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-analysis/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-analysis)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/review-analysis"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-analysis/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-analysis"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-analysis.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 | $0.00044 | $0.02642 |
| Opus 5.5 | $0.00018 | $0.01057 |
| Sonnet 5.5 | $0.00009 | $0.00528 |
| Haiku 4.5 | $0.00004 | $0.00264 |
Grade A, and why
review-analysis 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
需求分析审查
--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:普通技能调用使用 model;仅用户明确要求时使用 human。{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。
审查最新需求分析产物,并产出 review-analysis.md 或 review-analysis-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 - 本技能只审查分析产物并写报告,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-analysis.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:
<task-workspace>/active/{task-id}/task.md- 至少一个分析产物:
analysis.md或analysis-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.
- today Changed 3e8b3adac418
- 6d ago Changed 95f644f07ccf
- 23d ago Changed 6450f059b664
- 29d ago Changed c2e0ce72ebb7
- 1mo ago Changed · +2 lines 754c99bf536b
- 1mo ago Changed baec33b5abc6
- 1mo ago Changed · +4 lines 1dd6c32d463d
- 1mo ago Changed 0e20af0e35e9
- 1mo ago Changed 7f2832571df4
- 1mo ago First seen · 132 lines · 44 tokens per session scan A 5851a5e55822
review-analysis is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 2,642 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
plan-tree
Maintain a structured planning document tree made of roadmap/status files, implementation status or handoff TODO files, topic notes, decision records, open questions, ideas/inspiration pools, and repository/file-structure hygiene plans. Use when Codex needs to create, reorganize, audit, or update a multi-file plan…
ccb-github
Maintain this CCB project's GitHub-facing release and npm publication surface. Use when preparing, publishing, auditing, or fixing CCB releases; updating README.md, README/zh.md, localized README files, CHANGELOG.md, VERSION, package.json, GitHub release notes/assets, repository description/topics, npm registry state…
planner-task-packet
Draft CCB workflow task packets, readiness recommendations, and candidate clarification questions without mutating authoritative state.
orchestration-bundle-candidate
Select one route and return one adaptive one-to-four-workgroup bundle candidate as reply-only evidence.
planner-closure-backfill
Produce one revision-fenced Planner backfill proposal for either a Detailer replan or task-set closure, with compact Frontdesk status evidence.
ccb-diagnose
Diagnose a named CCB agent by combining authoritative runtime and job lineage with deep read-only pane inspection, apply bounded recovery when evidence supports it, verify the result, and request authorization before submitting a redacted GitHub issue. Use for $ccbdiagnose agentname, $ccb-diagnose agentname, or…