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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,权限为执行命令;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 60,488。

总安装 367周安装 15GitHub Stars 3下载 118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/terraphim/terraphim-skills
仓库路径:skills/judge
安装命令:
npx skills add https://github.com/terraphim/terraphim-skills --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/terraphim/terraphim-skills --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。

judge 用于查找、检索和筛选相关信息,支持基于关键词快速定位结果。它适用于需要根据任务场景或来源线索筛选信息的场景。可在 Codex、Claude、Cursor、Gemini CLI 中调用。安装前应确认权限范围和是否涉及敏感操作。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/terraphim/terraphim-skills --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

    它需要执行命令,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

judge 怎么安装?

在终端执行 npx skills add https://github.com/terraphim/terraphim-skills --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?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/terraphim/terraphim-skills --skill judge 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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 367 次,近一周安装 15 次,下载 118 次,GitHub 3 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。

先想清楚的地方

  • 需要执行命令,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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
judge

judge 开发 · 本页

3673执行命令未明确通过

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

Judge

Overview

Evaluate agent task outputs against a three-dimension rubric and produce structured verdict records. The judge operates as a quality gate at the task completion boundary, scoring outputs on Semantic accuracy, Pragmatic usefulness, and Syntactic consistency.

Rubric: Three KLS Dimensions

The rubric reuses three dimensions from the KLS (Krogstie-Lindland-Sindre) quality framework defined in disciplined-quality-evaluation:

DimensionQuestionCriteria
SemanticDoes it accurately represent the domain?Factual correctness, domain terminology, no contradictions
PragmaticDoes it enable the intended decisions/actions?Actionable, useful, addresses the task goal
SyntacticIs it internally consistent and well-structured?Format compliance, structural completeness, no broken references

Scoring

ScoreMeaning
1Poor -- major issues, blocks use
2Below Standard -- significant gaps
3Adequate -- meets minimum bar
4Good -- clear, useful, few issues
5Excellent -- exemplary, no issues

Verdict Thresholds

ConditionVerdict
All dimensions >= 3 AND average >= 3.5accept
Any dimension < 3 OR average < 3.5, but all >= 2improve
Any dimension < 2reject
Models disagree on accept/rejectescalate

Verdict Format

Every judge evaluation produces a JSON verdict record. See automation/judge/verdict-schema.json for the full schema. Minimal structure:

{
  "task_id": "issue-18",
  "model": "opencode/gpt-5-nano",
  "mode": "quick",
  "verdict": "accept",
  "scores": {
    "semantic": 4,
    "pragmatic": 4,
    "syntactic": 5
  },
  "average": 4.33,
  "reasoning": "Brief justification for scores",
  "improvements": [],
  "timestamp": "2026-02-17T14:30:00Z"
}

Output MUST be valid JSON only -- no markdown fencing, no preamble, no trailing text.

Judge Modes

Quick Judge

  • Model: opencode/gpt-5-nano (fast, low latency)
  • Purpose: Rapid pass/fail screening at task completion
  • Timeout: 30 seconds
  • Prompt template: references/prompt-quick.md
  • Output: Verdict JSON with scores and one-line reasoning

Deep Judge

  • Model: opencode/kimi-k2.5-free (thorough, reasoning-capable)
  • Purpose: Detailed evaluation with improvement suggestions
  • Timeout: 60 seconds
  • Prompt template: references/prompt-deep.md
  • Output: Verdict JSON with scores, reasoning chain, and improvement list

Tiebreaker

  • Model: opencode/gpt-5.1-codex-mini (independent perspective, different provider)
  • Purpose: Resolve accept/reject disagreement between quick and deep judges
  • Timeout: 45 seconds
  • Prompt template: Uses deep prompt with prior verdicts appended
  • Trigger: Quick and deep verdicts disagree on accept vs reject/improve

Multi-Iteration Protocol

1. Run quick judge
   |
   +-- accept --> DONE (accept)
   +-- reject --> DONE (reject, log improvements)
   +-- improve --> Run deep judge
                   |
                   +-- accept --> DONE (accept)
                   +-- reject --> DONE (reject, log improvements)
                   +-- improve --> Human fallback

When quick and deep disagree on accept vs reject:

Quick: accept + Deep: reject --> Run tiebreaker
Quick: reject + Deep: accept --> Run tiebreaker
Tiebreaker verdict is final (no further iteration)

Maximum 3 model calls before human fallback.

Human Fallback Criteria

Escalate to human review when:

  1. Three model calls completed without consensus
  2. Any model returns invalid JSON after retry
  3. Any model times out after retry
  4. Verdict is "improve" from both quick and deep judges (ambiguous quality)
  5. Task involves security-sensitive or compliance-critical content

Integration Points

Runner Script

The judge is invoked by automation/judge/run-judge.sh (v2), which:

  • Writes prompts to temp files (eliminates shell escaping issues with file content)
  • Calls opencode with --file <tempfile> for reliable prompt delivery
  • Parses verdict JSON from opencode's JSON event stream
  • Implements the multi-iteration protocol (quick -> deep -> tiebreaker)
  • Optionally enriches verdict logging with terraphim-cli term matching

Verdict Logging

All verdicts are appended to automation/judge/verdicts.jsonl as one JSON object per line. This enables:

  • Audit trail of all quality evaluations
  • Trend analysis across tasks
  • Model performance comparison

Pre-Push Hook

The judge can be invoked as a pre-push hook via automation/judge/pre-push-judge.sh, blocking pushes that receive a "reject" verdict.

Terraphim Integration (Optional)

When terraphim-cli is available, the judge runner uses knowledge graph-based term normalization to identify rubric dimensions in model reasoning.

Knowledge Graph Setup

# Install judge KG files and configure role
bash automation/judge/setup-judge-kg.sh

# Verify installation
terraphim-cli thesaurus --limit 50
terraphim-cli find "factual correctness and actionability"

KG Files

Located in automation/judge/kg/:

FileNormalized TermPurpose
judge-semantic.mdjudge-semanticSynonyms for semantic quality dimension
judge-pragmatic.mdjudge-pragmaticSynonyms for pragmatic quality dimension
judge-syntactic.mdjudge-syntacticSynonyms for syntactic quality dimension
judge-verdicts.mdjudge-verdictsVerdict vocabulary normalization
judge-checklist.mdjudge-checklistRequired verdict elements

Fail-Open Design

If terraphim-cli is not installed, the judge falls back to direct JSON extraction without term normalization. All core functionality works without terraphim -- the KG integration is an enrichment layer.

Resources

references/

平台分布

Codex

36.98%
按下载量换算44

Claude

28.84%
按下载量换算34

Cursor

17.93%
按下载量换算21

Gemini CLI

9.59%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/terraphim/terraphim-skills --skill judge 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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