Borrowing it
Nothing to install: this file belongs to benchflow-ai/benchflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/benchflow-ai/benchflow/main/.agents/skills/thermo-nuclear-code-quality-review/SKILL.mdgit clone --depth 1 https://github.com/benchflow-ai/benchflowWrote 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.
[](https://agentmods.dev/skills/benchflow-ai/benchflow/thermo-nuclear-code-quality-review)<a href="https://agentmods.dev/skills/benchflow-ai/benchflow/thermo-nuclear-code-quality-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/benchflow/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.
<a href="https://agentmods.dev/skills/benchflow-ai/benchflow/thermo-nuclear-code-quality-review"><img src="https://agentmods.dev/badge/skills/benchflow-ai/benchflow/thermo-nuclear-code-quality-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.02485 |
| Opus 5.5 | $0.00022 | $0.00994 |
| Sonnet 5.5 | $0.00011 | $0.00497 |
| Haiku 4.5 | $0.00005 | $0.00248 |
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.
This is a copy
89% identical to thermo-nuclear-code-quality-review — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, this skill should push the reviewer to 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
Start from this baseline:
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 some of the codebase, go for it. Be extremely thorough and rigorous. Measure twice, cut once.
Non-Negotiable Additional Standards
Apply the baseline prompt above, plus these explicit review rules:
-
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.
- Assume there is often a "code judo" move available: a re-organization that uses the existing architecture more effectively and makes the change dramatically simpler and more elegant.
- If you see a path to delete complexity rather than rearrange it, push hard for that path.
-
Do not let a PR push a file from under 1k lines to over 1k lines without a very strong reason.
- Treat this as a strong code-quality smell by default.
- Prefer extracting helpers, subcomponents, modules, or local abstractions instead of letting a file sprawl past 1000 lines.
- If the diff crosses that threshold, explicitly ask whether the code should be decomposed first.
- Only waive this if there is a compelling structural reason and the resulting file is still clearly organized.
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.
- 1mo ago First seen · 193 lines · 54 tokens per session scan A 7faca08b51b6
thermo-nuclear-code-quality-review is a skill published in the GitHub repository benchflow-ai/benchflow (355 stars, last pushed 7d ago), licensed Apache-2.0. It adds 54 tokens to every session and 2,485 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to thermo-nuclear-code-quality-review, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
find-bugs
Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.
architect-init
Use when bootstrapping architecture documentation for a brownfield project by reverse-engineering ADRs from an existing codebase.
architect-analyze
Use when validating generated or refined architecture artifacts, before feature development, during architecture review, or periodically to detect ADR↔AD drift, completeness gaps, and quality issues.
factory-review
Use when reviewing PRs for severity-ranked policy compliance against REVIEW.md, babysitting agent PRs to merge, or self-healing Important findings before a human sees them.
code-review
Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.
parallel-pr-review
Use when asked to "review the open PRs", review a batch or stack of pull requests, or run a recurring PR-review pass on a repo — especially with many PRs, stacked branches, conflicts, or security-sensitive changes. Covers grouping, fan-out to review subagents, verdict synthesis, and posting.