Borrowing it
Nothing to install: this file belongs to fitlab-ai/agent-infra. 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/fitlab-ai/agent-infra/main/.agents/skills/review-code/SKILL.mdgit clone --depth 1 https://github.com/fitlab-ai/agent-infraWrote 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/fitlab-ai/agent-infra/review-code)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/review-code"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-code/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/fitlab-ai/agent-infra/review-code"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.04447 |
| Opus 5.5 | $0.00019 | $0.01779 |
| Sonnet 5.5 | $0.00009 | $0.00889 |
| Haiku 4.5 | $0.00005 | $0.00445 |
Grade A, and why
review-code 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
代码审查
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
生命周期事件必须携带显式触发信息：普通调用使用 model；仅用户明确要求补充审查时，使用 --initiator human --reason-code manual-review-supplement。{request-id} 是本任务与本轮产物的稳定单行标识，{reason-code} 普通审查使用 user-request 或 review-finding；started 与 completed 使用同一组值。
审查最新代码轮次，并产出 review-code.md 或 review-code-r{N}.md。
行为边界 / 关键规则
路径与返工
本阶段只消费核心从最新有效分析产物解析出的规范路径事实；未被所选路径包含的阶段不得创建产物。同阶段完成一次修订—复审后再次收到 Changes Requested 时，必须在下一执行产物前提交绑定 finding、来源 SHA 和稳定任务事实摘要的返工分类；轮次或文件名变化不是新证据。
持久化报告证据
生成审查报告时，先读取 .agents/rules/evidence-reporting.md。正常检视记录命令、范围、结构化结果、实际结论和未覆盖部分；finding、阻塞或争议保留可复现位置与决定性摘录，身份字段必须精确保留。
- 审查候选资格或
HD-N判断时，先读取.agents/rules/decision-qualification.md，逐项复核产物中的三张资格审计决策表、资格快照、digest 和 QCR；不得把流程标签当作身份认证 - 本技能只审查代码并写报告，不修改业务代码
- 生成会同步到 Issue 的任务或生命周期 Markdown 前，先读取
.agents/rules/sync-content-generation.md并遵循其中的生成端约束；同步端不解析或改写正文 - 执行本技能后，你必须立即更新 task.md
版本戳规则：创建或更新 task.md frontmatter 时，先读取 .agents/rules/version-stamp.md，并写入或刷新 agent_infra_version。
常见违规借口与反驳
| 借口 | 反驳 |
|---|---|
| 「只改了一行，不影响功能」 | 行数不等于影响面；必须读完整 git diff 并定位每处改动的下游效果。 |
| 「大体没问题，给个 Approved」 | 结论必须由 blocker/major/minor 计数支撑，每个问题引用文件:行号，不能凭印象放行。 |
| 「测试改动看着合理，跳过细看」 | 审查测试变更前必须逐条核对 .agents/rules/testing-discipline.md（见步骤 4 门禁）。 |
| 「记得就是这一行，不用查」 | 行号会漂移；下结论前必须用 rg/nl 复核 file:line，不能复现的判断不要写成 blocker。 |
第 0 步：状态核对（执行前硬约束）
在加载 workflow / skill / rules 指令之后、做任何任务状态判断或用户可见结论之前，必须先执行状态核对。指令类文件读取不算对外动作或结论。
运行以下命令，并在本轮产物的 ## 状态核对 段记录任务/产物范围、关键结果和未覆盖部分；正常成功不粘贴完整目录清单或 task.md 尾部。失败、阻塞、身份不一致或争议时，附决定性原文行：
agent-infra-internal task-snapshot {task-id} --format text
状态核对完成前，禁止任何关于外部状态的断言（例如“代码没变”“测试已通过”“没有其他引用”），包括思考阶段。本门禁只提供结构下限；逐条证据配对和真实性仍需按报告模板与审查要求核对。
任务上下文解析
入口可省略 task ref；显式 task scope 仅接受
--task <ref>或-t <ref>，不再解释位置 task ref。保留其余业务操作数后调用agent-infra-internal task-context resolve {task-scope}；{task-scope}为空或 task flag 之一。只读取结构化结果的taskId，后续把{task-id}绑定为完整TASK-YYYYMMDD-HHMMSS。解析失败时透传非零退出码，不自行扫描任务。
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- config/verify.json 655 B
- reference/concurrency-risks.md 924 B
- reference/cross-platform-risks.md 1.3 KB
- reference/documentation-antipatterns.md 925 B
- reference/migration-risks.md 1.6 KB
- reference/output-templates.md 6.5 KB
- reference/report-template.md 7.9 KB
- reference/review-criteria.md 3.6 KB
- reference/security-risks.md 994 B
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.
- today Changed 181a20686587
- 6d ago Changed c4408321c2d1
- 23d ago Changed 644a89532489
- 24d ago Changed 5bb53fb4af81
- 26d ago Changed 88b754a0b7d8
- 29d ago Changed 361369729980
- 1mo ago Changed · -1 lines 3158d050c08d
- 1mo ago Changed 42321c92547d
- 1mo ago Changed · +4 lines 32ceca618d7b
- 1mo ago First seen · 191 lines · 47 tokens per session scan A 73b8d39a87b9
review-code is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 4,447 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 2026-08-30.
Other skills, from other repositories
assigned-review-chain
Submit one node result to the assigned Reviewer and finish only after a bounded pass/rework chain.
find-simplifications
Use for a periodic repo-wide sweep of qwen-code for accumulated excess surface — dead components and files, orphaned locale keys, exports nothing consumes, added-then-removed scaffolding — filing candidates on a tracking issue and landing only what a maintainer has said yes to. Repo-wide and evidence-first; every…
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
agent-debug-ci
Investigate failed scheduled or nightly GitHub Actions runs on the default branch, determine whether the failure is reproducible and repository-owned, implement the smallest evidence-backed fix, validate it, and open a pull request. Use when a failed nightly CI event starts an agent, or when the standard CI or…
audit-ci
Run and analyze Elements cold CI performance profiles and propose evidence-backed build, test, lint, and dependency-graph improvements. Use whenever asked to profile or benchmark pnpm run ci, rerun ci:profile, create a CI performance audit, compare CI timings, find bottlenecks or the completion path, investigate a CI…
review
Adversarial review of an increment before it ships, by a fresh context or another model family; every finding cites path:line and is re-verified. Use for "review", "qa", "critique this".