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Agent Skill · 研究检索

judge(judge 开发)

judge 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。

judge 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,权限为只读;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 65,208。

总安装 285周安装 12GitHub Stars 31下载 348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:judge(judge 开发)
来源仓库:https://github.com/2389-research/claude-plugins
仓库路径:skills/judge
安装命令:
npx skills add https://github.com/2389-research/claude-plugins --skill judge
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/2389-research/claude-plugins --skill judge

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。

适用于需要快速定位和评估多种实现方案的研究场景,尤其适合在代码审查或技术选型时使用。核心能力包括基于五维框架对实现进行评分、可行性检查和设计一致性验证。使用方式要求严格遵循预设输出格式,分别填写 Gate Check、Feasibility Check 和 Scoring Worksheet 三部分内容。

安装需通过 npx 从指定 GitHub 仓库添加,建议确认其是否涉及联网、命令执行或文件读写权限。judge 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/judge

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/2389-research/claude-plugins --skill judge;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

judge 怎么安装?

在终端执行 npx skills add https://github.com/2389-research/claude-plugins --skill judge。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

judge 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。

judge 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

judge 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。

judge 用的人多吗?

公开数据是累计安装 285 次,近一周安装 12 次,下载 348 次,GitHub 31 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。

judge 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
judge

judge 开发 · 本页

28531只读未明确通过

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

Speed-Run Judge

Score implementations using the 5-criteria framework. Fill out ALL sections exactly as shown.

Terminology: This skill uses "impl" but works for both:

  • Showdown: runner-1, runner-2, runner-3 (same design, different implementations)
  • Any%: variant-a, variant-b (different approaches/designs)

REQUIRED OUTPUT FORMAT

You MUST produce this exact structure. Do not summarize or abbreviate.

## Gate Check
| Impl | Tests Pass | Design Adherence |
|------|------------|------------------|
| impl-1 | X/X or | Yes/No |
| impl-2 | X/X or | Yes/No |

## Feasibility Check
| Impl | Status | Notes |
|------|--------|-------|
| impl-1 | OK / Flag | Details |
| impl-2 | OK / Flag | Details |

## Scoring Worksheet

### impl-1
**Fitness for Purpose** (Does it solve the actual problem?)

*Functional requirements:*
- [ ] Primary use case works end-to-end?
- [ ] All explicitly stated requirements implemented?
- [ ] Handles realistic scenarios, not just happy path?

*User needs (beyond literal requirements):*
- [ ] Would the user actually use this, or just demo it?
- [ ] Does it solve the real problem, not just the literal request?
- [ ] Does deployment/distribution match stated needs?

*Future considerations (if relevant):*
- [ ] If growth/scaling mentioned, does architecture support it?
- [ ] If team/collaboration mentioned, is it maintainable by others?

Checklist: _/8 YES → **Score: _/5** (7-8=5, 5-6=4, 4=3, 2-3=2, 0-1=1)
*Note: Not all items apply to every project. Score based on relevant items.*

**Justified Complexity** (Every line earning its keep?)
- Unnecessary abstractions: ___
- Dead code: ___
- Bloat estimate: ___%

*Line count comparison (if multiple impls):*
- This impl: ___ lines
- Smallest impl: ___ lines
- Extra lines justified by: ___

→ **Score: _/5** (5=minimal, 4=slight bloat <10%, 3=10-25% bloat, 2=25-50%, 1=>50%)

**Readability** (Understand core flow in 5 min?)
Violations:
- [ ] Single-letter vars (not loop index): +1 each = __
- [ ] Functions >50 lines: +1 each = __
- [ ] Nesting >3 levels: +1 each = __
- [ ] Magic numbers: +1 each = __
- [ ] Bad function names: +1 each = __
Total violations: __ → **Score: _/5** (0=5, 1-2=4, 3-4=3, 5-7=2, 8+=1)

**Robustness & Scale** (Handles unexpected + growth?)
- [ ] Input validation?
- [ ] External call error handling?
- [ ] Useful error messages?
- [ ] Null/empty handling?
- [ ] Async timeouts?
- [ ] No unbounded loops?
- [ ] O(n log n) or better?
- [ ] Bounded memory?
- [ ] Queries paginated?
- [ ] No blocking I/O in hot path?
- [ ] Backoff/retry logic?
- [ ] Handles 10x load?
Checklist: _/12 YES + feasibility flags → **Score: _/5**
(11-12 + no flags=5, 9-10 or minor flag=4, 7-8=3, 5-6 or major flag=2, <5 or critical flag=1)

**Maintainability** (Pain of next change?)
- [ ] Single responsibility per function?
- [ ] Explicit dependencies (no globals)?
- [ ] Business logic separated from infra?
- [ ] New feature = ≤3 files changed?
- [ ] Config externalized?
- [ ] Tests catch regressions?
Checklist: _/6 YES → **Score: _/5** (6=5, 5=4, 4=3, 2-3=2, 0-1=1)

### impl-2
[REPEAT SAME FORMAT]

### impl-3 (if applicable)
[REPEAT SAME FORMAT]

## Speed-Run Metrics
| Impl | Hosted LLM Calls | Fix Cycles | Generation Time (ms) |
|------|-------------------|------------|----------------------|
| impl-1 | | | |
| impl-2 | | | |

## Judge Scorecard
| Criterion | impl-1 | impl-2 | impl-3 | Best |
|-----------|--------|--------|--------|------|
| Fitness for Purpose | | | | |
| Justified Complexity | | | | |
| Readability | | | | |
| Robustness & Scale | | | | |
| Maintainability | | | | |
| **TOTAL** | /25 | /25 | /25 | |

## Hard Gates
| Gate | Result |
|------|--------|
| Fitness Gate (Δ ≥ 2) | Triggered/Not triggered |
| Critical Flaw (any = 1) | Triggered/Not triggered |

## Winner Selection
**Winner: impl-X** (Score: __/25)

**Selection rationale:**
[2-3 sentences explaining WHY this implementation won]

**Trade-offs acknowledged:**
[What the other implementations did better]

**Token efficiency note:**
[How the winner used hosted LLM - fewer fix cycles, better prompts, etc.]

Scoring Reference

Scores Meaning

ScoreMeaning
5Excellent - exceeds expectations
4Good - fully meets requirements
3Adequate - core works, some gaps
2Poor - significant issues
1Critical flaw - disqualifying

Hard Gates (Automatic)

  1. Fitness Gate: If Fitness Δ ≥ 2 between impls → Higher fitness WINS immediately
  2. Critical Flaw: If ANY criterion = 1 → That impl is ELIMINATED
Fitness Gate Interpretation

The Fitness Gate triggers the same way in both contexts, but means different things:

ContextWhat Fitness Δ ≥ 2 Means
ShowdownOne runner *deviated from or misunderstood the design*. All runners should have similar Fitness since they're implementing the same spec. A large gap is a red flag.
Any%One approach *genuinely solves the problem better*. Different approaches can legitimately have different Fitness. A large gap means one approach is clearly superior.

In both cases, higher Fitness wins. The interpretation just explains *why* the gap exists.

Tiebreaker: Speed-Run Efficiency

When total scores are tied or within 1 point, prefer the implementation that:

  1. Used fewer hosted LLM fix cycles (cleaner contract prompts)
  2. Had fewer total hosted LLM calls (better task decomposition)
  3. Generated code faster (simpler, more focused prompts)

This rewards better use of the speed-run pipeline, not just code quality.

Feasibility Red Flags

Check before scoring:

  • O(n²) or worse on unbounded data
  • Unbounded memory growth
  • Self-DDoS patterns (polling, no backoff)
  • Missing pagination
  • Blocking I/O in hot path
  • No error recovery

Process

  1. Read all implementation code (should already be in context)
  2. Fill out the worksheet for EACH implementation - do not skip sections
  3. Fill out the Speed-Run Metrics table
  4. Check hard gates
  5. Announce winner with rationale (include token efficiency note)

CRITICAL: Use integer scores only (1-5). Do not use half points like 4.5.

CRITICAL: Fill out every checkbox. Do not summarize or abbreviate the worksheet.

平台分布

Codex

35.51%
按下载量换算124

Claude

31.54%
按下载量换算110

Cursor

18.66%
按下载量换算65

Gemini CLI

9.01%
按下载量换算31

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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