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judgejudge 开发

Agent Skill

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。

总安装

285

周安装

12

GitHub Stars

31

下载量

348
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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