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add-golden添加金色

Agent Skill

add-golden 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

235

周安装

10

GitHub Stars

160

下载量

82
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

3

许可证

MIT

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:add-golden(添加金色)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/add-golden
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill add-golden
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/yonatangross/orchestkit --skill add-golden

简介

add-golden 提供多智能体协同的内容审核与数据集更新流程,支持质量评分、偏差检测与版本追踪。

  • 适用于需要高质量数据筛选、知识库维护或研究素材整理的场景。
  • 通过九阶段流程完成内容整理、质量分析、验证审批到数据集写入的全链路处理。
  • 需确认是否具备网络访问权限及数据写入权限,注意敏感信息过滤规则的合规性。
  • add-golden 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Add to Golden Dataset

Multi-agent curation workflow with quality score explanations, bias detection, and version tracking.

Quick Start

/add-golden https://example.com/article
/add-golden https://arxiv.org/abs/2312.xxxxx

Task Management (CC 2.1.16)

# Create main curation task
TaskCreate(
  subject="Add to golden dataset: {url}",
  description="Multi-agent curation with quality explanation",
  activeForm="Curating document"
)

# Create subtasks for 9-phase process
phases = ["Fetch content", "Run quality analysis", "Explain scores",
          "Check bias", "Check diversity", "Validate", "Get approval",
          "Write to dataset", "Update version"]
for phase in phases:
    TaskCreate(subject=phase, activeForm=f"{phase}ing")

Workflow Overview

PhaseActivitiesOutput
1. Input CollectionGet URL, detect content typeDocument metadata
2. Fetch and ExtractParse document structureStructured content
3. Quality Analysis4 parallel agents evaluateRaw scores
4. Quality ExplanationExplain WHY each scoreScore rationale
5. Bias DetectionCheck for bias in contentBias report
6. Diversity CheckAssess dataset balanceDiversity metrics
7. ValidationSchema, duplicates, gatesValidation status
8. Silver-to-GoldPromote or mark as silverClassification
9. Version TrackingTrack changes, rollbackVersion entry

Phase 1-2: Input and Extraction

Detect content type: article, tutorial, documentation, research_paper.

Extract: title, sections, code blocks, key terms, metadata (author, date).


Phase 3: Parallel Quality Analysis (4 Agents)

Launch ALL agents in ONE message with run_in_background=True.

AgentFocusOutput
code-quality-reviewerAccuracy, coherence, depth, relevanceQuality scores
workflow-architectKeyword directness, paraphrase, reasoningDifficulty level
data-pipeline-engineerPrimary/secondary domains, skill levelTags
test-generatorDirect, paraphrased, multi-hop queriesTest queries

See Quality Scoring for detailed criteria.


Phase 4: Quality Explanation

Each dimension gets WHY explanation:

### Accuracy: [N.NN]/1.0
**Why this score:**
- [Specific reason with evidence]
**What would improve it:**
- [Specific improvement]

Phase 5: Bias Detection

See Bias Detection Guide for patterns.

Check for:

  • Technology bias (favors specific tools)
  • Recency bias (ignores LTS versions)
  • Complexity bias (assumed knowledge)
  • Vendor bias (promotes products)
  • Geographic/cultural bias
Bias ScoreAction
0-2Proceed normally
3-5Add disclaimer
6-8Require user review
9-10Recommend against

Phase 6: Diversity Dashboard

Track dataset balance across:

  • Domain distribution (AI/ML, Backend, Frontend, DevOps, Security)
  • Difficulty distribution (trivial, easy, medium, hard, adversarial)

Impact assessment: Does new document improve or worsen diversity?


Phase 7: Validation

  • URL validation (no placeholders)
  • Schema validation (required fields)
  • Duplicate check (>80% similarity)
  • Quality gates (min sections, content length)

Phase 8: Silver-to-Gold Workflow

See Silver-Gold Promotion for criteria.

StatusCriteriaAction
GOLDScore >= 0.75, no biasAdd to main dataset
SILVERScore 0.55-0.74Add to silver, track
REJECTScore < 0.55Do not add

Promotion criteria: 7+ days in silver, quality >= 0.75, no negative feedback.


Phase 9: Version Tracking

{
  "version": "1.2.3",
  "change_type": "ADD|UPDATE|REMOVE|PROMOTE",
  "document_id": "doc-123",
  "quality_score": 0.82,
  "rollback_available": true
}
Update TypeVersion Bump
Add/Update documentPatch (0.0.X)
Remove documentMinor (0.X.0)
Schema changeMajor (X.0.0)

Quality Scoring

DimensionWeight
Accuracy0.25
Coherence0.20
Depth0.25
Relevance0.30

Formula: quality_score = accuracy*0.25 + coherence*0.20 + depth*0.25 + relevance*0.30


Key Decisions

DecisionChoiceRationale
Score explanationRequiredTransparency, actionable feedback
Bias detectionDedicated agentPrevent dataset contamination
Two-tier systemSilver + GoldAllow docs time to mature
Version trackingSemantic versioningClear history, safe rollbacks

Related Skills

  • golden-dataset-validation - Validate existing datasets
  • llm-evaluation - LLM output evaluation patterns
  • test-data-management - Test data strategies

Version: 2.0.0 (January 2026)

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

25.56%
按下载量换算21

windsurf

22.88%
按下载量换算19

trae

17.94%
按下载量换算15

OpenCode

13.58%
按下载量换算11

Codex

8.67%
按下载量换算7

Antigravity

3.22%
按下载量换算3

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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