g. docs/quirks.md — hard-learned domain gotchas) — its entries feed Phase 0's semantic-drift sweep and Phase 3.
  4. Base & anchor: resolve the base (git symbolic-ref refs/remotes/origin/HEAD or main/master), then capture git rev-parse HEAD and git rev-parse <base> — they must match the current ## Review line's reviewed @ / base @; a mismatch means the suite evidence is for another commit (see the ship rule).
  5. Review git diff <base>...HEAD. Lenses below reconcile cleanly: Phase 0 checks the diff does what was decided; Phase 1 removes what should not exist; Phase 2 restructures what remains.

Read the full file on GitHub · 258 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +4 lines 6635eee44007
  2. 1mo ago Changed · +8 lines b1021545de70
  3. 1mo ago Changed · +20 lines · -4 tokens per session 4539b3b9f6e3
  4. 1mo ago First seen · 226 lines · 66 tokens per session scan A 9c762f074fec

Subscribe to this mod's changes

thermo-nuclear-code-quality-review is a skill published in the GitHub repository ayberkcansever/ai-skills (2 stars, last pushed 25d ago), licensed MIT. It adds 62 tokens to every session and 3,551 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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