27 lines
2.1 KiB
JSON
27 lines
2.1 KiB
JSON
{
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"skill_name": "requirements-analysis",
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"evals": [
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{
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"id": 1,
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"name": "vague-no-schema",
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"prompt": "帮我统计下近30天新用户转化率",
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"expected_output": "应主动索要相关表/schema/字段说明，并对'近30天'、'新用户'、'转化'这三个词分别提出多选式澄清问题。不应直接生成 SQL 或假设口径。",
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"files": []
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},
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{
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"id": 2,
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"name": "schema-with-ambiguity",
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"prompt": "统计上周每个渠道的支付成功订单数和支付金额。表结构如下：\n\norder_info(\n order_id bigint,\n user_id bigint,\n channel_id int,\n create_time timestamp,\n pay_time timestamp,\n status int, -- 1待支付 2已支付 3已发货 4已完成 5已退款 6部分退款\n amount decimal(18,2), -- 应付金额\n paid_amount decimal(18,2) -- 实付金额\n)\n\nchannel_dim(\n channel_id int,\n channel_name varchar\n)",
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"expected_output": "应识别出'支付成功'状态码歧义（2/3/4/6 是否都算）、'支付金额'是 amount 还是 paid_amount、'上周'是自然周还是滚动 7 天、退款订单是否计入。应提议 left join channel_dim。不应直接生成 SQL。",
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"files": []
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},
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{
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"id": 3,
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"name": "multi-table-loan",
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"prompt": "我要算下贷款产品的首贷转化漏斗，从注册到首次申请到首次放款的转化率，按月看趋势。表：\n\nuser_register(user_id bigint, register_time timestamp, channel varchar)\nloan_apply(apply_id bigint, user_id bigint, apply_time timestamp, product_id int, apply_amount decimal)\nloan_disburse(disburse_id bigint, apply_id bigint, disburse_time timestamp, disburse_amount decimal, status int)\nproduct_dim(product_id int, product_name varchar, product_type varchar)",
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"expected_output": "应识别多表 join 关系（user → apply 按 user_id, apply → disburse 按 apply_id），追问首贷定义（按用户首次还是按产品首次）、放款 status 含义、'按月看'是注册月还是事件发生月、是否限定产品类型、漏斗分母是否包含没注册成功的用户。",
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"files": []
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}
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]
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}
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