拥有20年招聘经验、审阅过数十万份简历的资深HR的实战判断框架，用于简历筛选评估与面试教练。适用两类场景：(1) 求职者请求"帮我看看这份简历""这份简历有什么问题""帮我准备面试""陪我模拟面试""这个面试问题该怎么回答"；(2) 招聘方/HR/面试官请求"帮我筛选这些简历""这个候选人怎么样""帮我设计面试题""这份简历有什么疑点/红旗"。只要用户提到简历诊断、简历评估、面试准备、模拟面试、候选人评估、面试题设计、识别简历造假/注水/跳槽风险等，都应主动使用此技能，即使用户没有明确说"用这个技能"。尤其适合科技/研发/技术类岗位，但招聘判断逻辑对通用岗位同样适用。
Turn pasted AI evaluation cases into an evidence-bounded triage brief that separates expected and observed behavior, hypotheses, and missing checks without claiming a root cause or fix.
1日あたりの投稿枠、補充の頻度と1回あたりの生産数、現在の在庫数を貼るだけで、週の収支とあと何日で枯れるか、週あたり何本足りないかを式の形で返します。休日だけの増枠が計算から漏れる、1回のバッチで作れる本数に上限がある、補充は成功しているのに量が足りない、作成済みと未使用を分けて数えていないなど9項目を検査し、安全在庫の決め方まで返します。
用配對 CSV 核對原始值與報告值、單位、期間及容許誤差。Compare explicitly paired source/report numbers, units and periods from CSV. Readable Python download; no model required.
We built Autowin by putting models in a loop: discover upgrades → test them → judge them with an assembly of adversarial sub-agents — and we ran that very process on itself, iterating the kit until each piece held up under its own review. The result: from one loosely-defined need, you get back work that has been built, tested, and scored by a panel of judge sub-agents — with closure authority kept outside the model (deterministic checks + you), so an "it's done" is never just taken on faith.
앞두고 있는 결정을 붙여넣으면, "6개월 뒤 이미 실패했다"고 가정하고 실패 원인을 역설계해 위험 순위·조기경보 신호·사전 가드까지 지도로 만들어줍니다. 결정은 대신 내려주지 않습니다.