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-plan/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-plan)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/review-plan"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-plan/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-plan"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-plan.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 | $0.00041 | $0.02620 |
| Opus 5 | $0.00020 | $0.01310 |
| Sonnet 5 | $0.00008 | $0.00524 |
| Haiku 4.5 | $0.00004 | $0.00262 |
Grade A, and why
review-plan 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
技术方案审查
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
若入口业务操作数包含 --orchestrated，绑定 {execution-flag} = --orchestrated 并原样转发给 summary finalizer 与 completed 事件；否则绑定为空。不得从 orchestration.json、环境变量或历史产物推断该标记。生命周期事件还必须携带显式触发信息：编排调用使用 {trigger-initiator}=orchestrator，否则使用 model；{request-id} 是本任务与本轮产物的稳定单行标识，{reason-code} 使用 user-request 或 review-finding；started 与 completed 使用同一组值。
审查最新技术方案产物，并产出 review-plan.md 或 review-plan-r{N}.md。
行为边界 / 关键规则
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，逐项复核产物中的五张资格审计表、digest、QCR 和上游关系；不得把流程标签当作身份认证 - 本技能只审查方案产物并写报告，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。
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。
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。
解析任务引用，并确认任务位于本技能支持的状态或目录且存在
task.md；无法定位时按未找到任务处理并停止。
步骤开始：声明 started 事件
确认前置条件和产物上下文后、本轮第一个产出动作之前执行 agent-infra-internal task-event {task-id} review-plan.started --agent {standard-agent-token} --initiator {trigger-initiator} --request-id {request-id} --reason-code {reason-code}。
执行步骤
1. 验证前置条件
要求存在：
.agents/workspace/active/{task-id}/task.md- 至少一个方案产物：
plan.md或plan-r{N}.md
2. 解析审查上下文
What ships with it
4 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.
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.
- 4d ago Changed · +8 lines 436fe00c1ca6
- 6d ago Changed · +5 lines 23196d477eac
- 7d ago Changed b0ef3b470738
- 8d ago Changed · +3 lines f1d01a3f9c80
- 12d ago First seen · 116 lines · 41 tokens per session scan A e077dfe1ea09
review-plan is a skill published in the GitHub repository fitlab-ai/agent-infra (84 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 2,620 once invoked, about $0.0002 per session on Opus 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.
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