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 skills add GaryFigno/Dr.WangAgent --skill code-reviewgit clone --depth 1 https://github.com/GaryFigno/Dr.WangAgentWrote 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/garyfigno/dr.wangagent/code-review)<a href="https://agentmods.dev/skills/garyfigno/dr.wangagent/code-review"><img src="https://agentmods.dev/badge/skills/garyfigno/dr.wangagent/code-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/garyfigno/dr.wangagent/code-review"><img src="https://agentmods.dev/badge/skills/garyfigno/dr.wangagent/code-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 | $0.00049 | $0.00433 |
| Opus 5.5 | $0.00020 | $0.00173 |
| Sonnet 5.5 | $0.00010 | $0.00087 |
| Haiku 4.5 | $0.00005 | $0.00043 |
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 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.
What it actually says
代码审查清单
先定位范围
- 用户没指定文件时，用
git diff --stat HEAD或最近改动定位本次要审的代码 - 每次只审一个明确的变更集，不把无关代码卷进来
逐条核对（按顺序）
- 行为正确性：逻辑分支是否有遗漏、边界条件（空输入 / 并发 / 失败路径）是否处理
- 错误处理：异常是否被吞掉；失败时是否有用户可见的提示
- 安全性：是否有密钥/路径注入、命令拼接、越权文件访问；用户输入是否被转义
- 性能：是否有无界循环、重复 IO、把大文件整读进内存等明显问题
- 可维护性：魔法数字、复制粘贴代码、超长函数、命名是否达意
- 测试：改动是否缺测试；已有测试是否真的覆盖了改动路径
输出格式
对每条发现给出：
[严重/建议/疑问] 文件:行号 — 问题描述
建议：怎么改
- 严重：会导致错误行为、数据丢失或安全问题，必须修
- 建议：能改得更好，但不阻塞
- 疑问：需要作者澄清，不是结论
最后给一个总评（1-3 句）+ 是否建议合并。不要为了凑条数而挑刺，也不要漏掉真问题。
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 · 37 lines · 49 tokens per session scan A 712b334816bd
code-review is a skill published in the GitHub repository GaryFigno/Dr.WangAgent (2 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 433 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.
Other skills, from other repositories
modularity-review
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deslop
Remove AI code slop before committing. Review AI-generated code for unnecessary verbosity, redundant comments, over-engineering, and low-signal patterns that degrade codebase quality over time.
risky-module-review
Mandatory deep review for changes to security-sensitive modules: adminauth.py, keystore.py, agent/tools.py, and any auth/session/key path. Do not skip this for any change to these files.
council-review
Multi-perspective code review before merging. Simulates a council of reviewers (security, correctness, performance, maintainability) independently evaluating a change. Use before any PR merge or for significant changes.
cowork-session
Claude Cowork shared AI pairing sessions with real-time sync, turn-taking, and collaboration context propagation.
karpathy-guidelines
Andrej Karpathy's coding guidelines for AI agents — concise, correct, and well-tested code.