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Agent Skill · 研究检索

schema-optimization-orchestrator(模式优化协调器)

schema-optimization-orchestrator 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。

schema-optimization-orchestrator 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 50,054。

总安装 630周安装 26GitHub Stars 2,066下载 206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:schema-optimization-orchestrator(模式优化协调器)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/schema-optimization-orchestrator
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill schema-optimization-orchestrator
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/jeremylongshore/claude-code-plugins-plus-skills --skill schema-optimization-orchestrator

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,支持根据关键词定位候选结果。适合在任务场景中快速获取线索或缩小搜索范围。可结合原始 README 核验实际用法,确保与预期场景匹配。安装前建议确认维护状态及是否依赖外部网络调用。

schema-optimization-orchestrator 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/schema-optimization-orchestrator

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/jeremylongshore/claude-code-plugins-plus-skill…;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

schema-optimization-orchestrator 怎么安装?

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

schema-optimization-orchestrator 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。

schema-optimization-orchestrator 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/jeremylongshore/claude-code-plugins-plus-skills --skill schema-optimization-orchestrator 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。

schema-optimization-orchestrator 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。

schema-optimization-orchestrator 用的人多吗?

公开数据是累计安装 630 次,近一周安装 26 次,下载 206 次,GitHub 2,066 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。

schema-optimization-orchestrator 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。
  • 来源仓库有 2,066 星,维护活跃度相对可查。

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
schema-optimization-orchestrator

模式优化协调器 · 本页

6302,066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

Schema Optimization Orchestrator

Runs a multi-phase schema optimization workflow with strict validation and evidence collection.

Workflow Pattern

This is a test harness pattern:

  • Creates isolated session directory per run
  • Spawns 5 phase agents sequentially
  • Each phase reads reference docs, runs scripts, writes reports
  • Validates JSON outputs and file artifacts
  • Aggregates final summary

Inputs (JSON)

{
  "skill_dir": "/absolute/path/to/.claude/skills/schema-optimization",
  "input_folder": "/path/to/bigquery/export",
  "extraction_type": "bigquery_json",
  "session_dir_base": ".claude/skills/schema-optimization/reports/runs"
}

Required:

  • skill_dir: Absolute path to this skill directory
  • input_folder: Path to data to analyze
  • extraction_type: Type of data extraction (e.g., "bigquery_json")

Optional:

  • session_dir_base: Where to create run directories (default: reports/runs)

Orchestration Steps

1. Create Session Directory

TIMESTAMP=$(date +%Y-%m-%d_%H%M%S)
SESSION_DIR="${session_dir_base}/${TIMESTAMP}"
mkdir -p "${SESSION_DIR}"

2. Run Phase 1: Initial Schema Analysis

Spawn Phase 1 agent with:

{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/01-phase-1.md",
  "input_folder": "<input_folder>",
  "extraction_type": "<extraction_type>"
}

Expected output:

{
  "status": "complete",
  "report_path": "<SESSION_DIR>/01-initial-schema-analysis.md",
  "schema_summary": {
    "total_tables": 0,
    "total_fields": 0,
    "key_findings": []
  }
}

Validation:

  • JSON parse succeeds
  • status is "complete"
  • report_path file exists
  • schema_summary has required keys

3. Run Phase 2: Field Utilization Analysis

Spawn Phase 2 agent with:

{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/02-phase-2.md",
  "phase1_report_path": "<phase1_report_path>",
  "input_folder": "<input_folder>"
}

Expected output:

{
  "status": "complete",
  "report_path": "<SESSION_DIR>/02-field-utilization-analysis.md",
  "utilization_summary": {
    "unused_fields": [],
    "low_utilization_fields": [],
    "recommendations": []
  }
}

4. Run Phase 3: Impact Assessment

Spawn Phase 3 agent with:

{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/03-phase-3.md",
  "phase1_report_path": "<phase1_report_path>",
  "phase2_report_path": "<phase2_report_path>",
  "input_folder": "<input_folder>"
}

Expected output:

{
  "status": "complete",
  "report_path": "<SESSION_DIR>/03-impact-assessment.md",
  "impact_summary": {
    "high_risk_changes": [],
    "medium_risk_changes": [],
    "low_risk_changes": [],
    "estimated_savings": {}
  }
}

5. Run Phase 4: Verification with Script

Spawn Phase 4 agent with:

{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/04-phase-4-verify-with-script.md",
  "phase2_report_path": "<phase2_report_path>",
  "phase3_report_path": "<phase3_report_path>",
  "input_folder": "<input_folder>",
  "script_path": "<skill_dir>/scripts/analyze_field_utilization.sh",
  "output_folder_path": "<input_folder>"
}

Expected output:

{
  "status": "complete",
  "report_path": "<SESSION_DIR>/04-field-utilization-verification.md",
  "verification_summary": {
    "files_analyzed": 0,
    "conclusions_confirmed": [],
    "conclusions_revised": [],
    "unexpected_findings": [],
    "revised_action_items": []
  }
}

6. Run Phase 5: Final Recommendations

Spawn Phase 5 agent with:

{
  "skill_dir": "<skill_dir>",
  "session_dir": "<SESSION_DIR>",
  "reference_path": "<skill_dir>/references/05-phase-5.md",
  "phase1_report_path": "<phase1_report_path>",
  "phase2_report_path": "<phase2_report_path>",
  "phase3_report_path": "<phase3_report_path>",
  "phase4_report_path": "<phase4_report_path>"
}

Expected output:

{
  "status": "complete",
  "report_path": "<SESSION_DIR>/05-final-recommendations.md",
  "recommendations_summary": {
    "priority_actions": [],
    "implementation_plan": [],
    "success_metrics": []
  }
}

Output (JSON Only)

{
  "status": "complete",
  "session_dir": "<SESSION_DIR>",
  "timestamp": "YYYY-MM-DD_HHMMSS",
  "phase_reports": {
    "phase1": "<SESSION_DIR>/01-initial-schema-analysis.md",
    "phase2": "<SESSION_DIR>/02-field-utilization-analysis.md",
    "phase3": "<SESSION_DIR>/03-impact-assessment.md",
    "phase4": "<SESSION_DIR>/04-field-utilization-verification.md",
    "phase5": "<SESSION_DIR>/05-final-recommendations.md"
  },
  "final_summary": {
    "total_tables": 0,
    "total_fields": 0,
    "unused_fields": 0,
    "optimization_opportunities": 0,
    "estimated_savings_pct": 0,
    "verification_status": "confirmed"
  }
}

Error Handling

If any phase fails:

  • Stop execution
  • Return error status with phase details
  • Preserve partial reports for debugging
{
  "status": "error",
  "failed_phase": 3,
  "error_message": "Phase 3 agent failed validation",
  "session_dir": "<SESSION_DIR>",
  "completed_phases": ["phase1", "phase2"]
}

Validation Rules

After each phase:

  1. Parse returned JSON (fail if invalid)
  2. Check status is "complete" (fail if not)
  3. Verify report_path exists on disk (fail if not)
  4. Validate phase-specific summary keys (fail if missing)

Implementation Notes

  • Use Task tool to spawn phase agents
  • Pass exact file paths (no wildcards)
  • Session directory must be absolute path
  • All reports must be written before returning
  • No terminal output except final JSON

Example Usage

User: "Run schema optimization on my BigQuery export"

Claude: [Creates session directory]
Claude: [Spawns Phase 1 agent]
Claude: [Validates Phase 1 output]
Claude: [Spawns Phase 2 agent with Phase 1 report]
Claude: [... continues through Phase 5]
Claude: [Returns final JSON summary]

Files Created Per Run

reports/runs/2025-01-15_143022/
├── 01-initial-schema-analysis.md
├── 02-field-utilization-analysis.md
├── 03-impact-assessment.md
├── 04-field-utilization-verification.md
└── 05-final-recommendations.md

Each file is evidence of work completed.

平台分布

Codex

34.27%
按下载量换算71

Claude

29.62%
按下载量换算61

Cursor

21.56%
按下载量换算44

Gemini CLI

10.7%
按下载量换算22

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/jeremylongshore/claude-code-plugins-plus-skills --skill schema-optimization-orchestrator 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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