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.
npx agentmods add skills/royzhao1991/lingshu/code-reviewnpx skills add RoyZhao1991/LingShu --skill code-reviewgit clone --depth 1 https://github.com/RoyZhao1991/LingShuWhat 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 | $0.00037 | $0.02466 |
| Opus 5 | $0.00018 | $0.01233 |
| Sonnet 5 | $0.00007 | $0.00493 |
| Haiku 4.5 | $0.00004 | $0.00247 |
Grade A, and why
code-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 2d 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
94% identical to thermo-nuclear-code-quality-review — 6 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.
Strict 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.
- 2d ago First seen · 193 lines · 37 tokens per session scan A df6f708a5207
code-review is a skill published in the GitHub repository RoyZhao1991/LingShu (11 stars, last pushed 12d ago), licensed Apache-2.0. It adds 37 tokens to every session and 2,466 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to thermo-nuclear-code-quality-review, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
strands-review
Local preview of the strands-agents/devtools /strands review agent. Body is the upstream Task Reviewer SOP verbatim — do not paraphrase. Use when the user types /strands-review, asks for a "strands review" of a PR, or wants to anticipate what the remote /strands review GitHub Action will flag. Findings are close but…
docs-writer
Draft or rewrite Strands Agents documentation pages. Use when writing new doc pages, rewriting pages that failed audit, drafting sections for existing pages, or writing blog posts and release notes about Strands. Also triggers on "write a doc", "draft a page", "rewrite the quickstart", "add a tutorial for X"…
pr-writer
Generates pull request titles and descriptions. Use when the user asks to create, open, write, draft, or generate a PR, pull request, or merge request description.
docs-planner
Identify documentation gaps and prioritize the docs backlog. Use when planning a docs improvement sprint, after signals surface repeated friction, when new SDK features ship without docs, or for periodic health assessment. Also triggers on "plan docs work", "what docs need writing", "prioritize the backlog", "docs…
pr-create
Creates a GitHub pull request using the gh CLI. Use when the user asks to create, open, or submit a PR on GitHub.
pr-feedback
Fetches PR review feedback and inline comments, categorizes them, and presents options to the user. Use when the user asks to get, read, address, or fix review comments on a pull request.