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JingHao-LeonJingHao-Leon/fde-case-lab/skills/doc-review/SKILL.md

doc-review

Skill0 starsChanged 2 months agoMITView on GitHub

单据/材料/报销的智能审核与置信度分流——规则引擎做交叉校验,确定单自动直通,低置信与问题单进人工队列,误放必须为零。当用户要"自动审核材料/发票/办件/包装"时使用。

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SKILL.md

Doc Review · 单据审核与置信分流

对应案例:NO.2(车管所办件)、NO.9(央企财务报销)、NO.19(包装审核)。 参考实现:cases/02-doc-review/(零漏放零误拦,产能 15×人工)、 cases/09-finance-audit/(检出 100%、误放 0、直通 78%)。

何时使用

  • 人工逐张审核材料/发票/办件/包装稿,量大、规则明确、错放后果严重
  • 要把"人工全审"改成"AI 直通 + 人工只看风险"

流程

  1. 必填与齐全性(V1 基线):必需材料清单 + 必填字段非空——这是最小可上线版本。
  2. 交叉校验(V2):证照校验位算法、跨材料字段一致性(姓名/金额,只比对 语义上应该一致的材料——保单金额≠车价)、有效期。
  3. 双角色复核:执行角色与监督角色独立按规则集复核,结论交叉 (案例 9 的"一个执行判断、一个监督、一个发现风险")。
  4. 置信度分流:抽取置信度 < 阈值(默认 0.9)→ review,人工只看问题材料; 硬错误 → reject 并附依据;无任何问题 → auto_pass。
  5. 红线纪律:误放(有问题却直通)= 0 是硬约束;为守住红线牺牲直通率是 正确取舍。
  6. 尺度问题上交:审核发现的历史"放宽"行为,把数据与影响摆给甲方决策 (案例 2 的核心转折),技能不替业务定尺度。

验收

  • 植入缺陷的测试集上:误放 = 0,检出率 ≥ 95%
  • 干净单据误拦率有上限(案例 2 口径:0)
  • 每条 reject/review 附可解释依据

边界

  • 图像抽取(OCR/多模态)是上游,本技能假设字段与置信度已产出
  • 涉及业务利益的审核尺度决策不自动化

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