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Agent Skill · 研究检索

factory-review(工厂审查)

factory-review 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。

factory-review 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,权限为权限未标注;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 37,555。

总安装 1,714周安装 70GitHub Stars 2下载 549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:factory-review(工厂审查)
来源仓库:https://github.com/jwilger/agent-skills
仓库路径:skills/factory-review
安装命令:
npx skills add https://github.com/jwilger/agent-skills --skill factory-review
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/jwilger/agent-skills --skill factory-review

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。

用于查找、检索和筛选相关信息,支持根据关键词快速定位结果。适用于工厂模式构建中的审查流程,提供审计摘要和质量报告。生成 PR 摘要、趋势分析和自主调优接口,提升审查效率。安装方式:通过 npx skills add 命令从 GitHub 仓库安装,建议确认权限范围。

factory-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/factory-review

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/jwilger/agent-skills --skill factory-review;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

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

factory-review 怎么安装?

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

factory-review 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。

factory-review 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

factory-review 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。

factory-review 用的人多吗?

公开数据是累计安装 1,714 次,近一周安装 70 次,下载 549 次,GitHub 2 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。

factory-review 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

  • 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

  • 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
factory-review

工厂审查 · 本页

1,7142权限未标注未明确通过

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

Factory Review

Value: Communication -- factory mode concentrates the human's attention on decisions that matter. This skill ensures the review interface is clear, concise, and actionable so the human can provide meaningful oversight without re-reading every line of code.

Purpose

Structures the human review experience during Phase 3 of factory-mode builds. Produces audit trail summaries, PR digests, retrospective synthesis, quality trend reports, and an autonomy tuning interface. The goal is high-signal review in minimal time.

Practices

Audit Trail Summary

Read from .factory/audit-trail/ and present a concise build summary:

  • Slices completed: Count and list (by slice ID and title)
  • Rework rate: Percentage of slices that required rework cycles
  • Gate failure distribution: Count of failures by gate type (tdd, review, ci, mutation)
  • Escalations pending: Count and brief description of each unresolved escalation

Format as a dashboard-style summary. Lead with the numbers, follow with details only if the human asks.

PR Digest

For each merged PR in the build, produce a one-paragraph summary:

  1. What changed: Feature or fix description in plain language
  2. Which slice: The vertical slice ID this PR implements
  3. Which pair: The two engineers who built it
  4. Gate results: Pass/fail for each quality gate (tdd, review, ci, mutation)
  5. Rework count: Number of rework cycles before all gates passed

Keep each digest to 3-5 sentences. The human should be able to scan all PR digests in under a minute for a typical build session.

Retrospective Synthesis

Aggregate findings from team retrospectives (stored in .factory/audit-trail/retrospectives/) and surface patterns:

  • Recurring rework causes: What kinds of issues triggered the most rework?
  • Pair effectiveness: Which pairings produced the fewest rework cycles?
  • Domain hotspots: Which areas of the domain model generated the most discussion or revision?
  • Process friction: Any team-identified impediments or improvement suggestions

Present as bullet points grouped by theme. Do not editorialize -- report what the team said.

Quality Trend Tracking

Read from .factory/audit-trail/metrics/ and present trends:

MetricCurrentPreviousTrend
Mutation score%%up/down/stable
Rework rate%%up/down/stable
Cycle time per slicedurationdurationup/down/stable
Gate failure rate by typecountscountsup/down/stable

"Previous" means the last completed build session. If no previous session exists, omit the comparison column.

Tuning Interface

Accept adjustments to .factory/config.yaml during review. For each proposed change:

  1. Validate: Check that the change is consistent (e.g., cannot enable auto-merge at conservative autonomy level; cannot disable a gate that another setting depends on)
  2. Explain implications: What will change in behavior if this setting is modified? Be specific.
  3. Apply or reject: If valid, apply the change. If invalid, explain why and suggest the nearest valid alternative.

Never apply config changes silently. Always confirm with the human before writing to .factory/config.yaml.

Escalation Review

Present each pending escalation with full context:

  1. Which gate: The quality gate that triggered the escalation
  2. Rework attempts: How many rework cycles were attempted
  3. What was tried: Brief summary of each rework attempt
  4. Current state: What the code looks like now (diff or description)
  5. Recommendation: The team's suggested resolution (if any)

The human decides: resolve, override, or send back for more rework.

Enforcement Note

Advisory in all modes. Summaries and trend reports are self-enforced formatting disciplines.

Hard constraints:

  • No silent config changes: [H]

Constraints

  • "No editorializing": Report what the team said, not what you think about what the team said. "The team discussed X and decided Y" is reporting. "The team wisely decided Y" is editorializing. "The team discussed X but missed the important point about Z" is editorializing. If you have a concern, present it separately as your own analysis, clearly labeled.
  • Config validation: "Never apply config changes silently" means every config change goes through explicit user confirmation, even if the user previously said "go ahead and configure things." Each individual change is confirmed. Blanket permission does not exist for config changes.

Verification

After completing a factory review session, verify:

  • Audit trail summary was presented with current build metrics
  • Every merged PR has a digest (no PRs skipped)
  • Retrospective synthesis covers all team retrospectives from the session
  • Quality trends include comparison to previous session (if available)
  • All pending escalations were presented with full context
  • Any config changes were validated before being applied
  • Human confirmed or overrode each escalation

If any criterion is not met, revisit the relevant practice.

Dependencies

This skill requires the pipeline skill for factory mode infrastructure. It integrates with:

  • pipeline: Reads audit trail files and metrics produced by the pipeline orchestrator during Phase 2
  • ensemble-team: Retrospective synthesis reads team retro output; pair effectiveness data comes from pairing history
  • ci-integration: Gate failure data includes CI_RESULT packets from the ci-integration skill

Missing a dependency? Install with:

npx skills add jwilger/agent-skills --skill pipeline

平台分布

Codex

36.65%
按下载量换算201

Claude

29.79%
按下载量换算164

Cursor

20.6%
按下载量换算113

Gemini CLI

10.41%
按下载量换算57

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

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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