agent-infra: Skill for Claude Code

.agents/skills/review-analysis/SKILL.md

review-analysis is a skill for Claude Code, Codex from fitlab-ai/agent-infra. It costs 44 tokens per session (2,642 once invoked), scanned A, original, MIT.

A review workflow for checking a written requirements analysis before the team turns it into a technical plan.

In plain words
What is it for?
Use it when a tracked task has a requirements-analysis document that needs an independent review and a saved review report.
Why use it?
It catches unclear, missing, or inconsistent requirements early and preserves the review as a task record without changing the application code.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is fitlab-ai/agent-infra's own configuration. It tells Claude Code and Codex how to work on agent-infra itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agent-infra configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/.agents/skills/review-analysis/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/fitlab-ai/agent-infra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for review-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/review-analysis/github.svg)](https://agentmods.dev/skills/fitlab-ai/agent-infra/review-analysis)
Your own site
<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.

agentmods 80×15 button for review-analysis

Your own site · 80×15
<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>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,642 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured today against content hash 3e8b3adac418, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.agents/skills/review-analysis/SKILL.md · 138 lines

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

Read the full file on GitHub · 138 lines

Files

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.

Changes

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.

  1. today Changed 3e8b3adac418
  2. 6d ago Changed 95f644f07ccf
  3. 23d ago Changed 6450f059b664
  4. 29d ago Changed c2e0ce72ebb7
  5. 1mo ago Changed · +2 lines 754c99bf536b
  6. 1mo ago Changed baec33b5abc6
  7. 1mo ago Changed · +4 lines 1dd6c32d463d
  8. 1mo ago Changed 0e20af0e35e9
  9. 1mo ago Changed 7f2832571df4
  10. 1mo ago First seen · 132 lines · 44 tokens per session scan A 5851a5e55822

Subscribe to this mod's changes

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.

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