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Agent Skill · 研究检索

peer-review-loop(同行评审循环)

peer-review-loop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

peer-review-loop 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 69,087。

总安装 220周安装 9GitHub Stars 798下载 71核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:peer-review-loop(同行评审循环)
来源仓库:https://github.com/juliusbrussee/cavekit
仓库路径:skills/peer-review-loop
安装命令:
npx skills add https://github.com/juliusbrussee/cavekit --skill peer-review-loop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/juliusbrussee/cavekit --skill peer-review-loop

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

peer-review-loop 实现多轮评审意见整合与迭代修改追踪机制。适用于复杂需求变更或高风险代码重构场景。可记录每次修改原因与评审人反馈,形成可追溯的决策链条。建议在私有分支完成多轮评审后再合并主干,减少主分支污染风险。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/peer-review-loop

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/juliusbrussee/cavekit --skill peer-review-loop;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

peer-review-loop 怎么安装?

在终端执行 npx skills add https://github.com/juliusbrussee/cavekit --skill peer-review-loop。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

peer-review-loop 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

peer-review-loop 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

peer-review-loop 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

peer-review-loop 用的人多吗?

公开数据是累计安装 220 次,近一周安装 9 次,下载 71 次,GitHub 798 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

peer-review-loop 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
peer-review-loop

同行评审循环 · 本页

220798接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Peer Review Loop — Cavekit + Ralph Loop + Codex Peer reviewer

Run a Cavekit cavekit through a Ralph Loop where Claude builds and Codex adversarially reviews. This is the most rigorous automated quality process available: every few iterations, a completely different model (different training data, different biases, different blind spots) challenges your implementation.


Why This Works

FactorSingle-Model LoopPeer Review Loop
Blind spotsSame model, same blind spots every iterationTwo models catch different classes of issues
Cavekit driftBuilder may silently deviate from cavekitPeer reviewer checks cavekit compliance explicitly
Quality floorConverges to "good enough for one model"Converges to "survives cross-examination"
Dead endsMay retry failed approachesPeer reviewer flags repeated patterns

Architecture

┌─────────────────────────────────────────────────────┐
│                   Ralph Loop                         │
│  (Stop hook feeds same prompt each iteration)        │
│                                                      │
│  ┌──────────┐    ┌──────────────┐    ┌────────────┐ │
│  │  Claude   │───▶│ Build from   │───▶│  Commit    │ │
│  │  (Build)  │    │ cavekit +  │    │  changes   │ │
│  └──────────┘    └──────────────┘    └──────┬─────┘ │
│       ▲                                      │       │
│       │                                      ▼       │
│  ┌──────────┐    ┌──────────────┐    ┌────────────┐ │
│  │  Fix      │◀──│ Parse        │◀──│  Codex CLI │ │
│  │  findings │    │ findings     │    │  (Review)  │ │
│  └──────────┘    └──────────────┘    └────────────┘ │
│                                                      │
│  Completion: all cavekit requirements met +         │
│              no CRITICAL/HIGH findings               │
└─────────────────────────────────────────────────────┘

Review Invocation: Codex CLI (primary) vs MCP (legacy)

The peer review loop supports two invocation paths:

  1. Codex CLI delegation (primary) — Uses scripts/codex-review.sh which calls codex directly in --approval-mode full-auto with a structured review prompt. Faster, no MCP server overhead, findings are parsed and appended to context/impl/impl-review-findings.md automatically.
  2. MCP server (legacy fallback) — Configures Codex as an MCP server in .mcp.json. Claude calls the MCP tool on review iterations. Used only when Codex CLI delegation is unavailable (e.g., older Codex versions).

The build script (setup-build.sh) auto-detects which path to use: if codex-review.sh is present and codex CLI is available, it uses CLI delegation. Otherwise it falls back to MCP configuration.


Quick Start

# Basic: implement a cavekit with peer review
/ck:peer-review-loop context/kits/cavekit-auth.md

# With options
/ck:peer-review-loop context/kits/cavekit-api.md --max-iterations 20 --codex-model gpt-5.4-mini

# Review-only mode (review existing code, don't build new)
/ck:peer-review-loop context/kits/cavekit-api.md --review-only

# Review every iteration instead of every 2nd
/ck:peer-review-loop context/kits/cavekit-auth.md --review-interval 1

What the Command Does

  1. Validates the cavekit file exists and Codex CLI is installed
  2. Configures Codex as an MCP server in .mcp.json (if not already configured)
  3. Builds a Ralph Loop prompt that embeds:

- The cavekit path and related plan/impl files - Instructions to alternate between build and review iterations - The peer review prompt template for Codex - Completion criteria tied to cavekit acceptance criteria

  1. Starts the Ralph Loop via the stop hook mechanism

Codex Review Invocation

Primary: Codex CLI via codex-review.sh

When codex CLI is available, the loop delegates review to scripts/codex-review.sh which exposes the bp_codex_review function. This runs Codex in full-auto mode with a structured adversarial review prompt, parses findings into a standardized table, and appends them to context/impl/impl-review-findings.md.

# What the build loop runs on review iterations:
source scripts/codex-review.sh
bp_codex_review --base main

The CLI path is faster (no MCP server startup), produces structured findings with severity levels (P0-P3), and handles fallback gracefully if Codex is unavailable.

Legacy fallback: Codex MCP Server

When Codex CLI delegation is not available, the command configures Codex as an MCP server automatically:

{
  "mcpServers": {
    "codex-reviewer": {
      "command": "codex",
      "args": ["mcp-server", "-c", "model=\"gpt-5.4\""]
    }
  }
}

Claude calls this MCP server on review iterations to get peer review feedback. The MCP server exposes Codex as a tool that accepts prompts and returns responses — Claude sends the cavekit + code diff, Codex returns findings.

Changing the Codex Model

Use --codex-model to specify which OpenAI model Codex should use:

/ck:peer-review-loop cavekit.md --codex-model gpt-5.4-mini    # faster, cheaper
/ck:peer-review-loop cavekit.md --codex-model gpt-5.4          # default, most capable

Iteration Pattern

Iteration 1: BUILD  — Read cavekit, implement first requirement
Iteration 2: REVIEW — Call Codex CLI (or MCP fallback), get findings, fix CRITICAL/HIGH
Iteration 3: BUILD  — Continue implementing, address remaining findings
Iteration 4: REVIEW — Call Codex CLI (or MCP fallback) again, new findings on new code
...
Iteration N: BUILD  — All requirements met, all findings fixed
             → outputs <promise>SPEC COMPLETE</promise>

The review interval is configurable. Default is every 2nd iteration. Use --review-interval 1 for maximum rigor (review every iteration).


Peer Review Findings File

Review findings are tracked in context/peer-review-findings.md:

# Peer Review Findings

## Latest Review: Iteration 4 — 2026-03-14T10:30:00Z
### Reviewer: Codex (gpt-5.4)

| # | Severity | File | Issue | Status |
|---|----------|------|-------|--------|
| 1 | CRITICAL | src/auth.ts:L42 | Missing input validation on token | FIXED |
| 2 | HIGH | src/auth.ts:L67 | Race condition in session refresh | FIXED |
| 3 | MEDIUM | src/auth.ts:L15 | Unused import | NEW |
| 4 | LOW | src/auth.ts:L3 | Comment typo | WONTFIX |

## History
### Iteration 2
| # | Severity | File | Issue | Status |
|---|----------|------|-------|--------|
| 1 | CRITICAL | src/auth.ts:L20 | SQL injection in login query | FIXED |

Completion Criteria

The loop exits when the completion promise is output. The prompt instructs Claude to ONLY output it when ALL of these are true:

  • All cavekit requirements (R-numbers) have been implemented
  • All acceptance criteria pass
  • No CRITICAL or HIGH peer review findings remain unfixed
  • Build passes
  • Tests pass
  • At least one review iteration completed with no new CRITICAL/HIGH findings

Modes

Build + Review (default)

Alternates between implementing cavekit requirements and calling Codex for review. Use for greenfield implementation from a cavekit.

Review Only (--review-only)

Skips building. Each iteration calls Codex to review existing code against the cavekit, then fixes issues found. Use when code already exists and you want peer review QA.


Prerequisites

  1. Codex CLI installed: npm install -g @openai/codex
  2. OpenAI API key configured: Codex needs authentication (via codex login or env var)
  3. Cavekit context directory: Cavekit file must exist at the given path
  4. Ralph Loop plugin: The ralph-loop plugin must be installed (provides the stop hook)

Convergence Signals

The peer review loop has converged when:

  • Codex's findings drop to zero or only LOW/MEDIUM severity
  • Code diffs between iterations are minimal
  • All cavekit requirements confirmed as met by both Claude and Codex

If the loop hits max iterations without converging:

  • Check context/peer-review-findings.md for persistent issues
  • Consider whether the cavekit needs clarification
  • Run /ck:revise to trace issues back to kits

Cross-References

  • peer-review — The underlying peer review patterns and prompt templates
  • convergence-monitoring — How to detect convergence vs ceiling
  • validation-first — Validation gates that run on every build iteration
  • impl-tracking — How implementation progress is tracked across iterations
  • Ralph Loop — The underlying Ralph Loop mechanism

平台分布

Codex

37.27%
按下载量换算26

Claude

27.75%
按下载量换算20

Cursor

16.75%
按下载量换算12

Gemini CLI

9.26%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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