red-blue-review

red-blue-review is a skill for Claude Code from baixinghao/intent-gate. It costs 111 tokens per session (3,637 once invoked), scanned A, original, MIT.

An optional adversarial review process for requirement analyses. One agent produces the analysis, while an independent reviewer checks its report, database definitions, alignment records, and requirements for missed issues.

In plain words
What is it for?
Use it for complex requirement reviews, structured findings, and revisions based on those findings. It is not intended as a required step for simple or medium-sized requests.
Why use it?
It helps find incorrect assumptions, missing details, and unresolved requirements before work begins. It also keeps review findings separate from the original analysis.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the intent-gate plugin — 4 skills, 1 hook, 1 MCP server shipped together

Good fit Use it for complex requirement reviews, structured findings, and revisions based on those findings. It is not intended as a required step for simple or medium-sized requests.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/baixinghao/intent-gate/red-blue-review
Install

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.

Any agent
npx skills add baixinghao/intent-gate --skill red-blue-review
Clone the repo
git clone --depth 1 https://github.com/baixinghao/intent-gate

Made for: Claude Code.

Or install intent-gate, the plugin that ships this one along with the rest of its 4 skills, 1 hook, 1 MCP server.

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 red-blue-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/baixinghao/intent-gate/red-blue-review/github.svg)](https://agentmods.dev/skills/baixinghao/intent-gate/red-blue-review)
Your own site
<a href="https://agentmods.dev/skills/baixinghao/intent-gate/red-blue-review"><img src="https://agentmods.dev/badge/skills/baixinghao/intent-gate/red-blue-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.

agentmods 80×15 button for red-blue-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/baixinghao/intent-gate/red-blue-review"><img src="https://agentmods.dev/badge/skills/baixinghao/intent-gate/red-blue-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,637 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.
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 $0.00111 $0.03637
Opus 5 $0.00056 $0.01818
Sonnet 5 $0.00022 $0.00727
Haiku 4.5 $0.00011 $0.00364

Measured 9d ago against content hash 85d0561d71ec, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

red-blue-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 9d 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.

skills/red-blue-review/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

红蓝对抗评审(蓝军 playbook)

对需求分析产物做对抗性审查:红军(需求分析方)产出 summary.md/DDL/对齐流水, 蓝军(你)以对立视角找茬,产出结构化 findings,驱动红军整改闭环。

可选项:本评审是插件自带的可选质量门,不是必经流程—— 人类点名启动,或 complex 需求交付时由你建议、人类同意后启动。 没启动意图时不要把评审内容塞进正常分析流程。

适用门槛:仅 complexity=complex 的需求值得评审;simple/medium 直接 PASS 放行并说明。


1. 独立性与信息节食(🔴 对抗有效性的根基)

  • 🔴 蓝军必须在独立 session 执行(新开对话,或派遣与红军无共享上下文的子代理), 禁止与红军同 session——同 session 看到红军推理过程即继承其假设,对抗退化成自查。
  • 🔴 信息不对称:蓝军只读"产物 + 过程文件 + rubric 文件", 禁止向人类索要红军的思维链、对话记录或"当时是怎么想的"。
  • 🔴 蓝军不修改红军的任何产物,唯一输出是 _review/review-findings.md
  • 🟡 同厂商异 session 即可满足独立性;异厂商模型为加分项,非准入门槛。

2. 输入(全部来自 .harness/requests/{需求名}/

文件 用途
summary.md 被审主产物(报告)
_review/alignment-log.md 意图对齐流水(R1 注入保真复核的唯一依据)
_review/review-request.md 红军开单(产物清单 + 自评 + 建议评审重点)
_review/review-findings.md(前轮,如存在) 🔴 并集复核:逐条判定"仍有效→并入本轮 / 已失效→注明原因",禁止覆盖丢弃前轮成果
sql/*.sql(项目根目录) 被审 DDL
原始 PRD(doc/request/ 或用户提供) 蓝军独立扫雷用(R3 歧义漏判检查)

rubric 参照:项目红线文件(有则读)、术语 wiki(有则读)、 MCP prompt doc_analysis_playbook 的产出规范(§3.5-3.8 图表规范)。

🟡 进场前置检查:发现红军产物目录缺 _review/alignment-log.md → 不必开审,直接结论 FAIL-可整改(过程证据缺失:没有对齐流水, R1 注入保真无从复核),要求红军补齐流水后重新开单。

2.5 执行流程(lint 先行 + 两遍制)

第 0 步:lint 先行(🔴 不可跳过)

调 MCP 工具 lint_summary(summary_path),机械检查(状态机终态/死状态/锚点引用/ 规则引用/表读写矩阵)由工具完成。蓝军必须将其作为 findings 基线: 复核真实性后并入 findings(标注来源 [lint]),禁止忽略或重复劳动。 lint 覆盖了 R4/R5/R9 的机械半边,蓝军把精力留给语义判断。

第 1 遍:单产物检查(R1-R8)

逐表逐节过 R1-R8,产出单产物 findings。

第 2 遍:跨图 + 独立扫雷(R9 + R3)——lint 出矩阵,蓝军填判断

🔴 禁止手工建矩阵。lint 已在 _review/lint-report.md 生成三张矩阵骨架 (① 转移清单含事件/动作、② 表读写矩阵、③ 引用核对清单),蓝军的工作是 填判断列:矩阵中的空格、"待核"、❌ 格就是 findings 候选。

🔴 多出边状态的语义跟进(禁止踢回人类):lint L3 或矩阵①标出的"同一状态多条出边", 蓝军必须逐边对照 alignment-log 注入意图与决策表规则,自行判定触发条件是否互斥、 是否与注入意图矛盾(典型案件:注入意图说"超时自动刷新",状态机却画了"超时 → 踢出回首页")。 确实无法判定的,标 MAJOR 并写明缺什么信息, 禁止原样写"需人工确认"——语义分析是蓝军的活,不是人类的

🔴 前轮 findings 并集(限 CRITICAL/MAJOR):进场发现前轮 review-findings.md 时, 仅对前轮 CRITICAL/MAJOR 逐条复核——仍有效的并入本轮(标注"前轮遗留"), 已失效的注明失效原因;MINOR 免检(不复核、不并入,防止低价值项滚动膨胀)。禁止静默丢弃。

Read the full file on GitHub · 197 lines

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. 9d ago First seen · 197 lines · 111 tokens per session scan A 85d0561d71ec

Subscribe to this mod's changes

red-blue-review is a skill published in the GitHub repository baixinghao/intent-gate (2 stars, last pushed 25d ago), licensed MIT. It adds 111 tokens to every session and 3,637 once invoked, about $0.0006 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 2026-08-31.

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