{
  "$schema": "https://bestaiskill.com/schemas/agent-install/2.json",
  "skill": "ljg-analogy",
  "built": "2026-10-26",
  "control_level": 1,
  "control_level_label": "L1 基础",
  "capabilities": [],
  "required_controls": {
    "agent": [
      "auto_load"
    ],
    "admin": [
      "register_source_and_version"
    ]
  },
  "evidence": [
    {
      "file": "SKILL.md",
      "summary": "Pure prompt instructions and rule text, without script execution or external side effects"
    }
  ],
  "verification": {
    "method": "第一遍只读遍历 skills/ljg-analogy 四个文件（SKILL.md、Theory.md、EvalCases.json、agents/openai.yaml）与风险扫描结果，逐条记录实现原理、能力与安全面。第二遍不沿用第一遍结论，重读 Gotchas、『什么样的类比值得交付』与 Theory.md 理论归属段、EvalCases.json 的 purpose，核对每条主张与引用是否逐字可定位，并反查是否漏计脚本或网络调用。",
    "second_pass": [
      {
        "item": "功能声明与实现一致",
        "status": "ok",
        "detail": "frontmatter 声明『基于结构映射理论…寻找精妙类比与关系隐喻』，正文给出目标域/基域定义、成品标准与 Theory.md 理论依据，未发现夸大。"
      },
      {
        "item": "外部依赖核验：无运行时依赖",
        "status": "ok",
        "detail": "目录四个文件均为文本（md/json/yaml）；风险扫描四类（执行外部程序、网络请求、凭证、写盘）全部无命中。"
      },
      {
        "item": "安全结论反例检查",
        "status": "ok",
        "detail": "逐行重核无 curl/wget/fetch、child_process 或 fs 写入/读取凭据；无脚本文件，故 scripts_executed 为空。"
      },
      {
        "item": "引用可定位性",
        "status": "ok",
        "detail": "所有 quote 均可在 SKILL.md／Theory.md／EvalCases.json 原文逐字匹配，长度均大于 12 字符。"
      },
      {
        "item": "判级口径复核",
        "status": "ok",
        "detail": "按判级六档，纯指令无脚本无外发，落 green；Theory.md 的外链属来源标注，不改档位。"
      },
      {
        "item": "理论归属表述核对",
        "status": "ok",
        "detail": "SKILL.md 明写新增标准『不宣称出自论文或已被论文验证』，分析据此未把创作规则写成论文结论。"
      },
      {
        "item": "用例集性质核对",
        "status": "ok",
        "detail": "EvalCases.json purpose 声明不按固定措辞或唯一基域打分，分析未把单一基域当作正确解。"
      }
    ],
    "level": 1,
    "level_label": "V1 已固定",
    "commit": "9e75497a950d2cc58ab9e6871f76405144687bd9",
    "sha256": "f6af0a46838494b1ef2651b58482fe210fe3a776479ce1c2635b2792f007c6df",
    "reviewed_by": null,
    "reviewed_at": null
  },
  "assessed_at": "2026-10-25",
  "method_version": "2.0",
  "mirror": {
    "archive": "https://skillpub.pages.dev/r2/v/2026-10-10/ljg-analogy.tar.gz",
    "sha256": "f6af0a46838494b1ef2651b58482fe210fe3a776479ce1c2635b2792f007c6df",
    "commit": "9e75497a950d2cc58ab9e6871f76405144687bd9"
  },
  "original": {
    "github": "https://github.com/lijigang/ljg-skills/tree/main/skills/ljg-analogy",
    "path": "skills/ljg-analogy",
    "commit": "9e75497a950d2cc58ab9e6871f76405144687bd9"
  }
}