diff --git a/artifacts/generated/provenance/finance_standard_alignment.json b/artifacts/generated/provenance/finance_standard_alignment.json
new file mode 100644
index 0000000..864ef80
--- /dev/null
+++ b/artifacts/generated/provenance/finance_standard_alignment.json
@@ -0,0 +1,474 @@
+{
+  "mismatched_samples": [
+    {
+      "category_id": "finance:业务.交易信息.交易基本信息",
+      "field": "AMONEY",
+      "name": "交易基本信息",
+      "sample_level": "L3",
+      "sample_path": [
+        "业务",
+        "交易信息",
+        "交易通用信息",
+        "交易基本信息"
+      ],
+      "source_rows": [
+        133
+      ],
+      "standard_entry_ids": [
+        "finance:业务.交易信息.交易通用信息.交易基本信息"
+      ],
+      "standard_levels": [
+        "L2"
+      ]
+    },
+    {
+      "category_id": "finance:业务.账户信息.基本信息",
+      "field": "HXTRADENO",
+      "name": "基本信息",
+      "sample_level": "L3",
+      "sample_path": [
+        "业务",
+        "账户信息",
+        "基本信息"
+      ],
+      "source_rows": [
+        51
+      ],
+      "standard_entry_ids": [
+        "finance:业务.账户信息..基本信息"
+      ],
+      "standard_levels": [
+        "L2"
+      ]
+    }
+  ],
+  "resolved_match_rate": 0.9962,
+  "sample_counts": {
+    "matched": 529,
+    "mismatched": 2,
+    "resolved": 531,
+    "standard_level_unavailable": 0,
+    "standard_missing": 0,
+    "total": 568,
+    "unresolved": 37
+  },
+  "sample_missing_standard_categories": [
+    "finance:业务.交易信息.保险收费信息",
+    "finance:业务.交易信息.保险金信托给付信息",
+    "finance:业务.合约协议.交易类中间业务信息",
+    "finance:业务.合约协议.代理类中间业务信息",
+    "finance:业务.合约协议.保单基本信息",
+    "finance:业务.合约协议.保单责任信息",
+    "finance:业务.合约协议.信托产品信息",
+    "finance:业务.合约协议.信托募集信息",
+    "finance:业务.合约协议.信托运用重要信息",
+    "finance:业务.合约协议.债权转让合同信息",
+    "finance:业务.合约协议.催收信息",
+    "finance:业务.合约协议.其他类中间业务信息",
+    "finance:业务.合约协议.咨询顾问类中间业务信息",
+    "finance:业务.合约协议.固有业务信息",
+    "finance:业务.合约协议.垫款信息",
+    "finance:业务.合约协议.展期信息",
+    "finance:业务.合约协议.托管业务信息",
+    "finance:业务.合约协议.投资理财信息",
+    "finance:业务.合约协议.投资银行业务信息",
+    "finance:业务.合约协议.担保信息",
+    "finance:业务.合约协议.担保承诺类中间业务信息",
+    "finance:业务.合约协议.授信信息",
+    "finance:业务.合约协议.放还款信息",
+    "finance:业务.合约协议.核保信息",
+    "finance:业务.合约协议.签约信息",
+    "finance:业务.合约协议.自营资金投资信息",
+    "finance:业务.合约协议.贷款业务重要信息",
+    "finance:业务.合约协议.资产信息",
+    "finance:业务.合约协议.赔付结果信息",
+    "finance:业务.合约协议.违约信息",
+    "finance:业务.合约协议.逾期信息",
+    "finance:业务.合约协议.销售渠道费用信息",
+    "finance:业务.账户信息.介质信息",
+    "finance:业务.账户信息.冻结信息",
+    "finance:业务.账户信息.特有账户信息",
+    "finance:业务.金融监管和服务.LEI数据发布信息",
+    "finance:业务.金融监管和服务.LEI注册信息",
+    "finance:业务.金融监管和服务.个人征信机构管理信息",
+    "finance:业务.金融监管和服务.事件公告信息",
+    "finance:业务.金融监管和服务.事项审批信息",
+    "finance:业务.金融监管和服务.事项申请信息",
+    "finance:业务.金融监管和服务.企业征信机构管理信息",
+    "finance:业务.金融监管和服务.信息沟通信息",
+    "finance:业务.金融监管和服务.信用体系建设管理信息",
+    "finance:业务.金融监管和服务.信用评级信息",
+    "finance:业务.金融监管和服务.分类考核评级信息",
+    "finance:业务.金融监管和服务.咨询投诉管理信息",
+    "finance:业务.金融监管和服务.国际合作信息",
+    "finance:业务.金融监管和服务.失效居民身份证核查信息",
+    "finance:业务.金融监管和服务.宣传培训信息",
+    "finance:业务.金融监管和服务.居民身份证核查信息",
+    "finance:业务.金融监管和服务.异议反馈信息",
+    "finance:业务.金融监管和服务.征信维权信息",
+    "finance:业务.金融监管和服务.成果评审信息",
+    "finance:业务.金融监管和服务.房地产市场分析信息",
+    "finance:业务.金融监管和服务.房地产数据处理信息",
+    "finance:业务.金融监管和服务.房地产调查信息",
+    "finance:业务.金融监管和服务.报表处理信息",
+    "finance:业务.金融监管和服务.指标分析信息",
+    "finance:业务.金融监管和服务.指标管理信息",
+    "finance:业务.金融监管和服务.机构业务申请基本信息",
+    "finance:业务.金融监管和服务.机构业务许可证书管理信息",
+    "finance:业务.金融监管和服务.机构年检信息",
+    "finance:业务.金融监管和服务.案例管理信息",
+    "finance:业务.金融监管和服务.汇率信息",
+    "finance:业务.金融监管和服务.消费者教育信息",
+    "finance:业务.金融监管和服务.热点汇总信息",
+    "finance:业务.金融监管和服务.电子公告管理信息",
+    "finance:业务.金融监管和服务.监督检查信息",
+    "finance:业务.金融监管和服务.舆情采集信息",
+    "finance:业务.金融监管和服务.行政监管信息",
+    "finance:业务.金融监管和服务.调查协查信息",
+    "finance:业务.金融监管和服务.金融信用数据库管理信息",
+    "finance:业务.金融监管和服务.问卷调查信息",
+    "finance:业务.金融监管和服务.非居民身份信息核查信息",
+    "finance:客户.个人.个人信贷信息",
+    "finance:客户.个人.个人健康生理信息",
+    "finance:客户.个人.个人党政信息",
+    "finance:客户.个人.个人司法信息",
+    "finance:客户.个人.个人地理位置信息",
+    "finance:客户.个人.个人就学信息",
+    "finance:客户.个人.个人职业信息",
+    "finance:客户.个人.个人财产信息",
+    "finance:客户.个人.个人资质证书信息",
+    "finance:客户.个人.个人间关系信息",
+    "finance:客户.个人.交易类标签信息",
+    "finance:客户.个人.价值标签信息",
+    "finance:客户.个人.公私间关系信息",
+    "finance:客户.个人.关系标签信息",
+    "finance:客户.个人.基础标签信息",
+    "finance:客户.个人.弱隐私生物特征信息",
+    "finance:客户.个人.强隐私生物特征信息",
+    "finance:客户.个人.签约标签信息",
+    "finance:客户.个人.营销服务标签信息",
+    "finance:客户.个人.行为信息",
+    "finance:客户.个人.行为标签信息",
+    "finance:客户.个人.风险标签信息",
+    "finance:客户.单位.交易类标签信息",
+    "finance:客户.单位.价值标签信息",
+    "finance:客户.单位.企业信贷信息",
+    "finance:客户.单位.企业司法信息",
+    "finance:客户.单位.企业工商信息",
+    "finance:客户.单位.企业税务信息",
+    "finance:客户.单位.传统鉴别信息",
+    "finance:客户.单位.公私间关系信息",
+    "finance:客户.单位.单位联系信息",
+    "finance:客户.单位.单位财务信息",
+    "finance:客户.单位.单位间关系信息",
+    "finance:客户.单位.基础标签信息",
+    "finance:客户.单位.签约标签信息",
+    "finance:客户.单位.股东信息",
+    "finance:客户.单位.股东重要信息",
+    "finance:客户.单位.营销标签信息",
+    "finance:客户.单位.行为信息",
+    "finance:客户.单位.行为标签信息",
+    "finance:客户.单位.风险标签信息",
+    "finance:监管.数据报送.监管指标上报信息",
+    "finance:监管.数据报送.监管明细数据上报信息",
+    "finance:监管.数据报送.金融统计信息",
+    "finance:监管.数据收取.审计信息",
+    "finance:监管.数据收取.统计分析信息",
+    "finance:监管.数据收取.评价、处罚与违规信息",
+    "finance:监管.数据收取.预警信息",
+    "finance:经营管理.技术管理.信息资产管理信息",
+    "finance:经营管理.技术管理.办公软件资源",
+    "finance:经营管理.技术管理.安全管理信息",
+    "finance:经营管理.技术管理.开发信息",
+    "finance:经营管理.技术管理.测试信息",
+    "finance:经营管理.技术管理.系统运维信息",
+    "finance:经营管理.技术管理.规划信息",
+    "finance:经营管理.技术管理.质量管理信息",
+    "finance:经营管理.综合管理.一般员工信息（公开）",
+    "finance:经营管理.综合管理.业务发展规划信息",
+    "finance:经营管理.综合管理.业绩信息",
+    "finance:经营管理.综合管理.人力需求规划信息",
+    "finance:经营管理.综合管理.人员招聘报名信息",
+    "finance:经营管理.综合管理.人员招聘考试信息",
+    "finance:经营管理.综合管理.企事业财务管理信息",
+    "finance:经营管理.综合管理.信息披露信息",
+    "finance:经营管理.综合管理.党务纪检信息",
+    "finance:经营管理.综合管理.公文信息",
+    "finance:经营管理.综合管理.内部审计信息",
+    "finance:经营管理.综合管理.内部资金往来信息",
+    "finance:经营管理.综合管理.分类信息",
+    "finance:经营管理.综合管理.利率管理信息",
+    "finance:经营管理.综合管理.合规信息",
+    "finance:经营管理.综合管理.员工信息（非公开）",
+    "finance:经营管理.综合管理.品牌战略规划信息",
+    "finance:经营管理.综合管理.国库管理信息",
+    "finance:经营管理.综合管理.培训与资质信息",
+    "finance:经营管理.综合管理.基建财务管理信息",
+    "finance:经营管理.综合管理.基本信息（公开）",
+    "finance:经营管理.综合管理.基本信息（非公开）",
+    "finance:经营管理.综合管理.层级信息",
+    "finance:经营管理.综合管理.岗位角色信息",
+    "finance:经营管理.综合管理.工会信息",
+    "finance:经营管理.综合管理.市场营销规划信息",
+    "finance:经营管理.综合管理.技能信息",
+    "finance:经营管理.综合管理.支付清算汇总信息",
+    "finance:经营管理.综合管理.支付清算鉴别信息",
+    "finance:经营管理.综合管理.档案管理信息",
+    "finance:经营管理.综合管理.法务信息",
+    "finance:经营管理.综合管理.税务信息",
+    "finance:经营管理.综合管理.章程制度信息",
+    "finance:经营管理.综合管理.管理会计信息",
+    "finance:经营管理.综合管理.薪资信息",
+    "finance:经营管理.综合管理.证件信息",
+    "finance:经营管理.综合管理.财务会计信息",
+    "finance:经营管理.综合管理.财务支出信息",
+    "finance:经营管理.综合管理.资产负债管理信息",
+    "finance:经营管理.综合管理.资金渠道流通汇总信息",
+    "finance:经营管理.综合管理.资金规划信息",
+    "finance:经营管理.综合管理.邮件信息",
+    "finance:经营管理.营销服务.产品重要信息",
+    "finance:经营管理.营销服务.分类信息",
+    "finance:经营管理.营销服务.基本信息",
+    "finance:经营管理.营销服务.市场营销信息（公开）",
+    "finance:经营管理.营销服务.市场营销信息（非公开）",
+    "finance:经营管理.营销服务.新产品（项目）研发信息",
+    "finance:经营管理.营销服务.服务管理信息",
+    "finance:经营管理.营销服务.渠道管理信息",
+    "finance:经营管理.营销服务.特征信息",
+    "finance:经营管理.营销服务.第三方代理渠道信息（公开）",
+    "finance:经营管理.营销服务.管理信息",
+    "finance:经营管理.营销服务.线上自有渠道信息（公开）",
+    "finance:经营管理.营销服务.线下自有渠道信息（公开）",
+    "finance:经营管理.营销服务.营销管理信息",
+    "finance:经营管理.运营管理.单证入库信息",
+    "finance:经营管理.运营管理.单证发放信息",
+    "finance:经营管理.运营管理.单证日常管理信息",
+    "finance:经营管理.运营管理.单证核销信息",
+    "finance:经营管理.运营管理.单证设计信息",
+    "finance:经营管理.运营管理.参数／指标运维信息",
+    "finance:经营管理.运营管理.合作内容信息",
+    "finance:经营管理.运营管理.合作单位基本信息",
+    "finance:经营管理.运营管理.合作单位联系人信息",
+    "finance:经营管理.运营管理.客户及监管相关音影像信息",
+    "finance:经营管理.运营管理.技术安防信息",
+    "finance:经营管理.运营管理.日常管理相关音影像信息",
+    "finance:经营管理.运营管理.柜面服务信息",
+    "finance:经营管理.运营管理.物理安防信息",
+    "finance:经营管理.运营管理.电话服务信息",
+    "finance:经营管理.运营管理.网络服务信息",
+    "finance:经营管理.风险管理信息.风险偏好不定量指标",
+    "finance:经营管理.风险管理信息.风险偏好偏离信息",
+    "finance:经营管理.风险管理信息.风险偏好定最指标",
+    "finance:经营管理.风险管理信息.风险抵御水平信息",
+    "finance:经营管理.风险管理信息.风险控制信息",
+    "finance:经营管理.风险管理信息.风险监测信息",
+    "finance:经营管理.风险管理信息.风险缓释信息",
+    "finance:经营管理.风险管理信息.风险计量信息",
+    "finance:经营管理.风险管理信息.风险识别信息",
+    "finance:经营管理.风险管理信息.黑名单信息"
+  ],
+  "standard": "finance",
+  "standard_entries": 237,
+  "standard_level_unavailable_samples": [],
+  "standard_missing_samples": [],
+  "training_categories": 233,
+  "training_categories_observed": 20,
+  "training_categories_unobserved": 213,
+  "unresolved_by_status": {
+    "missing_leaf": 34,
+    "path_mismatch": 3
+  },
+  "unresolved_evidence": [
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "交易清金额信息",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本信息",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本信息",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本信息",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "单位基本情况",
+      "status": "missing_leaf"
+    },
+    {
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+}
diff --git a/artifacts/generated/provenance/infra_standard_alignment.json b/artifacts/generated/provenance/infra_standard_alignment.json
new file mode 100644
index 0000000..03b2695
--- /dev/null
+++ b/artifacts/generated/provenance/infra_standard_alignment.json
@@ -0,0 +1,254 @@
+{
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new file mode 100644
index 0000000..38473ca
--- /dev/null
+++ b/artifacts/generated/provenance/shougang_standard_alignment.json
@@ -0,0 +1,5179 @@
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+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    },
+    {
+      "candidate_standard_categories": [],
+      "leaf_name": "——",
+      "status": "placeholder"
+    }
+  ]
+}
diff --git a/artifacts/generated/provenance/standard_build_summary.json b/artifacts/generated/provenance/standard_build_summary.json
new file mode 100644
index 0000000..1181702
--- /dev/null
+++ b/artifacts/generated/provenance/standard_build_summary.json
@@ -0,0 +1,149 @@
+{
+  "legacy_information_loss": {
+    "finance": {
+      "legacy_dict_entries": 237,
+      "legacy_dict_path_compression": {
+        "entries_at_legacy_depth": 6,
+        "entries_with_path_deeper_than_legacy_L1_L2_leaf": 231,
+        "note": "legacy financial_standards_dict stored L1-L2-leaf identity strings; the real standard has 三级子类 provenance nodes that the legacy digest dropped (canonical standard keeps every entry, standard_entry_id includes the real 三级)"
+      },
+      "legacy_unparseable_level_values": [
+        "3 4级",
+        "l级"
+      ],
+      "standard_vs_registry_ids": {
+        "missing_from_registry": 0,
+        "registry_ids": 233,
+        "standard_training_categories": 233
+      }
+    },
+    "shougang": {
+      "legacy_dict_losses": {
+        "catalog_entries": 234,
+        "legacy_dict_entries": 234,
+        "note": "guanji_dict kept only 'name（code）' + description: real hierarchy path and the 分级 column were dropped (registry path was [] and no class field existed)",
+        "with_grading_restored": 234,
+        "with_real_path_restored": 234
+      },
+      "standard_vs_registry_codes": {
+        "note": "B3-6 中厚板作业计划 exists in the raw catalog but was dropped by guanji_dict/registry",
+        "registry_codes": 233,
+        "registry_only": [],
+        "standard_codes": 234,
+        "standard_only": [
+          "B3-6"
+        ]
+      }
+    }
+  },
+  "pers_info": {
+    "alignment_headline": null,
+    "note": "no confirmed classification/grading standard; the 18-category registry remains dataset-derived and is NOT presented as a canonical standard; no standard_data_level is fabricated",
+    "standard_source": null,
+    "status": "missing_or_unknown"
+  },
+  "phase": "phase1-canonical-standard",
+  "standards": {
+    "finance": {
+      "alignment_headline": {
+        "matched": 529,
+        "mismatched": 2,
+        "resolved": 531,
+        "resolved_match_rate": 0.9962,
+        "samples_total": 568,
+        "standard_missing": 0
+      },
+      "build": {
+        "dataset": "finance",
+        "entries_read": 237,
+        "id_strategy": "path",
+        "issues": [
+          {
+            "detail": "row 150 category '市场营销信息（公开）': raw level 'l' kept as-is, standard_data_level=null (not guessed)",
+            "kind": "level_unparseable"
+          },
+          {
+            "detail": "row 168 category '客户及监管相关音影像信息': raw level '3 4' kept as-is, standard_data_level=null (not guessed)",
+            "kind": "level_unparseable"
+          }
+        ],
+        "level_distribution": {
+          "''": 2,
+          "L1": 11,
+          "L2": 157,
+          "L3": 61,
+          "L4": 6
+        },
+        "source_file": "data/raw/金融行业数据安全分类分级标准指南.xlsx",
+        "source_sheet": "Table 1",
+        "standard_entries_out": 237,
+        "standard_name": "金融行业数据安全分类分级标准指南",
+        "training_categories": 233
+      },
+      "standard_entries": 237,
+      "standard_source": "finance",
+      "status": "built",
+      "training_categories": 233
+    },
+    "infra": {
+      "alignment_headline": {
+        "matched": 64,
+        "mismatched": 0,
+        "resolved": 64,
+        "resolved_match_rate": 1.0,
+        "samples_total": 64,
+        "standard_missing": 0
+      },
+      "build": {
+        "dataset": "shougang",
+        "entries_read": 234,
+        "id_strategy": "code",
+        "issues": [],
+        "level_distribution": {
+          "L1": 16,
+          "L2": 170,
+          "L3": 48
+        },
+        "source_file": "data/raw/关基-数据分类分级目录.xlsx",
+        "source_sheet": "数据分类分级",
+        "standard_entries_out": 234,
+        "standard_name": "首钢京唐数据分类分级目录（关基）",
+        "training_categories": 234
+      },
+      "standard_entries": 234,
+      "standard_source": "shougang",
+      "status": "built",
+      "training_categories": 234
+    },
+    "shougang": {
+      "alignment_headline": {
+        "matched": 18393,
+        "mismatched": 0,
+        "resolved": 18393,
+        "resolved_match_rate": 1.0,
+        "samples_total": 19415,
+        "standard_missing": 0
+      },
+      "build": {
+        "dataset": "shougang",
+        "entries_read": 234,
+        "id_strategy": "code",
+        "issues": [],
+        "level_distribution": {
+          "L1": 16,
+          "L2": 170,
+          "L3": 48
+        },
+        "source_file": "data/raw/关基-数据分类分级目录.xlsx",
+        "source_sheet": "数据分类分级",
+        "standard_entries_out": 234,
+        "standard_name": "首钢京唐数据分类分级目录（关基）",
+        "training_categories": 234
+      },
+      "standard_entries": 234,
+      "standard_source": "shougang",
+      "status": "built",
+      "training_categories": 234
+    }
+  }
+}
diff --git a/docs/design/data_level_design.md b/docs/design/data_level_design.md
new file mode 100644
index 0000000..371b062
--- /dev/null
+++ b/docs/design/data_level_design.md
@@ -0,0 +1,168 @@
+# data_level / classification / field_sensitive 角色区分（设计说明 · Phase 0）
+
+Status: 只读记录（2026-08-20，数据快照 v1 后新增 raw Excel 已核对）。
+原则：只记录仓库内可验证事实，不猜测等级语义；**不改任何代码 / 数据 / 现有训练行为**。
+
+**Phase 0 冻结范围**：只冻结三个字段「是什么、来自哪里、当前代码是否训练」。**不决定** `data_level` 最终训不训练——那属于 Stage2 task contract 的决策，本文档保持开放。
+
+## 1. 三个概念是什么（角色模型）
+
+### `classification`（`level_1..level_4`）
+- **角色**：sample label（源端标注），并且是**当前**的 training target（经 canonical 解析为 `target.category_id`，Stage1/Stage2 唯一监督目标）。
+- **槽位口径（重要，避免概念混淆）**：`level_1..level_4` 是统一分类槽位。当前 processed 数据把**最终/最小粒度类别**放进 `level_4` 槽位，但不同源数据的**真实分类深度并不统一**：
+  - pers_info：源数据是单层分类（学籍管理信息…），canonical schema 把它放进 `level_4`；`level_1/2/3` 为空，**不是说源标准真有四级体系**。
+  - finance：四层填充（`level_1..level_4`）；shougang/infra：四层填充。
+  - 因此**不能把 `level_4` 等价成"真实标准的第四层"**；建无损 standard（Phase 1）时按各源的真实深度建模。
+
+### `data_level`（`L1..L4`）
+- **角色**：**sample label——样本携带的分级标签**。是否同时可被确定为 standard knowledge，**分数据集**：
+  - finance / shougang / infra：有证据可追溯到分类分级标准/目录（finance↔《金融行业数据安全分类分级标准指南》"最低安全级别参考"；shougang/infra↔首钢京唐数据分类分级目录"分级"列，192/192 零冲突）。
+  - pers_info：**目前只有 sample label，尚无已确认的 standard knowledge 来源**（源 Excel 直接填 `L1/L2/L3`，仓库内无对应标准文档）。
+  - 所以不做笼统定义"`data_level` = standard knowledge 的样本级表现"——pers_info 就是反例。
+- **训练目标状态**：当前实现未进入 training target（`src/agent`、`script/verl`、`script/canonical` 对 `data_level` 零引用，parquet 不导出）；**目标设计倾向将 `data_level` 作为与 classification 并列的模型预测目标**，即模型需要根据字段语义、业务上下文和分级规则独立判断安全等级，而不是仅根据 category 查表返回标准等级。最终接口仍待 Stage2 task contract 冻结。
+- **训练目标总口径**：
+  ```text
+  当前实现：
+    classification → training target
+    data_level     → provenance only
+  目标任务方向：
+    classification → 模型预测
+    data_level     → 模型独立预测
+    standard_data_level → 分级参考 / 审计依据，不直接等同于字段最终等级
+
+  最终输出格式、prompt 可见信息和 reward
+  → 待 Stage2 task contract 冻结
+  ```
+
+### `sample_data_level` 与 `standard_data_level`（两个变体，必须分开保存）
+- **`sample_data_level`** = 原始数据中具体字段的实际分级标签（即 § 上文 `data_level` 的本义），作为训练/评测 gold 候选。
+- **`standard_data_level`** = 分类分级标准给 category 的参考/最低等级（如 finance 标准列原名就叫**"最低安全级别参考"**）。
+- 二者**必须分别保存，不应在 canonicalization 时相互覆盖**：finance 的"最低安全级别参考"本身就不适合直接建模成 `category → 唯一最终 data_level`。
+- 后续 Phase 1 的 canonical standard 建议明确字段名 `standard_data_level`（值如 `"L3"`），**不要**简单命名为 `data_level`，否则日后容易再次与 sample gold 混淆。
+
+### `field_sensitive`（是/否）
+- **角色**：**独立 field-level 源端标注**，不属于上述两者。
+- 仅存在于 `data/raw/关基设施数据分类分级-不包含训练-用于测试(1).xlsx`（infra 测试来源）的"字段是否敏感"列；mapping 未映射 → 未进入 processed/canonical/parquet。
+- 与 `data_level` **不同步**（64 行：`L3+是=32 / L3+否=14 / L2+是=14 / L2+否=4`），证据明确，故为独立概念。
+
+### 三者关系（已验证统计事实）
+- `data_level` 与分类深度无关（pers_info 单层分类却分布 L1–L3）；在数据上 `data_level` 对叶子类别近似确定性映射（finance 18/20、infra 4/4、pers_info 17/18、shougang 192/192 类别为单一级别，全库仅 4 条跨级样本）。
+
+## 2. 三个字段在管线各层的位置
+
+| 层 | classification | data_level | field_sensitive |
+| --- | --- | --- | --- |
+| raw（data/raw/*.xlsx） | 源列：一级子类/一级分类/分类 等 | 源列：数据级别(finance) / 分级(infra,shougang,pers_info)，原始值 `1..4`（pers_info 为 `L1..L3`） | 仅 `关基设施…用于测试(1).xlsx` 有"字段是否敏感"列 |
+| preprocessing（`script/preprocessing/processor.py`） | `normalize_label` 归一化（剥 `（A1-1-3）` 代码后缀） | `normalize_level`：`1/2/3/4/LEVEL1..4 → L1..L4`，其余直接 raise（仅重命名+别名归一，无转换规则） | **未映射 → 丢弃** |
+| canonical（`data/canonical/<ds>/all.json`） | 原样保留（provenance）；另解析出 `target.category_id` 为唯一训练身份 | 原样保留（provenance；已验证 4 数据集 100% 保真） | 不存在 |
+| SFT / RL parquet（`data/sft|rl/…`） | 标签 = `target.category_id`；messages 只含 prompt-visible 元数据（默认 field_name/field_description/field_type） | **不导出**（SFT row 与 RL five-field 均无 data_level） | 不存在 |
+
+关键代码事实：
+- `src/agent/task/contracts.py::SampleTarget` 仅含 `leaf_level/leaf_name/category_id/category_path`，不含 data_level。
+- `src/agent/training/common.py::canonical_target`：训练标签唯一来自 `target.category_id`；`classification.level_1..4` 明确 "provenance only, never fallback"。
+- 全仓 grep：`data_level` 在 `src/agent`、`script/verl`、`script/canonical` 中 0 命中；仅 预处理（processor/split/rft_export）、mapping、`script/analysis`（只读统计）触及。
+
+## 3. 各数据集数据来源与已知事实
+
+### finance（信托核心系统）
+- 原始文件：`data/raw/部分金融数据.xlsx`（568 行 ↔ processed 568；按 (table,field) 566/566 data_level 一致）；标准文档 `data/raw/金融行业数据安全分类分级标准指南.xlsx`。
+- 分类体系：标准指南 → `financial_standards_dict.json`（233 条）→ finance corpus 233 / registry（path ID）。
+- data_level 来源：`部分金融数据.xlsx`"数据级别"列（原始 1/2/3/4）；与标准指南"最低安全级别参考"（1级~4级）**一致**：529/568 精确对齐，37 条可经同域相邻类对齐（如 单位联系人信息 L2 ↔ 单位联系信息 2级），仅 2 条离群（AMONEY、HXTRADENO）。
+- data_level 分布：`L1:25 / L2:460 / L3:76 / L4:7`；L4 全部为"个人身份鉴别信息/传统鉴别信息"。
+- field_sensitive：无。等级语义（1~4 级代表什么）：**仓库内无定义文本 → 不猜测**。
+
+### shougang（首钢京唐集团）
+- 原始文件：`data/raw/关基-数据分类分级目录.xlsx`（"首钢京唐数据分类分级目录"，234 叶，含**"分级"列** per-leaf `1/2/3`，分布 1:16/2:170/3:48）；测试子集导出 `data/raw/关基设施…用于测试(1).xlsx`。
+- 分类体系：目录 → `guanji_dict.json`（234 条，**只保留类别、丢掉了"分级"列**）→ shougang corpus 233 / registry（code ID `A1-1-1`）。
+- data_level 来源：目录"分级"列；shougang 已观测的 192 个 leaf category 中，sample `data_level` 与目录对应 category 的"分级"**192/192 一致、零冲突**（32 个目录类别在数据中未出现）。因此该目录是目前可确认的 `standard_data_level` 来源——但 sample 与 standard **概念上不预设必然相等**（见 §1 两个变体）。
+- data_level 分布：`L1:899 / L2:14,188 / L3:4,328`；占位符 `——` 不可训练（1,022 条）。
+- field_sensitive：主训练数据无；仅 64 行的测试导出文件带该列。等级语义：**无定义**（目录只有数字）。
+
+### infra（钢铁基建，= shougang 测试子集）
+- 原始文件：`data/raw/关基设施…用于测试(1).xlsx`（64 行）；逐条 (db,table,field) 与 infra processed **64/64 一致**（含 data_level）；⊂ shougang。data_level 的 `standard_data_level` 来源同样是该目录（经 shougang 复用），不另设独立映射。
+- 分类/registry：同 shougang（registry_source=shougang）。
+- data_level 分布：`L2:18 / L3:46`（原"分级"值 2/3）。
+- field_sensitive：**有**（"字段是否敏感"，是=46 / 否=18，与分级不同步，见 §1）；预处理丢弃。
+- 等级语义：无定义。
+
+### pers_info（高校个人信息）
+- 原始文件：`data/raw/带分级分类的个人基础信息样本190条.xlsx`（189 行 → 176，去重后；176/176 data_level 一致）。
+- 分类体系：单层 `level_4` 18 类（放入 `level_4` 槽位，见 §1）；corpus 来自数据集自身 universe（`build_report.source = null`）。
+- data_level 来源：该 Excel "分级"列直接 `L1/L2/L3`；**仓库无对应标准文档 → 仅有 sample label，无确认的 standard knowledge 来源**。
+- data_level 分布：`L1:31 / L2:98 / L3:47`；1 类跨级（基本信息年级信息和班级信息 L1×1/L2×8）。
+- field_sensitive：无。等级语义：仓库内 `education_dict.json` 为"公开/内部/重要/敏感"四档，与 L1~L3 无法对应且有反例（考核信息=内部数据却标 L3 等）→ 不猜测。
+
+## 4. Phase 0 冻结的核心模型
+
+```text
+classification                 data_level
+  sample label                 sample label
+      ↓                            ↓
+canonical category_id       normalized data_level
+      │                            │
+      └──────────┬─────────────────┘
+                 ↓
+      proposed Stage2 joint target
+         category + data_level
+      （Stage2 contract 待实现 / future target）
+
+
+              原始分类分级标准
+                         │
+                         ↓
+                canonical standard（Phase 1）
+              category_id / description / path
+              standard_data_level
+              grading_rules（若能获得）
+                         │
+                         ↓
+             为模型提供分级规则/参考
+             + 与 sample data_level 审计
+
+
+field_sensitive = 独立 field-level annotation，不属于上述两者
+```
+
+> **最关键的独立分级设计原则**：`standard_data_level` 是 category 的标准参考等级，**不预设其必然等于每个具体字段的最终 `sample_data_level`**；若目标是独立分级，Stage2 **不应直接把候选类别对应的 gold `standard_data_level` 暴露给模型**，否则 data_level 任务会退化为 category→level 查表。
+
+- **Phase 0 冻结**：`data_level` 是什么（sample label 的分级标签）、来自哪里（finance/shougang/infra 可追溯到标准/目录；pers_info 尚无标准来源）、当前代码未训练它（provenance only）。
+- **Phase 0 不决定**：`data_level` 最后训不训练；不把"当前未训练"写成"设计上不训练"。
+- **设计方向（非当前实现，待 Phase 1 起逐步验证）**：从原始分类分级标准构建 canonical standard（含 `standard_data_level`），再与样本 `data_level` 做一致性审计——因此建 standard 时按各源真实分类深度建模，勿把 `level_4` 槽位当作真实标准第四层。
+
+## 5. 目标任务方向与前置缺口
+
+**目标任务方向：优先采用任务形态 B —— 模型独立判断字段安全等级。**
+
+```text
+Stage1:
+field metadata + 全量 leaf categories
+→ Top-5 categories
+
+Stage2:
+field metadata
++ Top-5 category descriptions/examples
++ 领域信息
++ 分级规则/标准说明
+→ category + data_level
+```
+
+其中：
+- `category` 与 `data_level` **都是模型预测目标**。
+- `standard_data_level` 作为标准参考和审计字段保存。
+- **默认不直接把每个候选的 `standard_data_level` 暴露给 Stage2**，否则 data_level 任务会退化为 category→level 查表。
+
+**Baseline A（降级为基线）：lookup-assisted grading**
+向模型提供候选 category 的 `standard_data_level`，用于评估"分类 + 标准读取"任务，并作为 independent grading（Target B）的对照基线。
+
+**Target B：independent grading**
+不直接给候选 gold level，模型依据字段语义、业务上下文和分级规则自行判级。
+
+研究问题由此清晰为：
+- **A：会不会选对标准项？**
+- **B：会不会真正做分级判断？**
+
+**其他未决项**：
+- holdout 缺口：L4 仅 finance 7 条且全在 train；L1 在多数 val/test 缺失 → 分级泛化评估需处理（补充/重抽样/保持冻结）。
+- `field_sensitive` 是否建设为字段级敏感标签，及其与 data_level 的关系定义。
+- 跨数据集 L1~L4 **不推断同义**：finance / pers_info / shougang 无证据表明相同 `L3` 用同一套业务定义，仅 infra=shougang 同源明确。
+- **研究风险（需专门实验验证）**：per-category `data_level` 近确定性映射（跨级样本全库仅 4 条）——若训练集里几乎每个 category 永远只有一个 level，即使目标是独立分级，模型仍可能学成**隐式 `category → level` 查表**而非真正分级规则。后续必须专门设计实验检查：构造同 category 多 level / 跨级样本；并至少跑三组对照——**A lookup-assisted grading vs B independent grading vs B−（对 grading rules 做消融）**——用于区分模型学到的是查表还是真正分级规则。
diff --git a/docs/design/phase1_canonical_standard.md b/docs/design/phase1_canonical_standard.md
new file mode 100644
index 0000000..d4a756e
--- /dev/null
+++ b/docs/design/phase1_canonical_standard.md
@@ -0,0 +1,160 @@
+# Phase 1：无损 canonical standard 层（设计说明 + 迁移 note）
+
+Status: 2026-08-20。只读建立数据事实层，**不改任何现有训练行为**；不推断
+L1–L4 语义；不跨数据集假设同义；不修改样本、prompt、parser、reward、SFT/RL
+parquet；`standard_data_level` 仅作标准参考，绝不覆盖 processed/canonical 的
+sample `data_level`。
+
+相关文件：
+- Phase 0 语义：`docs/design/data_level_design.md`
+- 标准构建实现：`src/agent/standards/`（contracts/sources/build/align）
+- CLI：`script/standard/cli.py` → `python -m script.standard.cli`
+- 产物：`data/standards/*.standard.json`、`artifacts/generated/provenance/`
+
+---
+
+## 1. canonical standard schema
+
+每个 standard entry（`data/standards/<ds>.standard.json` 内，`entries[]`，
+一条原始标准行 = 一个 entry，**事实层不做任何聚合**）：
+
+```jsonc
+{
+  "standard_entry_id": "finance:业务.合约协议.贷款业务信息.基本信息 | A1-1-1",
+  "category_id": "finance:业务.合约协议.基本信息 | A1-1-1",
+  "name": "基本信息",
+  "path": ["业务", "合约协议", "贷款业务信息", "基本信息"],  // 真实源层级深度，空层省略
+  "description": "…",
+  "code": null | "A1-1-1",
+  "standard_data_level": "L1|L2|L3|L4|null",
+  "raw_level": "2 | l | 3 4",
+  "content": "…数据资源说明…",
+  "source": {"file": "…", "sheet": "…", "row": …},
+  "raw_fields": {   // 继承自合并组的事实，带 provenance
+    "level_2_definition": {"value": "…", "source_cell": "D93", "merged_range": "D93:D132", "start_row": 93, "end_row": 132, "inherited": true}
+  }
+}
+```
+
+顶层：`dataset / id_strategy / standard_name / standard_source{file,sheet} /
+fingerprint / entries[] / training_projection / scoped_annotations[]`。
+
+- **`standard_entry_id`** = 原始标准中的**真实身份**（finance 含真实三级子类
+  `finance:{L1}.{L2}.{L3}.{L4}`；shougang = guanji code）。唯一。
+- **`category_id`** = 当前训练/registry 兼容 alias（finance = L1-L2-leaf，与
+  `DatasetConfig.identity_fields` 一致）。**不唯一**：多个标准 entry 可投影到同一
+  训练类别。
+- **`training_projection`** = 派生视图 `{category_id: [standard_entry_id…]}`，
+  把 237 个 finance entry 投影到 233 个训练类别。
+- **`raw_fields`** = entry 从合并组**继承**的层级事实（finance 二级/三级定义；
+  shougang 一级/二级/三级定义 + 数据来源 resource），每项带 `source_cell` /
+  `merged_range` provenance——是网格事实，不是 leaf 私有标签。
+- **`scoped_annotations`** = 网格作用域注解（finance 备注 J、部门意见 K，当前 K 为空）：
+  `{annotation_id, type, text, source_cell, merged_range, start_row, end_row,
+  applies_to_standard_entry_ids}`。合并格是一条源值作用于一个行范围，**绝不复制成
+  单个 leaf 的私有备注**。
+- `fingerprint` = entries（不含 source 行号）+ projection + annotations 的
+  sha256；**与输入顺序无关**（按 entry id / annotation id 排序）。
+
+### 1.1 列语义：hierarchy / leaf / scoped（merged-aware）
+
+| 源 | hierarchy-level（组级合并） | leaf-level（逐行） | scoped annotation |
+| --- | --- | --- | --- |
+| finance | B 一级子类 / C 二级子类 / D 二级定义 / E 三级子类 / F 三级定义 | G 四级子类 / H 内容 / I 安全级别 | J 备注（J55 单行 / J93:J132 40 行 / J168:J169 2 行）、K 部门意见(空) |
+| shougang | B..G 一级分类+定义 / 二级分类+定义 / 三级分类+定义 | H 四级分类 / I 四级定义 / J 数据资源说明 / K 分级 / L 数据来源 | 无备注列 |
+
+- **merged 处理只作用于两个 standard source**（finance 指南、shougang 目录）：
+  `MergedCellResolver` 对任意 cell 返回 value + anchor_cell + merged_range +
+  start/end row + inherited，reader 不再用手工 carry-forward。
+- **三个 sample source（部分金融数据 / 带分级分类… / 关基设施…用于测试）无业务级
+  合并**（`merged_ranges_total=0`；finance 样本仅标题 A1:A2 一条），样本层 zero 改动。
+
+## 2. 各数据集 standard source 状态
+
+| dataset | standard_source | 事实源文件 | 状态 |
+| --- | --- | --- | --- |
+| finance | `finance` | `data/raw/金融行业数据安全分类分级标准指南.xlsx`（sheet Table 1） | built（**237 entries / 233 training categories**） |
+| shougang | `shougang` | `data/raw/关基-数据分类分级目录.xlsx`（sheet 数据分类分级） | built（234 entries） |
+| infra | `shougang`（复用，不复制维护另一套） | — | 复用共享标准（64/64 对齐） |
+| pers_info | `null`（missing / unknown） | 无已确认标准 | **不生成虚假 standard_data_level** |
+
+pers_info：仓库内无确认的分类分级标准；18 类 registry 维持当前
+dataset-derived 行为，但**不伪装成 canonical standard**——不生成
+`pers_info.standard.json`，不在 summary 中编造等级。
+
+## 3. sample ↔ standard 对齐统计（严格 category alias 连接）
+
+| dataset | total | resolved | matched | mismatched | standard_missing | unresolved | resolved_match_rate |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| finance | 568 | 531 | 529 | 2 | 0 | 37 | 99.62% |
+| shougang | 19,415 | 18,393 | 18,393 | 0 | 0 | 1,022 | 100% |
+| infra | 64 | 64 | 64 | 0 | 0 | 0 | 100% |
+| pers_info | — | — | — | — | — | — | 无标准 |
+
+- finance 未解析 37 条附 **`unresolved_evidence`**（evidence-only）：每条含
+  `{status, leaf_name, candidate_standard_categories[]}`（同名校对候选，不修复）；
+  其中 `missing_leaf 34 + path_mismatch 3`。
+- 对齐支持多 entry 类别：sample 等级命中该类别任一 entry 的等级即 matched。
+- 对齐只读：不修改 sample `data_level`，不自动修标签。
+
+## 4. 发现的数据异常（只报告，不修复）
+
+1. **finance 原始标准 2 处不可解析等级**：row 150 `市场营销信息（公开）` 原始值
+   `l`、row 168 `客户及监管相关音影像信息` 原始值 `3 4`。→ `standard_data_level=null`
+   + build issue，不做猜测。
+2. **shougang 目录中"三级即叶子"层级**：10 行 四列为 `——`、叶子码在 三级
+   （合同归并 B1-2、合同跟踪 B1-5 等）。是真实类别，已按真实深度 3 层保存
+   （不发明第 4 层）。
+3. **数据侧 `——` 与目录侧 `——` 语义不同**：样本 `level_4='——'` 是不可训练
+   占位；目录 `——` 是"该层无子结点"。分别处理。
+4. **legacy 信息损失**：finance dict 压平 4 层 path（232/233 类丢 三级 provenance
+   层）；shougang dict 丢全部 path+分级，并**漏掉 B3-6 中厚板作业计划**。
+   canonical standard 均已恢复。
+5. **finance 5 条同训练类别的标准 entry**（业务/合约协议/基本信息 下的 合同通用/
+   贷款业务/中间业务/资金业务/其他支付业务）全部保留，等级一致（2），经
+   `training_projection` 投影为 1 个训练类别——这是低损事实层与训练投影的边界，
+   不是 bug。
+
+## 5. 下一阶段（registry/corpus 接口变化）
+
+当前：`raw standard(Excel) → canonical standard（无损，本文档）→（下一步）LeafRegistry + Corpus`。
+
+下一步需明确：
+1. `src/agent/task/canonical_corpus.py` 改消费 `CanonicalStandard`：按
+   `category_id`（training 投影）建 LeafRegistry，`path` 用标准真实深度；
+   233 类保留、B3-6 是否收养（当前数据 0 样本，纯宇宙完整性）。
+2. `corpus_to_mapping` 是否附带 `standard_data_level`/`standard_entry_id`
+   作为 Stage2 知识——属任务契约决策（Phase 0 §5 A/B），本阶段不预置。
+3. 现有 dict 产物降级为 legacy/derived 审计对照，不再作事实源。
+
+## 6. 测试
+
+```
+tests/standards/
+  test_merged_resolver.py   MergedCellResolver anchor/inherited/range（tmp workbook）
+  test_contracts.py         round-trip（含 raw_fields / scoped_annotations）/ normalize / fingerprint
+  test_build_finance.py     D/F 层级定义继承、J55/J93:J132/J168:J169 作用域、level 异常、确定性
+  test_build_shougang.py    C/E/G 定义 + resource 保留、三级叶、no_code、确定性
+  test_align.py             对齐桶、多 entry 类别、不修改样本、unresolved evidence、路由
+  test_checksum.py          checksum manifest 校验（缺失/不符）
+  test_real_xlsx.py         真实 Excel + canonical 集成断言（raw 缺失时 skip）
+    —— 含：3 个 sample source 无业务级 merged cells；J55=1/J93:J132=40/J168:J169=2
+```
+
+结果：`pytest tests/standards` → **56 passed**；全仓 **298 passed, 2 skipped**
+（skip=本地无 verl，既有）。
+
+产物可重生成：`python -m script.standard.cli`（拒绝无 `--overwrite` 覆盖；
+先行全部构建/对齐、后写盘；重复构建字节级一致）。
+
+### restore / 分发（Blocker-2 选型：B）
+
+- **事实源不进入 Git**：raw workbook 属于数据提供方（含首钢内部目录），gitignored。
+- **受控恢复流程**：从数据提供方/私有 artifact 取回两张表到 `data/raw/` 后，CLI
+  先按 `script/standard/checksums.json`（已入库）校验 sha256，不符即拒绝构建
+  （`--skip-checksum` 供离线调试显式绕过）。
+- **git 边界**：`data/standards/*.standard.json` 仍被 `/data/*` 排除（可再生层，
+  与 processed/canonical 一致）；入库的是 `src/agent/standards/`、`script/standard/`
+  （含 `checksums.json`）、`tests/standards/`、`docs/design/*.md` 与
+  `artifacts/generated/provenance/`。fresh clone 后按 restore 流程即可重建完全一致的
+  事实层（fingerprint 可核对）。
diff --git a/script/standard/__init__.py b/script/standard/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/script/standard/checksums.json b/script/standard/checksums.json
new file mode 100644
index 0000000..f9460dd
--- /dev/null
+++ b/script/standard/checksums.json
@@ -0,0 +1,4 @@
+{
+  "data/raw/金融行业数据安全分类分级标准指南.xlsx": "8af7e04bf6928ad1740e5370e43d61a3b26c0677ead2525cf4198e2c43d54219",
+  "data/raw/关基-数据分类分级目录.xlsx": "32e00ec50573b8de310313486e8acd4d09e7614d1a2195f01c55ec7604d9d078"
+}
diff --git a/script/standard/cli.py b/script/standard/cli.py
new file mode 100644
index 0000000..61fd7f3
--- /dev/null
+++ b/script/standard/cli.py
@@ -0,0 +1,346 @@
+"""Phase 1 canonical standard CLI: build standards + alignment artifacts.
+
+Usage:
+    python -m script.standard.cli [--overwrite] [--skip-checksum]
+
+Reads the ORIGINAL standard workbooks (data/raw), verifies their sha256
+against the committed manifest (script/standard/checksums.json), and builds
+the canonical standards + sample<->standard alignment:
+
+    data/standards/finance.standard.json
+    data/standards/shougang.standard.json
+    artifacts/generated/provenance/finance_standard_alignment.json
+    artifacts/generated/provenance/shougang_standard_alignment.json
+    artifacts/generated/provenance/infra_standard_alignment.json
+    artifacts/generated/provenance/standard_build_summary.json
+
+Distribution (option B): raw workbooks are gitignored and must be restored
+from the data provider first (see docs/design/phase1_canonical_standard.md
+"restore" section). The CLI refuses to build on a missing file and on a
+checksum mismatch (silently building from a wrong file would corrupt the
+standard); --skip-checksum overrides the latter for offline tweaks.
+
+Fail-fast: every dataset is read + built + aligned before anything is
+written; all outputs are written exactly once. Deterministic: JSON is written
+with sort_keys and entries are pre-sorted by standard_entry_id; no timestamps
+or machine-local paths. Raw workbooks are never training dependencies.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import sys
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT / "src"))
+
+from agent.standards.align import align_dataset_to_standard, load_canonical_records
+from agent.standards.build import (
+    build_finance_standard,
+    build_shougang_standard,
+    resolve_standard_dataset,
+)
+from agent.standards.contracts import CanonicalStandard
+from agent.standards.sources import (
+    read_finance_standard_guide,
+    read_guanji_catalog,
+)
+
+DEFAULT_RAW_DIR = PROJECT_ROOT / "data" / "raw"
+DEFAULT_CANONICAL_DIR = PROJECT_ROOT / "data" / "canonical"
+DEFAULT_STANDARD_DIR = PROJECT_ROOT / "data" / "standards"
+DEFAULT_ARTIFACT_DIR = PROJECT_ROOT / "artifacts" / "generated" / "provenance"
+CHECKSUM_MANIFEST = Path(__file__).with_name("checksums.json")
+
+FINANCE_XLSX = "金融行业数据安全分类分级标准指南.xlsx"
+SHOUGANG_XLSX = "关基-数据分类分级目录.xlsx"
+
+
+def _repo_relative(path: str | Path) -> str:
+    try:
+        return Path(path).resolve().relative_to(PROJECT_ROOT.resolve()).as_posix()
+    except ValueError:
+        return Path(path).as_posix()
+
+
+def _write_json(payload, path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8", newline="\n") as handle:
+        json.dump(payload, handle, ensure_ascii=False, indent=2, sort_keys=True)
+        handle.write("\n")
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1 << 20), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _verify_checksums(
+    paths: dict[str, Path],
+    manifest_path: Path,
+    *,
+    allow_skip: bool,
+) -> None:
+    if not manifest_path.is_file():
+        if not allow_skip:
+            raise FileNotFoundError(
+                f"checksum manifest not found: {manifest_path} "
+                "(pass --skip-checksum to proceed without verification)"
+            )
+        return
+    with manifest_path.open(encoding="utf-8") as handle:
+        manifest = json.load(handle)
+    for rel, path in paths.items():
+        expected_raw = manifest.get(rel)
+        if not expected_raw:
+            continue  # file not covered by manifest
+        actual = _sha256(path)
+        if actual != expected_raw:
+            raise ValueError(
+                f"sha256 mismatch for {rel}: got {actual[:16]}… expected "
+                f"{expected_raw[:16]}… — refusing to build from a file that "
+                "differs from the recorded fact source "
+                "(pass --skip-checksum only if you know what you are doing)"
+            )
+
+
+def _registry_ids(dataset: str) -> set[str]:
+    from agent.task import LeafRegistry
+
+    path = PROJECT_ROOT / "cfg" / "task" / "registry" / f"{dataset}.registry.json"
+    return set(LeafRegistry.from_path(path).ids)
+
+
+def _legacy_information_loss(
+    finance_standard: CanonicalStandard,
+    shougang_standard: CanonicalStandard,
+) -> dict[str, object]:
+    """Quantify what the legacy standards_map digests dropped vs the raw
+    standard canonical build (path depth, grading, missing codes)."""
+
+    import json as _json
+
+    finance_out: dict[str, object] = {}
+    finance_ids = {c.category_id for c in finance_standard.categories}
+    finance_reg_ids = _registry_ids("finance")
+    finance_out["standard_vs_registry_ids"] = {
+        "standard_training_categories": len(finance_ids),
+        "registry_ids": len(finance_reg_ids),
+        "missing_from_registry": len(finance_ids - finance_reg_ids),
+    }
+    legacy = _json.load(
+        (
+            PROJECT_ROOT / "data" / "knowledge" / "standards_map"
+            / "financial_standards_dict.json"
+        ).open(encoding="utf-8")
+    )
+    depth_lost = 0
+    depth_kept = 0
+    for entry in finance_standard.categories:
+        segments = 3  # legacy dict identity was L1-L2-leaf
+        if len(entry.path) > segments:
+            depth_lost += 1
+        else:
+            depth_kept += 1
+    finance_out["legacy_dict_path_compression"] = {
+        "entries_with_path_deeper_than_legacy_L1_L2_leaf": depth_lost,
+        "entries_at_legacy_depth": depth_kept,
+        "note": "legacy financial_standards_dict stored L1-L2-leaf identity "
+        "strings; the real standard has 三级子类 provenance nodes that the "
+        "legacy digest dropped (canonical standard keeps every entry, "
+        "standard_entry_id includes the real 三级)",
+    }
+    finance_out["legacy_dict_entries"] = len(legacy)
+    finance_out["legacy_unparseable_level_values"] = sorted(
+        str(v.get("class", "")) for v in legacy.values() if isinstance(v, dict)
+        and v.get("class") in ("l级", "3 4级")
+    )
+
+    shougang_out: dict[str, object] = {}
+    shougang_ids = {c.category_id for c in shougang_standard.categories}
+    shougang_reg_ids = _registry_ids("shougang")
+    shougang_out["standard_vs_registry_codes"] = {
+        "standard_codes": len(shougang_ids),
+        "registry_codes": len(shougang_reg_ids),
+        "standard_only": sorted(shougang_ids - shougang_reg_ids),
+        "registry_only": sorted(shougang_reg_ids - shougang_ids),
+        "note": "B3-6 中厚板作业计划 exists in the raw catalog but was dropped "
+        "by guanji_dict/registry",
+    }
+    legacy_g = _json.load(
+        (
+            PROJECT_ROOT / "data" / "knowledge" / "standards_map" / "guanji_dict.json"
+        ).open(encoding="utf-8")
+    )
+    codes_without_path = 0
+    codes_with_level = 0
+    for entry in shougang_standard.categories:
+        if entry.path:
+            codes_without_path += 1
+        if entry.standard_data_level:
+            codes_with_level += 1
+    shougang_out["legacy_dict_losses"] = {
+        "catalog_entries": len(shougang_standard.categories),
+        "legacy_dict_entries": len(legacy_g),
+        "with_real_path_restored": codes_without_path,
+        "with_grading_restored": codes_with_level,
+        "note": "guanji_dict kept only 'name（code）' + description: real "
+        "hierarchy path and the 分级 column were dropped (registry path was "
+        "[] and no class field existed)",
+    }
+    return {"finance": finance_out, "shougang": shougang_out}
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--raw-dir", type=Path, default=DEFAULT_RAW_DIR)
+    parser.add_argument("--canonical-dir", type=Path, default=DEFAULT_CANONICAL_DIR)
+    parser.add_argument("--standard-dir", type=Path, default=DEFAULT_STANDARD_DIR)
+    parser.add_argument("--artifact-dir", type=Path, default=DEFAULT_ARTIFACT_DIR)
+    parser.add_argument("--overwrite", action="store_true")
+    parser.add_argument(
+        "--skip-checksum",
+        action="store_true",
+        help="build even when the checksum manifest is missing or a file "
+        "mismatches (use only for offline tweaks)",
+    )
+    args = parser.parse_args(argv)
+
+    raw_dir = Path(args.raw_dir)
+    finance_xlsx = raw_dir / FINANCE_XLSX
+    shougang_xlsx = raw_dir / SHOUGANG_XLSX
+    missing = [p for p in (finance_xlsx, shougang_xlsx) if not p.is_file()]
+    if missing:
+        raise FileNotFoundError(
+            "raw standard workbook(s) missing; restore them from the data "
+            "provider first (see docs/design/phase1_canonical_standard.md, "
+            "'restore' section). Missing: "
+            + ", ".join(str(p) for p in missing)
+        )
+    _verify_checksums(
+        {FINANCE_XLSX: finance_xlsx, SHOUGANG_XLSX: shougang_xlsx},
+        CHECKSUM_MANIFEST,
+        allow_skip=args.skip_checksum,
+    )
+
+    # 1. read + build + align EVERYTHING (pure computation, no writes)
+    finance_raw = read_finance_standard_guide(finance_xlsx)
+    shougang_raw = read_guanji_catalog(shougang_xlsx)
+    finance_standard, finance_report = build_finance_standard(
+        finance_raw.entries,
+        source_file=_repo_relative(finance_xlsx),
+        source_sheet="Table 1",
+        reader_issues=finance_raw.issues,
+    )
+    shougang_standard, shougang_report = build_shougang_standard(
+        shougang_raw.entries,
+        source_file=_repo_relative(shougang_xlsx),
+        source_sheet="数据分类分级",
+        reader_issues=shougang_raw.issues,
+    )
+
+    canonical_records = {
+        dataset: load_canonical_records(args.canonical_dir / dataset / "all.json")
+        for dataset in ("finance", "shougang", "infra")
+    }
+    alignments = {
+        "finance": align_dataset_to_standard(
+            canonical_records["finance"], finance_standard
+        ),
+        "shougang": align_dataset_to_standard(
+            canonical_records["shougang"], shougang_standard
+        ),
+        "infra": align_dataset_to_standard(
+            canonical_records["infra"], shougang_standard
+        ),
+    }
+    loss = _legacy_information_loss(finance_standard, shougang_standard)
+
+    # 2. refuse to overwrite without --overwrite
+    outputs = {
+        "standards/finance": args.standard_dir / "finance.standard.json",
+        "standards/shougang": args.standard_dir / "shougang.standard.json",
+        "provenance/finance_alignment": args.artifact_dir / "finance_standard_alignment.json",
+        "provenance/shougang_alignment": args.artifact_dir / "shougang_standard_alignment.json",
+        "provenance/infra_alignment": args.artifact_dir / "infra_standard_alignment.json",
+        "provenance/summary": args.artifact_dir / "standard_build_summary.json",
+    }
+    if not args.overwrite:
+        existing = [str(p) for p in outputs.values() if p.exists()]
+        if existing:
+            raise FileExistsError(
+                "refusing to overwrite existing canonical-standard artifacts: "
+                + ", ".join(existing)
+                + " (pass --overwrite to regenerate)"
+            )
+
+    # 3. build the summary first (fail-fast: nothing is written until every
+    #    computed payload is ready)
+    standards = {
+        "finance": finance_standard,
+        "shougang": shougang_standard,
+    }
+    summary = {
+        "phase": "phase1-canonical-standard",
+        "standards": {
+            dataset: {
+                "standard_source": resolve_standard_dataset(dataset),
+                "status": "built" if resolve_standard_dataset(dataset) else "missing_or_unknown",
+                "standard_entries": (
+                    len(standards[resolve_standard_dataset(dataset)].entries)
+                    if resolve_standard_dataset(dataset) in standards
+                    else 0
+                ),
+                "training_categories": (
+                    standards[resolve_standard_dataset(dataset)].trainable_category_count()
+                    if resolve_standard_dataset(dataset) in standards
+                    else 0
+                ),
+                "build": (
+                    finance_report.to_mapping()
+                    if dataset == "finance"
+                    else shougang_report.to_mapping()
+                    if dataset in ("shougang", "infra")
+                    else None
+                ),
+                "alignment_headline": {
+                    "samples_total": alignments[dataset]["sample_counts"]["total"],
+                    "resolved": alignments[dataset]["sample_counts"]["resolved"],
+                    "matched": alignments[dataset]["sample_counts"]["matched"],
+                    "mismatched": alignments[dataset]["sample_counts"]["mismatched"],
+                    "standard_missing": alignments[dataset]["sample_counts"]["standard_missing"],
+                    "resolved_match_rate": alignments[dataset]["resolved_match_rate"],
+                },
+            }
+            for dataset in ("finance", "shougang", "infra")
+        },
+        "pers_info": {
+            "standard_source": None,
+            "status": "missing_or_unknown",
+            "note": "no confirmed classification/grading standard; the 18-category "
+            "registry remains dataset-derived and is NOT presented as a canonical "
+            "standard; no standard_data_level is fabricated",
+            "alignment_headline": None,
+        },
+        "legacy_information_loss": loss,
+    }
+    # 4. write everything exactly once
+    _write_json(finance_standard.to_mapping(), outputs["standards/finance"])
+    _write_json(shougang_standard.to_mapping(), outputs["standards/shougang"])
+    for key in ("finance", "shougang", "infra"):
+        _write_json(alignments[key], outputs[f"provenance/{key}_alignment"])
+    _write_json(summary, outputs["provenance/summary"])
+
+    for name, path in outputs.items():
+        print(f"wrote: {_repo_relative(path)}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/agent/standards/__init__.py b/src/agent/standards/__init__.py
new file mode 100644
index 0000000..0495e2c
--- /dev/null
+++ b/src/agent/standards/__init__.py
@@ -0,0 +1,64 @@
+"""Canonical standard layer (Phase 1).
+
+Lossless, auditable, reproducible canonical form of the original
+classification/grading standards. Distinct from sample labels: see
+docs/design/data_level_design.md (Phase 0).
+"""
+
+from .contracts import (
+    LEVELS,
+    CanonicalStandard,
+    ScopedAnnotation,
+    SourceRef,
+    StandardCategory,
+    StandardCategoryBuilder,
+    clean,
+    compact,
+    normalize_standard_level,
+    strip_code,
+)
+from .sources import (
+    CellInfo,
+    MergedCellResolver,
+    RawEntry,
+    ReaderResult,
+    read_finance_standard_guide,
+    read_guanji_catalog,
+)
+from .build import (
+    BuildIssue,
+    StandardBuildReport,
+    build_finance_standard,
+    build_shougang_standard,
+    resolve_standard_dataset,
+)
+from .align import (
+    align_dataset_to_standard,
+    load_canonical_records,
+)
+
+__all__ = [
+    "LEVELS",
+    "CanonicalStandard",
+    "ScopedAnnotation",
+    "SourceRef",
+    "StandardCategory",
+    "StandardCategoryBuilder",
+    "clean",
+    "compact",
+    "normalize_standard_level",
+    "strip_code",
+    "CellInfo",
+    "MergedCellResolver",
+    "RawEntry",
+    "ReaderResult",
+    "read_finance_standard_guide",
+    "read_guanji_catalog",
+    "BuildIssue",
+    "StandardBuildReport",
+    "build_finance_standard",
+    "build_shougang_standard",
+    "resolve_standard_dataset",
+    "align_dataset_to_standard",
+    "load_canonical_records",
+]
diff --git a/src/agent/standards/align.py b/src/agent/standards/align.py
new file mode 100644
index 0000000..1fbd34e
--- /dev/null
+++ b/src/agent/standards/align.py
@@ -0,0 +1,197 @@
+"""sample <-> canonical standard alignment audit (Phase 1).
+
+Reads canonical records (data/canonical/<dataset>/all.json — the same records
+the training pipeline consumes, unchanged) and joins them to a
+``CanonicalStandard`` by the training alias ``category_id``.
+
+Buckets (strict alias join):
+- matched                     : sample level is among the entry levels of the category
+- mismatched                  : entries have levels, sample level differs from all of them
+- standard_level_unavailable  : the category's entries have no parseable level
+- standard_missing           : sample category_id not in the standard
+- sample_missing             : standard category observed in no resolved sample
+- unresolved                 : canonical records without a resolved target, with
+                               evidence-only candidate standard entries by leaf name
+
+Pure computation: NEVER modifies sample data_level, never auto-repairs labels,
+never guesses the meaning of a level. ``standard_data_level`` is only the
+standard's category-level reference.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import Counter
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+from agent.standards.contracts import CanonicalStandard
+
+
+def load_canonical_records(path: str | Path) -> list[dict[str, Any]]:
+    with Path(path).open(encoding="utf-8") as handle:
+        records = json.load(handle)
+    if not isinstance(records, list):
+        raise ValueError(f"{path} must be a JSON list")
+    return records
+
+
+def align_dataset_to_standard(
+    records: Sequence[Mapping[str, Any]],
+    standard: CanonicalStandard,
+    *,
+    field_for_audit: str = "field_name",
+) -> dict[str, Any]:
+    """Return a deterministic alignment report (no mutation of ``records``)."""
+    entries_by_category = standard.entries_by_category_id()
+    counts: Counter[str] = Counter()
+    unresolved_by_status: Counter[str] = Counter()
+    mismatched: list[dict[str, Any]] = []
+    standard_missing: list[dict[str, Any]] = []
+    level_unavailable: list[dict[str, Any]] = []
+    unresolved_evidence: list[dict[str, Any]] = []
+    resolved_categories: set[str] = set()
+
+    for record in records:
+        counts["total"] += 1
+        status = str(record.get("resolution_status", "") or "")
+        target = record.get("target")
+        if status != "resolved" or not isinstance(target, Mapping):
+            unresolved_by_status[status or "(no status)"] += 1
+            counts["unresolved"] += 1
+            unresolved_evidence.append(
+                _unresolved_evidence(record, status, standard)
+            )
+            continue
+        category_id = str(target.get("category_id", "") or "")
+        sample_level = str(record.get("data_level", "") or "")
+        counts["resolved"] += 1
+        resolved_categories.add(category_id)
+
+        entries = entries_by_category.get(category_id)
+        if not entries:
+            counts["standard_missing"] += 1
+            standard_missing.append(
+                {
+                    "category_id": category_id,
+                    "name": str(target.get("leaf_name", "") or ""),
+                    "sample_level": sample_level,
+                    "path": list(target.get("category_path") or ()),
+                    "field": _audit_field(record, field_for_audit),
+                }
+            )
+            continue
+        entry_levels = {
+            entry.standard_data_level
+            for entry in entries
+            if entry.standard_data_level is not None
+        }
+        if not entry_levels:
+            counts["standard_level_unavailable"] += 1
+            level_unavailable.append(
+                {
+                    "category_id": category_id,
+                    "name": entries[0].name,
+                    "sample_level": sample_level,
+                    "raw_levels": sorted({e.raw_level for e in entries}),
+                    "field": _audit_field(record, field_for_audit),
+                }
+            )
+            continue
+        if sample_level in entry_levels:
+            counts["matched"] += 1
+        else:
+            counts["mismatched"] += 1
+            mismatched.append(
+                {
+                    "category_id": category_id,
+                    "name": entries[0].name,
+                    "sample_level": sample_level,
+                    "standard_levels": sorted(entry_levels),
+                    "standard_entry_ids": [e.standard_entry_id for e in entries],
+                    "source_rows": [e.source.row for e in entries],
+                    "field": _audit_field(record, field_for_audit),
+                    "sample_path": list(target.get("category_path") or ()),
+                }
+            )
+
+    # standard categories never observed as a resolved sample
+    sample_missing = sorted(set(entries_by_category) - resolved_categories)
+
+    # near-alias candidates for standard-missing samples (leaf-name overlap),
+    # so the audit can distinguish "different standard branch" from "lost"
+    for item in standard_missing:
+        item["near_by_name"] = sorted(
+            {
+                entry.category_id
+                for entry in standard.entries
+                if entry.name == item["name"] and entry.category_id != item["category_id"]
+            }
+        )
+
+    resolved = counts["resolved"]
+    return {
+        "standard": standard.dataset,
+        "standard_entries": len(standard.entries),
+        "training_categories": len(entries_by_category),
+        "training_categories_observed": len(resolved_categories),
+        "training_categories_unobserved": len(sample_missing),
+        "sample_counts": {
+            "total": counts["total"],
+            "resolved": counts["resolved"],
+            "unresolved": counts["unresolved"],
+            "matched": counts["matched"],
+            "mismatched": counts["mismatched"],
+            "standard_missing": counts["standard_missing"],
+            "standard_level_unavailable": counts["standard_level_unavailable"],
+        },
+        "resolved_match_rate": round(counts["matched"] / resolved, 4) if resolved else 0.0,
+        "unresolved_by_status": dict(sorted(unresolved_by_status.items())),
+        "unresolved_evidence": sorted(
+            unresolved_evidence,
+            key=lambda x: (x["status"], x["leaf_name"]),
+        ),
+        "mismatched_samples": sorted(mismatched, key=lambda x: (x["category_id"], x["field"])),
+        "standard_missing_samples": sorted(standard_missing, key=lambda x: x["category_id"]),
+        "standard_level_unavailable_samples": sorted(
+            level_unavailable, key=lambda x: (x["category_id"], x["field"])
+        ),
+        "sample_missing_standard_categories": sample_missing,
+    }
+
+
+def _unresolved_evidence(
+    record: Mapping[str, Any],
+    status: str,
+    standard: CanonicalStandard,
+) -> dict[str, Any]:
+    """Evidence-only: which standard entries share the record's leaf name.
+
+    Aids the alias/near-alignment audit for unresolved samples. Never repairs.
+    """
+    classification = record.get("classification")
+    leaf = ""
+    if isinstance(classification, Mapping):
+        leaf = str(classification.get("level_4", "") or "")
+    candidates = sorted(
+        entry.category_id
+        for entry in standard.entries
+        if leaf and entry.name == leaf
+    )
+    return {
+        "status": status,
+        "leaf_name": leaf,
+        "candidate_standard_categories": candidates,
+    }
+
+
+def _audit_field(record: Mapping[str, Any], field_for_audit: str) -> str:
+    metadata = record.get("metadata")
+    if isinstance(metadata, Mapping):
+        value = metadata.get(field_for_audit, "")
+        if value:
+            return str(value)
+    return ""
+
+
+__all__ = ["load_canonical_records", "align_dataset_to_standard"]
diff --git a/src/agent/standards/build.py b/src/agent/standards/build.py
new file mode 100644
index 0000000..893a401
--- /dev/null
+++ b/src/agent/standards/build.py
@@ -0,0 +1,444 @@
+"""Canonical standard builders (Phase 1).
+
+Turn raw standard rows (from merge-aware ``sources``) into a LOSSESS,
+auditable ``CanonicalStandard``:
+- ONE entry per real standard row — no aggregation in the fact layer.
+  ``standard_entry_id`` is the true source identity; ``category_id`` is the
+  legacy training/registry alias (projection; Phase 2 decides membership).
+- Hierarchy facts that a leaf INHERITS from a merged group (finance 二级/三级
+  定义, shougang 一级/二级/三级 定义, resource) are kept per entry in
+  ``raw_fields`` WITH their source-cell / merged-range provenance.
+- GRID-scoped annotations (finance 备注 J, 部门意见 K) are kept at standard
+  level as ``ScopedAnnotation`` carrying their original merged range and the
+  entry ids they apply to — never copied into a single leaf as private info.
+- Grading columns are normalized L1..L4; unparseable values are reported and
+  kept raw, never fixed. Placeholder / malformed rows (shougang \"——\" with no
+  code, NaN) are skipped and reported; reader-level issues are merged in.
+
+Deterministic: every entry preserved and sorted by standard_entry_id;
+annotations sorted by annotation_id; no reliance on first-seen order.
+"""
+
+from __future__ import annotations
+
+import re
+from collections import Counter, defaultdict
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Iterable, Mapping, Sequence
+
+from agent.task.identity import qualified_category_id
+from agent.standards.contracts import (
+    CanonicalStandard,
+    ScopedAnnotation,
+    SourceRef,
+    StandardCategory,
+    StandardCategoryBuilder,
+    clean,
+    normalize_standard_level,
+    strip_code,
+)
+
+_PLACEHOLDER_NAMES = {"——", "nan", "none", "-", ""}
+_TRAILING_CODE_RE = re.compile(
+    r"[\(\[（【]\s*([A-Za-z]+\d*(?:-\d+)*)\s*[\)\]）】]\s*$"
+)
+
+
+@dataclass(frozen=True)
+class BuildIssue:
+    kind: str
+    detail: str
+
+    def to_mapping(self) -> dict[str, str]:
+        return {"kind": self.kind, "detail": self.detail}
+
+
+@dataclass
+class StandardBuildReport:
+    dataset: str
+    id_strategy: str
+    standard_name: str
+    source_file: str
+    source_sheet: str
+    entries_read: int = 0
+    standard_entries_out: int = 0
+    training_categories: int = 0
+    level_distribution: dict[str, int] = field(default_factory=dict)
+    issues: list[BuildIssue] = field(default_factory=list)
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {
+            "dataset": self.dataset,
+            "id_strategy": self.id_strategy,
+            "standard_name": self.standard_name,
+            "source_file": self.source_file,
+            "source_sheet": self.source_sheet,
+            "entries_read": self.entries_read,
+            "standard_entries_out": self.standard_entries_out,
+            "training_categories": self.training_categories,
+            "level_distribution": dict(self.level_distribution),
+            "issues": sorted(
+                (issue.to_mapping() for issue in self.issues),
+                key=lambda i: (i["kind"], i["detail"]),
+            ),
+        }
+
+
+def _add_issue(report: StandardBuildReport, kind: str, detail: str) -> None:
+    report.issues.append(BuildIssue(kind=kind, detail=detail))
+
+
+def _dedupe_path(parts: Sequence[str]) -> tuple[str, ...]:
+    """Drop empty parts and consecutive duplicates (a leaf living at level 3
+    would otherwise repeat its name in the path)."""
+    result: list[str] = []
+    for part in parts:
+        part = part.strip()
+        if not part:
+            continue
+        if result and result[-1] == part:
+            continue
+        result.append(part)
+    return tuple(result)
+
+
+def _entry_value(entry: Any, key: str) -> Any:
+    if hasattr(entry, key):
+        return getattr(entry, key)
+    if isinstance(entry, Mapping):
+        return entry.get(key)
+    return None
+
+
+def _prov(entry: Any, key: str) -> Mapping[str, Any]:
+    provenance = _entry_value(entry, "provenance") or {}
+    value = provenance.get(key)
+    return value if isinstance(value, Mapping) else {}
+
+
+def _raw_field(entry: Any, key: str):
+    """Build one raw_fields item ``{value, source_cell, merged_range, …}`` for
+    a non-empty inherited hierarchy field, else None."""
+    value = clean(_entry_value(entry, key) or "")
+    if not value:
+        return None
+    info = dict(_prov(entry, key))
+    info.pop("value", None)
+    item = {"value": value}
+    item.update(info)
+    return item
+
+
+def _scoped_annotations(
+    dataset: str,
+    rows: Sequence[tuple[int, str, str, str, str, str, int, int]],
+) -> tuple[ScopedAnnotation, ...]:
+    """Build gold-scope annotations from per-entry annotation sightings.
+
+    Groups sightings by ``(type, text, scope_key)`` where scope_key is the
+    merged range ("J93:J132") — or the cell itself ("J55") for an unmerged
+    cell. Two unmerged cells with identical text are therefore DIFFERENT
+    annotations (each keeps its own row), never merged into one spanning
+    range. ``start_row/end_row`` come from the merged-range provenance or the
+    single cell's own row, never from the observed member subset.
+    """
+    groups: dict[tuple[str, str, str], list[tuple[int, str, str, int, int]]] = defaultdict(list)
+    for row, entry_id, type_, text, source_cell, merged_range, start, end in rows:
+        if not text:
+            continue
+        scope_key = merged_range or source_cell or f"cell-{row}"
+        groups[(type_, text, scope_key)].append((row, entry_id, source_cell, start, end))
+
+    annotations: list[ScopedAnnotation] = []
+    for (type_, text, scope_key), members in groups.items():
+        members.sort(key=lambda m: (m[3] if m[3] is not None else m[0], m[0]))
+        start_rows = [m[3] if m[3] is not None else m[0] for m in members]
+        end_rows = [m[4] if m[4] is not None else m[0] for m in members]
+        start_row = min(start_rows)
+        end_row = max(end_rows)
+        source_cell = members[0][2]
+        # a merged range always contains ':' (e.g. "J93:J132"); a plain cell
+        # ("J55") is not a range -> merged_range=None keeps the single scope
+        merged_range = scope_key if ":" in scope_key else None
+        annotation_id = f"{dataset}-{type_}-{start_row}-{end_row}"
+        annotations.append(
+            ScopedAnnotation(
+                annotation_id=annotation_id,
+                type=type_,
+                text=text,
+                source_cell=source_cell or scope_key,
+                merged_range=merged_range,
+                start_row=start_row,
+                end_row=end_row,
+                applies_to_standard_entry_ids=tuple(
+                    sorted(entry_id for _, entry_id, _, _, _ in members)
+                ),
+            )
+        )
+    ids = [a.annotation_id for a in annotations]
+    if len(set(ids)) != len(ids):
+        raise ValueError(f"duplicate annotation ids (internal grouping error): {ids}")
+    return tuple(sorted(annotations, key=lambda a: a.annotation_id))
+
+
+def _finalize_report(
+    report: StandardBuildReport,
+    entries: Sequence[StandardCategory],
+    reader_issues: Sequence[str],
+) -> None:
+    level_counter: Counter[str] = Counter()
+    for entry in entries:
+        level_counter[entry.standard_data_level or "''"] += 1
+    report.level_distribution = dict(sorted(level_counter.items()))
+    report.training_categories = len({entry.category_id for entry in entries})
+    for detail in reader_issues:
+        report.issues.append(BuildIssue(kind="reader_issue", detail=str(detail)))
+
+
+def build_finance_standard(
+    entries: Sequence[Any],
+    *,
+    source_file: str,
+    source_sheet: str,
+    dataset: str = "finance",
+    standard_name: str = "金融行业数据安全分类分级标准指南",
+    reader_issues: Sequence[str] = (),
+) -> tuple[CanonicalStandard, StandardBuildReport]:
+    """Build the finance canonical standard from merge-aware raw entries.
+
+    standard_entry_id = finance:L1.L2.L3.leaf (true identity incl. 三级);
+    category_id = finance:L1.L2.leaf (training alias). Inherited 二级/三级
+    定义 go to ``raw_fields``; 备注/部门意见 become ``scoped_annotations``.
+    """
+    report = StandardBuildReport(
+        dataset=dataset,
+        id_strategy="path",
+        standard_name=standard_name,
+        source_file=source_file,
+        source_sheet=source_sheet,
+        entries_read=len(entries),
+    )
+    built: list[StandardCategory] = []
+    annotation_rows: list[tuple[int, str, str, str, str, str, int, int]] = []
+    for entry in entries:
+        level_1 = clean(_entry_value(entry, "level_1") or "")
+        level_2 = clean(_entry_value(entry, "level_2") or "")
+        level_3 = clean(_entry_value(entry, "level_3") or "")
+        leaf = clean(_entry_value(entry, "leaf") or "")
+        content = clean(_entry_value(entry, "description") or "")
+        raw_level = str(_entry_value(entry, "raw_level") or "").strip()
+        row = _entry_value(entry, "row")
+
+        if not leaf:
+            _add_issue(report, "empty_leaf_skipped", f"row {row}: empty leaf")
+            continue
+        standard_entry_id = qualified_category_id(dataset, (level_1, level_2, level_3, leaf))
+        category_id = qualified_category_id(dataset, (level_1, level_2, leaf))
+        path = _dedupe_path((level_1, level_2, level_3, leaf))
+        level, raw_clean = normalize_standard_level(raw_level)
+        if raw_level and level is None:
+            _add_issue(
+                report,
+                "level_unparseable",
+                f"row {row} category {leaf!r}: raw level {raw_clean!r} kept as-is, "
+                f"standard_data_level=null (not guessed)",
+            )
+        raw_fields: dict[str, Any] = {}
+        for key in ("level_2_definition", "level_3_definition"):
+            item = _raw_field(entry, key)
+            if item is not None:
+                raw_fields[key] = item
+
+        built.append(
+            StandardCategoryBuilder(
+                standard_entry_id=standard_entry_id,
+                category_id=category_id,
+                name=leaf,
+                path=path,
+                description=content,
+                code=None,
+                raw_level=raw_level,
+                raw_fields=raw_fields,
+                source_file=source_file,
+                source_sheet=source_sheet,
+                source_row=row,
+            ).build()
+        )
+        # scoped-annotation sightings (remark / department opinion)
+        remark = clean(_entry_value(entry, "remark") or "")
+        opinion = clean(_entry_value(entry, "department_opinion") or "")
+        if remark:
+            _push_annotation_sighting(
+                annotation_rows, row, standard_entry_id, "remark", remark,
+                _prov(entry, "remark"),
+            )
+        if opinion:
+            _push_annotation_sighting(
+                annotation_rows, row, standard_entry_id, "department_opinion",
+                opinion, _prov(entry, "department_opinion"),
+            )
+
+    built.sort(key=lambda c: c.standard_entry_id)
+    _assert_unique_entry_ids(built, "finance")
+    report.standard_entries_out = len(built)
+    _finalize_report(report, built, reader_issues)
+    scoped = _scoped_annotations(dataset, annotation_rows)
+    return CanonicalStandard(
+        dataset=dataset,
+        id_strategy="path",
+        standard_source=SourceRef(file=source_file, sheet=source_sheet),
+        standard_name=standard_name,
+        entries=tuple(built),
+        scoped_annotations=scoped,
+    ), report
+
+
+def build_shougang_standard(
+    entries: Sequence[Any],
+    *,
+    source_file: str,
+    source_sheet: str,
+    dataset: str = "shougang",
+    standard_name: str = "首钢京唐数据分类分级目录（关基）",
+    reader_issues: Sequence[str] = (),
+) -> tuple[CanonicalStandard, StandardBuildReport]:
+    """Build the shougang canonical standard from the raw guanji catalog.
+
+    standard_entry_id == category_id == guanji code. Inherited 一级/二级/三级
+    定义 and 数据来源(resource) are kept in ``raw_fields`` with provenance.
+    """
+    report = StandardBuildReport(
+        dataset=dataset,
+        id_strategy="code",
+        standard_name=standard_name,
+        source_file=source_file,
+        source_sheet=source_sheet,
+        entries_read=len(entries),
+    )
+    built: list[StandardCategory] = []
+    for entry in entries:
+        level_1 = clean(_entry_value(entry, "level_1") or "")
+        level_2 = clean(_entry_value(entry, "level_2") or "")
+        level_3 = clean(_entry_value(entry, "level_3") or "")
+        raw_leaf = clean(_entry_value(entry, "leaf") or "")
+        description = clean(_entry_value(entry, "description") or "")
+        content = clean(_entry_value(entry, "content") or "")
+        raw_level = str(_entry_value(entry, "raw_level") or "").strip()
+        row = _entry_value(entry, "row")
+
+        if not raw_leaf or raw_leaf.lower() in _PLACEHOLDER_NAMES or raw_leaf in _PLACEHOLDER_NAMES:
+            raw_leaf = clean(_entry_value(entry, "level_3") or "")
+            if not raw_leaf or raw_leaf in _PLACEHOLDER_NAMES:
+                _add_issue(
+                    report,
+                    "placeholder_skipped",
+                    f"row {row}: no real leaf level (all ——); skipped and reported",
+                )
+                continue
+        match = _TRAILING_CODE_RE.search(raw_leaf)
+        if match:
+            code = match.group(1)
+            name = strip_code(raw_leaf)
+        else:
+            _add_issue(
+                report,
+                "no_code",
+                f"row {row}: leaf {raw_leaf!r} has no category code; skipped "
+                f"(identity is code-based, not guessed)",
+            )
+            continue
+        path = _dedupe_path((strip_code(level_1), strip_code(level_2), strip_code(level_3), name))
+        level, raw_clean = normalize_standard_level(raw_level)
+        if raw_level and level is None:
+            _add_issue(
+                report,
+                "level_unparseable",
+                f"row {row} category {name!r}: raw level {raw_clean!r} kept as-is, "
+                f"standard_data_level=null (not guessed)",
+            )
+        raw_fields: dict[str, Any] = {}
+        for key in ("level_1_definition", "level_2_definition", "level_3_definition", "resource"):
+            item = _raw_field(entry, key)
+            if item is not None:
+                raw_fields[key] = item
+
+        built.append(
+            StandardCategoryBuilder(
+                standard_entry_id=code,
+                category_id=code,
+                name=name,
+                path=path,
+                description=description,
+                code=code,
+                raw_level=raw_level,
+                content=content,
+                raw_fields=raw_fields,
+                source_file=source_file,
+                source_sheet=source_sheet,
+                source_row=row,
+            ).build()
+        )
+    built.sort(key=lambda c: c.standard_entry_id)
+    _assert_unique_entry_ids(built, "shougang")
+    report.standard_entries_out = len(built)
+    _finalize_report(report, built, reader_issues)
+    return CanonicalStandard(
+        dataset=dataset,
+        id_strategy="code",
+        standard_source=SourceRef(file=source_file, sheet=source_sheet),
+        standard_name=standard_name,
+        entries=tuple(built),
+    ), report
+
+
+def _push_annotation_sighting(
+    rows: list[tuple[int, str, str, str, str, str, int, int]],
+    row: int,
+    entry_id: str,
+    type_: str,
+    text: str,
+    prov: Mapping[str, Any],
+) -> None:
+    start = prov.get("start_row")
+    end = prov.get("end_row")
+    rows.append(
+        (
+            row,
+            entry_id,
+            type_,
+            text,
+            str(prov.get("source_cell") or ""),
+            prov.get("merged_range"),
+            int(start) if start is not None else None,
+            int(end) if end is not None else None,
+        )
+    )
+
+
+def _assert_unique_entry_ids(built: Sequence[StandardCategory], dataset: str) -> None:
+    unique = {entry.standard_entry_id for entry in built}
+    if len(unique) != len(built):
+        raise ValueError(
+            f"{dataset} standard_entry_id must be unique; got "
+            f"{len(built) - len(unique)} duplicate(s)"
+        )
+
+
+def resolve_standard_dataset(dataset: str) -> str | None:
+    """Which canonical standard owns a dataset's category facts."""
+    return {
+        "finance": "finance",
+        "shougang": "shougang",
+        "infra": "shougang",
+        "pers_info": None,
+    }[dataset]
+
+
+__all__ = [
+    "BuildIssue",
+    "StandardBuildReport",
+    "build_finance_standard",
+    "build_shougang_standard",
+    "resolve_standard_dataset",
+]
diff --git a/src/agent/standards/contracts.py b/src/agent/standards/contracts.py
new file mode 100644
index 0000000..db6adab
--- /dev/null
+++ b/src/agent/standards/contracts.py
@@ -0,0 +1,385 @@
+"""Canonical standard contracts (Phase 1).
+
+Phase 0 frozen semantics respected here:
+- ``sample_data_level`` (per-field label in processed/canonical) and
+  ``standard_data_level`` (grade the classification/grading standard assigns
+  to a category) are distinct and are NEVER merged or overwritten.
+- ``standard_data_level`` is a category-level reference only; no natural-
+  language semantics of L1..L4 are asserted here, and levels are not assumed
+  equivalent across datasets.
+- ``path`` stores the REAL source-hierarchy depth of the standard; empty
+  levels are omitted (no invented padding).
+- category_id continues the existing stable identity strategy so the
+  standard stays joinable to the current registry/canonical targets.
+
+Determinism: categories are canonicalized by sorted category_id and JSON is
+written with sort_keys; ``fingerprint()`` is a sha256 over the canonical
+categories payload (no timestamps, no machine-local paths).
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import re
+from dataclasses import dataclass, field
+from typing import Any, Iterable, Mapping, Sequence
+
+_WS_COLLAPSE_RE = re.compile(r"\s+")
+_WS_REMOVE_RE = re.compile(r"\s+")
+_TRAILING_CODE_RE = re.compile(
+    r"\s*[\(\[（【]\s*([A-Za-z]+\d*(?:-\d+)*)\s*[\)\]）】]\s*$"
+)
+
+LEVELS = ("L1", "L2", "L3", "L4")
+
+# Aliases accepted when normalizing a raw standard level value. Anything not
+# covered here is kept raw and reported, never guessed.
+_LEVEL_ALIASES: dict[str, str] = {
+    "1": "L1", "2": "L2", "3": "L3", "4": "L4",
+    "L1": "L1", "L2": "L2", "L3": "L3", "L4": "L4",
+    "LEVEL1": "L1", "LEVEL2": "L2", "LEVEL3": "L3", "LEVEL4": "L4",
+    "1级": "L1", "2级": "L2", "3级": "L3", "4级": "L4",
+}
+
+
+def clean(value: Any) -> str:
+    """Collapse every whitespace run to a single space and strip."""
+    if value is None:
+        return ""
+    return _WS_COLLAPSE_RE.sub(" ", str(value).strip())
+
+
+def compact(value: Any) -> str:
+    """Remove every whitespace character (identity seed; matches identity.py)."""
+    if value is None:
+        return ""
+    return _WS_REMOVE_RE.sub("", str(value))
+
+
+def strip_code(text: str) -> str:
+    """Remove a trailing classification code such as （A1-1-1）, (A), 【A】."""
+    return _TRAILING_CODE_RE.sub("", text).strip()
+
+
+def normalize_standard_level(
+    raw: Any,
+) -> tuple[str | None, str]:
+    """Return (canonical L1..L4 | None, cleaned raw value).
+
+    Unparseable values (e.g. 'l', '3 4' from the finance guide) map to None
+    and keep the raw text; callers must report them, never fix or guess.
+    """
+    text = clean(raw)
+    if not text:
+        return None, ""
+    normalized = _LEVEL_ALIASES.get(text.upper())
+    return (normalized, text) if normalized is not None else (None, text)
+
+
+@dataclass(frozen=True)
+class SourceRef:
+    """Traceable origin of one standard category."""
+
+    file: str = ""
+    sheet: str = ""
+    row: int | None = None
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {"file": self.file, "sheet": self.sheet, "row": self.row}
+
+    @classmethod
+    def from_mapping(cls, value: Mapping[str, Any]) -> "SourceRef":
+        return cls(
+            file=str(value.get("file", "") or ""),
+            sheet=str(value.get("sheet", "") or ""),
+            row=value.get("row"),
+        )
+
+
+@dataclass(frozen=True)
+class ScopedAnnotation:
+    """A GRID-scoped annotation from the source standard (e.g. finance 备注).
+
+    A merged cell is a SINGLE source value that applies to a RANGE of rows
+    (and therefore to several standard entries). It must never be copied into
+    one leaf as if it were that leaf's private remark — the original merged
+    scope is preserved here.
+    """
+
+    annotation_id: str
+    type: str  # e.g. "remark" / "department_opinion"
+    text: str
+    source_cell: str
+    merged_range: str | None
+    start_row: int
+    end_row: int
+    applies_to_standard_entry_ids: tuple[str, ...] = ()
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {
+            "annotation_id": self.annotation_id,
+            "type": self.type,
+            "text": self.text,
+            "source_cell": self.source_cell,
+            "merged_range": self.merged_range,
+            "start_row": self.start_row,
+            "end_row": self.end_row,
+            "applies_to_standard_entry_ids": list(self.applies_to_standard_entry_ids),
+        }
+
+    @classmethod
+    def from_mapping(cls, value: Mapping[str, Any]) -> "ScopedAnnotation":
+        return cls(
+            annotation_id=str(value.get("annotation_id", "") or ""),
+            type=str(value.get("type", "") or ""),
+            text=str(value.get("text", "") or ""),
+            source_cell=str(value.get("source_cell", "") or ""),
+            merged_range=value.get("merged_range"),
+            start_row=int(value.get("start_row", 0) or 0),
+            end_row=int(value.get("end_row", 0) or 0),
+            applies_to_standard_entry_ids=tuple(
+                str(item) for item in value.get("applies_to_standard_entry_ids", ())
+            ),
+        )
+
+
+@dataclass(frozen=True)
+class StandardCategory:
+    """One entry of the LOSSESS canonical standard (one raw standard row).
+
+    - standard_entry_id: the TRUE identity of this entry in the raw standard
+      (finance includes the real 三级子类: ``finance:{L1}.{L2}.{L3}.{L4}``;
+      shougang = guanji code). Unique within one standard.
+    - category_id: the legacy training/registry alias (finance L1-L2-leaf =
+      ``DatasetConfig.identity_fields``; shougang = same code). NOT unique —
+      several distinct standard entries may project onto one training
+      category (Phase 2 decision; Phase 1 keeps every entry).
+    - raw_fields: source-specific EXTRA fields (hierarchy definitions,
+      resource) with per-value provenance (source_cell / merged_range). These
+      are grid facts that a leaf inherits from its merged group; they are NOT
+      leaf-private labels. Scoped annotations (remarks) live at standard level.
+    """
+
+    standard_entry_id: str
+    category_id: str
+    name: str
+    path: tuple[str, ...] = ()
+    description: str = ""
+    code: str | None = None
+    standard_data_level: str | None = None
+    raw_level: str = ""
+    content: str = ""
+    source: SourceRef = field(default_factory=SourceRef)
+    raw_fields: Mapping[str, Mapping[str, Any]] = field(default_factory=dict)
+
+    def to_mapping(self) -> dict[str, Any]:
+        mapping: dict[str, Any] = {
+            "standard_entry_id": self.standard_entry_id,
+            "category_id": self.category_id,
+            "name": self.name,
+            "path": list(self.path),
+            "description": self.description,
+            "code": self.code,
+            "standard_data_level": self.standard_data_level,
+            "raw_level": self.raw_level,
+            "source": self.source.to_mapping(),
+        }
+        if self.content:
+            mapping["content"] = self.content
+        if self.raw_fields:
+            mapping["raw_fields"] = {
+                key: dict(item)
+                for key, item in sorted(self.raw_fields.items())
+            }
+        return mapping
+
+    @classmethod
+    def from_mapping(cls, value: Mapping[str, Any]) -> "StandardCategory":
+        source = value.get("source") or {}
+        raw_fields = value.get("raw_fields") or {}
+        return cls(
+            standard_entry_id=str(value.get("standard_entry_id", "") or ""),
+            category_id=str(value.get("category_id", "") or ""),
+            name=str(value.get("name", "") or ""),
+            path=tuple(str(p) for p in value.get("path", ())),
+            description=str(value.get("description", "") or ""),
+            code=value.get("code"),
+            standard_data_level=value.get("standard_data_level"),
+            raw_level=str(value.get("raw_level", "") or ""),
+            content=str(value.get("content", "") or ""),
+            source=SourceRef.from_mapping(
+                source if isinstance(source, Mapping) else {}
+            ),
+            raw_fields=(
+                {str(k): dict(v) for k, v in raw_fields.items()}
+                if isinstance(raw_fields, Mapping)
+                else {}
+            ),
+        )
+
+
+@dataclass(frozen=True)
+class CanonicalStandard:
+    """The LOSSESS canonical standard for one dataset.
+
+    Contains one entry per real standard row (no aggregation); the
+    ``training_projection`` (category_id -> entry ids) is a derived downstream
+    alias view, never a fact-layer collapse.
+    """
+
+    dataset: str
+    id_strategy: str
+    standard_source: SourceRef
+    standard_name: str = ""
+    entries: tuple[StandardCategory, ...] = ()
+    scoped_annotations: tuple[ScopedAnnotation, ...] = ()
+
+    @property
+    def categories(self) -> tuple[StandardCategory, ...]:
+        """Backward-compatible alias: the lossless entries."""
+        return self.entries
+
+    def by_entry_id(self) -> dict[str, StandardCategory]:
+        return {entry.standard_entry_id: entry for entry in self.entries}
+
+    def entries_by_category_id(self) -> dict[str, list[StandardCategory]]:
+        grouped: dict[str, list[StandardCategory]] = {}
+        for entry in self.entries:
+            grouped.setdefault(entry.category_id, []).append(entry)
+        for values in grouped.values():
+            values.sort(key=lambda e: e.standard_entry_id)
+        return grouped
+
+    def training_projection(self) -> dict[str, list[str]]:
+        grouped: dict[str, list[str]] = {}
+        for entry in self.entries:
+            grouped.setdefault(entry.category_id, []).append(entry.standard_entry_id)
+        return {
+            key: sorted(values)
+            for key, values in sorted(grouped.items())
+        }
+
+    def trainable_category_count(self) -> int:
+        """Number of distinct training/registry categories (the 237-entries-of-
+        finance project onto 233 category_ids; this is that projection size)."""
+        return len(self.training_projection())
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {
+            "dataset": self.dataset,
+            "id_strategy": self.id_strategy,
+            "standard_name": self.standard_name,
+            "standard_source": self.standard_source.to_mapping(),
+            "fingerprint": self.fingerprint(),
+            "entries": [entry.to_mapping() for entry in self.entries],
+            "training_projection": self.training_projection(),
+            "scoped_annotations": [
+                annotation.to_mapping()
+                for annotation in sorted(
+                    self.scoped_annotations, key=lambda a: a.annotation_id
+                )
+            ],
+        }
+
+    @classmethod
+    def from_mapping(cls, value: Mapping[str, Any]) -> "CanonicalStandard":
+        raw_entries = value.get("entries", ())
+        entries = tuple(
+            StandardCategory.from_mapping(item)
+            for item in raw_entries
+            if isinstance(item, Mapping)
+        )
+        source = value.get("standard_source") or {}
+        raw_annotations = value.get("scoped_annotations", ())
+        annotations = tuple(
+            ScopedAnnotation.from_mapping(item)
+            for item in raw_annotations
+            if isinstance(item, Mapping)
+        )
+        return cls(
+            dataset=str(value.get("dataset", "") or ""),
+            id_strategy=str(value.get("id_strategy", "") or ""),
+            standard_name=str(value.get("standard_name", "") or ""),
+            standard_source=SourceRef.from_mapping(
+                source if isinstance(source, Mapping) else {}
+            ),
+            entries=entries,
+            scoped_annotations=annotations,
+        )
+
+    def fingerprint(self) -> str:
+        payload = {
+            "dataset": self.dataset,
+            "id_strategy": self.id_strategy,
+            "entries": [
+                {k: v for k, v in entry.to_mapping().items() if k != "source"}
+                for entry in sorted(self.entries, key=lambda e: e.standard_entry_id)
+            ],
+            "training_projection": self.training_projection(),
+            "scoped_annotations": [
+                annotation.to_mapping()
+                for annotation in sorted(
+                    self.scoped_annotations, key=lambda a: a.annotation_id
+                )
+            ],
+        }
+        digest = hashlib.sha256()
+        digest.update(
+            json.dumps(payload, ensure_ascii=False, sort_keys=True).encode("utf-8")
+        )
+        return digest.hexdigest()
+
+
+
+@dataclass(frozen=True)
+class StandardCategoryBuilder:
+    """Deterministic canonical category from raw standard source fields.
+
+    Kept as a small value object so build logic is trivially testable with
+    plain dicts (no Excel, no IO).
+    """
+
+    standard_entry_id: str
+    category_id: str
+    name: str
+    path: tuple[str, ...]
+    description: str = ""
+    code: str | None = None
+    raw_level: str = ""
+    content: str = ""
+    raw_fields: Mapping[str, Mapping[str, Any]] = field(default_factory=dict)
+    source_file: str = ""
+    source_sheet: str = ""
+    source_row: int | None = None
+
+    def build(self) -> StandardCategory:
+        level, _ = normalize_standard_level(self.raw_level)  # level kept, raw kept
+        return StandardCategory(
+            standard_entry_id=self.standard_entry_id,
+            category_id=self.category_id,
+            name=self.name,
+            path=self.path,
+            description=self.description,
+            code=self.code,
+            standard_data_level=level,
+            raw_level=clean(self.raw_level),
+            content=self.content,
+            raw_fields=dict(self.raw_fields),
+            source=SourceRef(
+                file=self.source_file, sheet=self.source_sheet, row=self.source_row
+            ),
+        )
+
+
+__all__ = [
+    "LEVELS",
+    "clean",
+    "compact",
+    "strip_code",
+    "normalize_standard_level",
+    "SourceRef",
+    "StandardCategory",
+    "CanonicalStandard",
+    "StandardCategoryBuilder",
+]
diff --git a/src/agent/standards/sources.py b/src/agent/standards/sources.py
new file mode 100644
index 0000000..f85bd08
--- /dev/null
+++ b/src/agent/standards/sources.py
@@ -0,0 +1,299 @@
+"""Raw standard readers (Phase 1): read the ORIGINAL standard workbooks into
+plain raw-entry dicts — MERGED-RANGE-AWARE.
+
+The standard workbooks use vertical cell merges for GROUP/hierarchy columns:
+finance 金融行业数据安全分类分级标准指南 (B..F hierarchy + J remark, K dept),
+shougang 关基-数据分类分级目录 (B..G hierarchy). A merged cell is ONE source
+value owned by a RANGE of rows; ``MergedCellResolver`` expands it to every
+covered cell while preserving the anchor and scope, so a leaf inherits its
+group's definitions/remark without misattributing them as leaf-private.
+
+Leaf columns (四级子类 / 内容 / 安全级别 / 分级 / 数据资源说明 / 数据来源) are
+per-row cells (no merges). The three SAMPLE-source workbooks (部分金融数据 /
+带分级分类… / 关基设施…用于测试) have no business-level merges and are NOT read
+by this module.
+
+Excel layout (verified against data/raw at 2026-08-20):
+- finance (sheet Table 1): B一级子类 C二级子类 D二级定义 E三级子类 F三级定义
+  G四级子类 H内容 I安全级别 J备注 K部门意见.
+- shougang (sheet 数据分类分级): B一级分类 C一级定义 D二级分类 E二级定义
+  F三级分类(G(三级定义) H四级分类 I四级定义 J数据资源说明 K分级 L数据来源.
+  A "——"/empty 四级 cell means the leaf lives at 三级 (code carried by G).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Mapping
+
+from agent.standards.contracts import clean
+
+FINANCE_SHEET = "Table 1"
+SHOUGANG_SHEET = "数据分类分级"
+
+# finance columns (1-based): letter -> semantic key
+FINANCE_COLS = {
+    "L1": "B", "L2": "C", "L2_DEF": "D", "L3": "E", "L3_DEF": "F",
+    "LEAF": "G", "DESC": "H", "LEVEL": "I", "REMARK": "J", "OPINION": "K",
+}
+SHOUGANG_COLS = {
+    "L1": "B", "L1_DEF": "C", "L2": "D", "L2_DEF": "E", "L3": "F",
+    "L3_DEF": "G", "LEAF": "H", "LEAF_DEF": "I", "CONTENT": "J",
+    "LEVEL": "K", "RESOURCE": "L",
+}
+
+
+@dataclass(frozen=True)
+class CellInfo:
+    """One cell resolved through the merged-grid: value + scope provenance."""
+
+    value: Any = None
+    anchor_cell: str = ""
+    merged_range: str | None = None
+    start_row: int | None = None
+    end_row: int | None = None
+    inherited: bool = False
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {
+            "value": self.value,
+            "anchor_cell": self.anchor_cell,
+            "merged_range": self.merged_range,
+            "start_row": self.start_row,
+            "end_row": self.end_row,
+            "inherited": self.inherited,
+        }
+
+
+class MergedCellResolver:
+    """Resolve any cell of a worksheet with merged-range awareness.
+
+    Non-anchor cells inside a merged range return the ANCHOR's value plus the
+    merged scope (anchor_cell / merged_range / start..end / inherited=True).
+    Plain cells return their own value with inherited=False and no range.
+    """
+
+    def __init__(self, workbook, sheet: str):
+        if sheet not in workbook.sheetnames:
+            raise ValueError(f"sheet {sheet!r} not found")
+        self._sheet = workbook[sheet]
+        self._ranges = list(self._sheet.merged_cells.ranges)
+
+    def close(self) -> None:
+        if self._sheet is not None:
+            pass  # workbook managed by caller
+
+    def cell(self, row: int, col) -> CellInfo:
+        """``col`` is a 1-based integer or a column letter (e.g. 'J')."""
+        from openpyxl.utils import column_index_from_string, get_column_letter
+
+        if isinstance(col, str):
+            col = column_index_from_string(col)
+        cell = self._sheet.cell(row=row, column=col)
+        for rng in self._ranges:
+            if rng.min_row <= row <= rng.max_row and rng.min_col <= col <= rng.max_col:
+                anchor_value = self._sheet.cell(rng.min_row, rng.min_col).value
+                return CellInfo(
+                    value=anchor_value,
+                    anchor_cell=f"{get_column_letter(rng.min_col)}{rng.min_row}",
+                    merged_range=str(rng),
+                    start_row=rng.min_row,
+                    end_row=rng.max_row,
+                    inherited=not (row == rng.min_row and col == rng.min_col),
+                )
+        return CellInfo(
+            value=cell.value,
+            anchor_cell=f"{get_column_letter(col)}{row}",
+            inherited=False,
+        )
+
+
+@dataclass(frozen=True)
+class RawEntry:
+    """One leaf row of a raw standard, with its traceable origin and the
+    hierarchy/annotation fields it INHERITS from merged groups."""
+
+    level_1: str = ""
+    level_2: str = ""
+    level_3: str = ""
+    leaf: str = ""
+    description: str = ""
+    content: str = ""
+    raw_level: str = ""
+    resource: str = ""
+    level_1_definition: str = ""
+    level_2_definition: str = ""
+    level_3_definition: str = ""
+    remark: str = ""
+    department_opinion: str = ""
+    sheet: str = ""
+    row: int | None = None
+    provenance: Mapping[str, Mapping[str, Any]] = field(default_factory=dict)
+
+    def to_mapping(self) -> dict[str, Any]:
+        return {
+            "level_1": self.level_1,
+            "level_2": self.level_2,
+            "level_3": self.level_3,
+            "leaf": self.leaf,
+            "description": self.description,
+            "content": self.content,
+            "raw_level": self.raw_level,
+            "resource": self.resource,
+            "level_1_definition": self.level_1_definition,
+            "level_2_definition": self.level_2_definition,
+            "level_3_definition": self.level_3_definition,
+            "remark": self.remark,
+            "department_opinion": self.department_opinion,
+            "sheet": self.sheet,
+            "row": self.row,
+            "provenance": dict(self.provenance),
+        }
+
+
+@dataclass(frozen=True)
+class ReaderResult:
+    entries: tuple[RawEntry, ...] = ()
+    issues: tuple[str, ...] = ()
+
+
+def _open_xlsx(path: Path):
+    """Load a workbook with cached values (data_only) for merged-range reads."""
+    try:
+        import openpyxl
+    except ImportError as exc:  # pragma: no cover - optional dependency
+        raise RuntimeError("reading raw standard workbooks requires openpyxl") from exc
+    return openpyxl.load_workbook(path, data_only=True)
+
+
+def _prov(cell_info: CellInfo) -> Mapping[str, Any]:
+    return {
+        "source_cell": cell_info.anchor_cell,
+        "merged_range": cell_info.merged_range,
+        "start_row": cell_info.start_row,
+        "end_row": cell_info.end_row,
+        "inherited": cell_info.inherited,
+    }
+
+
+def read_finance_standard_guide(path: str | Path) -> ReaderResult:
+    """Extract leaf rows of the finance grading-standard guide (merge-aware)."""
+    workbook = _open_xlsx(Path(path))
+    resolver = MergedCellResolver(workbook, FINANCE_SHEET)
+    entries: list[RawEntry] = []
+    issues: list[str] = []
+    try:
+        for row in range(3, (resolver._sheet.max_row or 2) + 1):
+            leaf = clean(resolver.cell(row, FINANCE_COLS["LEAF"]).value)
+            if not leaf:
+                continue  # trailing rows / headers; not a leaf
+            l1i = resolver.cell(row, FINANCE_COLS["L1"])
+            l2i = resolver.cell(row, FINANCE_COLS["L2"])
+            l2di = resolver.cell(row, FINANCE_COLS["L2_DEF"])
+            l3i = resolver.cell(row, FINANCE_COLS["L3"])
+            l3di = resolver.cell(row, FINANCE_COLS["L3_DEF"])
+            li = resolver.cell(row, FINANCE_COLS["DESC"])
+            lvi = resolver.cell(row, FINANCE_COLS["LEVEL"])
+            ri = resolver.cell(row, FINANCE_COLS["REMARK"])
+            oi = resolver.cell(row, FINANCE_COLS["OPINION"])
+            entries.append(
+                RawEntry(
+                    level_1=clean(l1i.value),
+                    level_2=clean(l2i.value),
+                    level_3=clean(l3i.value),
+                    leaf=leaf,
+                    description=clean(li.value),
+                    raw_level=str(lvi.value).strip() if lvi.value is not None else "",
+                    level_2_definition=clean(l2di.value),
+                    level_3_definition=clean(l3di.value),
+                    remark=clean(ri.value),
+                    department_opinion=clean(oi.value),
+                    sheet=FINANCE_SHEET,
+                    row=row,
+                    provenance={
+                        "level_2_definition": _prov(l2di),
+                        "level_3_definition": _prov(l3di),
+                        "remark": _prov(ri),
+                        "department_opinion": _prov(oi),
+                    },
+                )
+            )
+    finally:
+        workbook.close()
+    return ReaderResult(tuple(entries), tuple(issues))
+
+
+def read_guanji_catalog(path: str | Path) -> ReaderResult:
+    """Extract leaf rows of the shougang (关基) grading catalog (merge-aware).
+
+    A \"——\"/empty 四级 cell means the leaf lives at 三级 (the code/name is
+    carried by the 三级 column, which is itself a merged group cell).
+    """
+    workbook = _open_xlsx(Path(path))
+    resolver = MergedCellResolver(workbook, SHOUGANG_SHEET)
+    entries: list[RawEntry] = []
+    issues: list[str] = []
+    try:
+        for row in range(3, (resolver._sheet.max_row or 2) + 1):
+            l1i = resolver.cell(row, SHOUGANG_COLS["L1"])
+            l2i = resolver.cell(row, SHOUGANG_COLS["L2"])
+            l3i = resolver.cell(row, SHOUGANG_COLS["L3"])
+            leaf_h = resolver.cell(row, SHOUGANG_COLS["LEAF"])
+            leaf_h_def = resolver.cell(row, SHOUGANG_COLS["LEAF_DEF"])
+            if (leaf_h.value is None or clean(leaf_h.value) in ("", "——")):
+                # leaf sits at 三级: name+code and its definition come from G
+                if l3i.value is None or not clean(l3i.value):
+                    issues.append(f"shougang row {row}: no real leaf level")
+                    continue
+                leaf = clean(l3i.value)
+                description = clean(
+                    resolver.cell(row, SHOUGANG_COLS["L3_DEF"]).value
+                )
+            else:
+                leaf = clean(leaf_h.value)
+                description = clean(leaf_h_def.value)
+            content = clean(resolver.cell(row, SHOUGANG_COLS["CONTENT"]).value)
+            _level_cell = resolver.cell(row, SHOUGANG_COLS["LEVEL"]).value
+            raw_level = str(_level_cell).strip() if _level_cell is not None else ""
+            resource = clean(resolver.cell(row, SHOUGANG_COLS["RESOURCE"]).value)
+            l1di = resolver.cell(row, SHOUGANG_COLS["L1_DEF"])
+            l2di = resolver.cell(row, SHOUGANG_COLS["L2_DEF"])
+            l3di = resolver.cell(row, SHOUGANG_COLS["L3_DEF"])
+            lresi = resolver.cell(row, SHOUGANG_COLS["RESOURCE"])
+            entries.append(
+                RawEntry(
+                    level_1=clean(l1i.value),
+                    level_2=clean(l2i.value),
+                    level_3=clean(l3i.value),
+                    leaf=leaf,
+                    description=description,
+                    content=content,
+                    raw_level=raw_level,
+                    resource=resource,
+                    level_1_definition=clean(l1di.value),
+                    level_2_definition=clean(l2di.value),
+                    level_3_definition=clean(l3di.value),
+                    sheet=SHOUGANG_SHEET,
+                    row=row,
+                    provenance={
+                        "level_1_definition": _prov(l1di),
+                        "level_2_definition": _prov(l2di),
+                        "level_3_definition": _prov(l3di),
+                        "resource": _prov(lresi),
+                    },
+                )
+            )
+    finally:
+        workbook.close()
+    return ReaderResult(tuple(entries), tuple(issues))
+
+
+__all__ = [
+    "CellInfo",
+    "MergedCellResolver",
+    "RawEntry",
+    "ReaderResult",
+    "read_finance_standard_guide",
+    "read_guanji_catalog",
+]
diff --git a/tests/standards/test_align.py b/tests/standards/test_align.py
new file mode 100644
index 0000000..1d5c0c2
--- /dev/null
+++ b/tests/standards/test_align.py
@@ -0,0 +1,121 @@
+"""Phase 1 canonical standard — sample<->standard alignment tests (hermetic)."""
+
+from __future__ import annotations
+
+from agent.standards.align import align_dataset_to_standard
+from agent.standards.build import resolve_standard_dataset
+from agent.standards.contracts import CanonicalStandard, SourceRef, StandardCategory
+
+
+def _standard(entries: list[StandardCategory]) -> CanonicalStandard:
+    return CanonicalStandard(
+        dataset="ds",
+        id_strategy="code",
+        standard_source=SourceRef(),
+        entries=tuple(entries),
+    )
+
+
+def _cat(entry_id: str, level: str, category_id: str | None = None) -> StandardCategory:
+    return StandardCategory(
+        standard_entry_id=entry_id,
+        category_id=category_id or entry_id,
+        name=entry_id,
+        path=(entry_id,),
+        standard_data_level=level,
+        raw_level=level,
+    )
+
+
+def _record(category_id: str | None, level: str, status: str = "resolved", leaf: str | None = None):
+    record = {"data_level": level, "resolution_status": status}
+    if category_id is not None:
+        record["target"] = {
+            "category_id": category_id,
+            "leaf_name": category_id,
+            "category_path": [category_id],
+        }
+    if leaf is not None:
+        record["classification"] = {"level_4": leaf}
+    return record
+
+
+def test_alignment_buckets():
+    standard = _standard(
+        [
+            _cat("A", "L1"),
+            _cat("B", "L3"),
+            _cat("C", "L1"),  # observed in no sample -> sample_missing
+        ]
+    )
+    records = [
+        _record("A", "L1"),                # matched
+        _record("B", "L2"),                # mismatched
+        _record("D", "L3"),                # resolved but standard_missing
+        _record(None, "L2", "placeholder"),  # unresolved
+    ]
+    report = align_dataset_to_standard(records, standard)
+    assert report["sample_counts"]["total"] == 4
+    assert report["sample_counts"]["resolved"] == 3
+    assert report["sample_counts"]["unresolved"] == 1
+    assert report["sample_counts"]["matched"] == 1
+    assert report["sample_counts"]["mismatched"] == 1
+    assert report["sample_counts"]["standard_missing"] == 1
+    assert report["standard_missing_samples"][0]["category_id"] == "D"
+    assert report["sample_missing_standard_categories"] == ["C"]
+
+
+def test_alignment_never_mutates_sample_level():
+    standard = _standard([_cat("A", "L3")])
+    records = [_record("A", "L1")]
+    report = align_dataset_to_standard(records, standard)
+    assert report["sample_counts"]["mismatched"] == 1
+    assert records[0]["data_level"] == "L1"  # untouched
+
+
+def test_alignment_standard_level_unavailable_bucket():
+    standard = CanonicalStandard(
+        dataset="ds", id_strategy="path", standard_source=SourceRef(),
+        entries=(
+            StandardCategory(
+                standard_entry_id="X", category_id="X", name="X",
+                standard_data_level=None, raw_level="3 4",
+            ),
+        ),
+    )
+    report = align_dataset_to_standard([_record("X", "L3")], standard)
+    assert report["sample_counts"]["standard_level_unavailable"] == 1
+    assert report["sample_counts"]["mismatched"] == 0
+
+
+def test_alignment_multi_entry_category_matches_any_entry_level():
+    # one training category backed by two distinct standard entries (L2, L3):
+    # a sample at either level matches; a sample at an absent level mismatches
+    standard = _standard(
+        [
+            _cat("finance:a.中.基本信息", "L2", category_id="finance:a.b.基本信息"),
+            _cat("finance:a.乙.基本信息", "L3", category_id="finance:a.b.基本信息"),
+        ]
+    )
+    report = align_dataset_to_standard(
+        [_record("finance:a.b.基本信息", "L3")], standard
+    )
+    assert report["sample_counts"]["matched"] == 1
+    assert report["sample_counts"]["mismatched"] == 0
+
+
+def test_alignment_unresolved_evidence_is_evidence_only():
+    standard = _standard([_cat("X", "L1", category_id="X")])
+    records = [_record(None, "L2", "path_mismatch", leaf="X")]
+    report = align_dataset_to_standard(records, standard)
+    assert report["sample_counts"]["unresolved"] == 1
+    assert report["unresolved_evidence"][0]["status"] == "path_mismatch"
+    assert report["unresolved_evidence"][0]["leaf_name"] == "X"
+    assert report["unresolved_evidence"][0]["candidate_standard_categories"] == ["X"]
+
+
+def test_dataset_standard_routing():
+    assert resolve_standard_dataset("finance") == "finance"
+    assert resolve_standard_dataset("shougang") == "shougang"
+    assert resolve_standard_dataset("infra") == "shougang"  # shared, not a copy
+    assert resolve_standard_dataset("pers_info") is None  # no confirmed standard
diff --git a/tests/standards/test_build_finance.py b/tests/standards/test_build_finance.py
new file mode 100644
index 0000000..fd548b1
--- /dev/null
+++ b/tests/standards/test_build_finance.py
@@ -0,0 +1,222 @@
+"""Phase 1 canonical standard — finance build tests (dict fixtures, hermetic).
+
+Builders accept plain dict entries ({"level_1","level_2","level_3","leaf",
+"description","content","raw_level","sheet","row"}) with no Excel dependency.
+"""
+
+from __future__ import annotations
+
+from agent.standards.build import build_finance_standard
+
+
+def _entry(**kw):
+    base = dict(sheet="Table 1", row=1, content="", raw_level="")
+    base.update(kw)
+    return base
+
+
+def _by_category(standard):
+    """Find the single entry of a category (helper for fixtures)."""
+    return {entry.category_id: entry for entry in standard.entries}
+
+
+def test_finance_lossless_path_keeps_real_depth_and_no_padding():
+    entries = [
+        _entry(
+            level_1="客户", level_2="个人", level_3="个人自然信息",
+            leaf="个人基本概况信息", description="指个人基本情况数据",
+            raw_level="3", row=3,
+        ),
+        _entry(
+            level_1="业务", level_2="账户信息", level_3="",
+            leaf="基本信息", description="账户基本信息", raw_level="2", row=40,
+        ),
+    ]
+    standard, report = build_finance_standard(
+        entries, source_file="data/raw/g.xlsx", source_sheet="Table 1"
+    )
+    by_category = _by_category(standard)
+    full = by_category["finance:客户.个人.个人基本概况信息"]
+    assert full.path == ("客户", "个人", "个人自然信息", "个人基本概况信息")  # 4 real levels
+    assert full.standard_entry_id == "finance:客户.个人.个人自然信息.个人基本概况信息"
+    assert full.standard_data_level == "L3"
+    assert full.description == "指个人基本情况数据"
+    shallow = by_category["finance:业务.账户信息.基本信息"]
+    assert shallow.path == ("业务", "账户信息", "基本信息")  # empty 三级 omitted, no padding
+    assert shallow.standard_entry_id == "finance:业务.账户信息..基本信息"  # empty slot kept
+    assert shallow.standard_data_level == "L2"
+    assert report.issues == []
+
+
+def test_finance_identical_leaf_under_three_different_level3_kept_as_entries():
+    # The Phase-1 contract keeps every real standard row LOSSESS: two rows with
+    # the same L1/L2/leaf but different 三级子类 stay two entries that share a
+    # training category_id (projection), instead of collapsing to one.
+    entries = [
+        _entry(
+            level_1="客户", level_2="个人", level_3="个人自然信息",
+            leaf="个人基本概况信息", raw_level="3", row=3,
+        ),
+        _entry(
+            level_1="客户", level_2="个人", level_3="个人健康生理信息",
+            leaf="个人基本概况信息", raw_level="4", row=9,
+        ),
+    ]
+    standard, report = build_finance_standard(
+        entries, source_file="f", source_sheet="Table 1"
+    )
+    assert len(standard.entries) == 2
+    assert standard.trainable_category_count() == 1
+    entry_ids = {e.standard_entry_id for e in standard.entries}
+    assert entry_ids == {
+        "finance:客户.个人.个人自然信息.个人基本概况信息",
+        "finance:客户.个人.个人健康生理信息.个人基本概况信息",
+    }
+    paths = {e.path for e in standard.entries}
+    assert ("客户", "个人", "个人自然信息", "个人基本概况信息") in paths
+    assert ("客户", "个人", "个人健康生理信息", "个人基本概况信息") in paths
+    # distinct levels are preserved per entry (no level-conflict collapse)
+    levels = {e.standard_data_level for e in standard.entries}
+    assert levels == {"L3", "L4"}
+    assert report.issues == []
+
+
+def test_finance_unparseable_levels_reported_not_fixed():
+    entries = [
+        _entry(level_1="经营管理", level_2="综合管理", level_3="", leaf="市场营销信息（非公开）", raw_level="l", row=150),
+        _entry(level_1="客户", level_2="个人", level_3="个人基本概况", leaf="个人健康生理影像信息", raw_level="3 4", row=168),
+    ]
+    standard, report = build_finance_standard(
+        entries, source_file="f", source_sheet="Table 1"
+    )
+    for entry in standard.entries:
+        assert entry.standard_data_level is None
+        assert entry.raw_level in ("l", "3 4")
+    kinds = {i.kind for i in report.issues}
+    assert "level_unparseable" in kinds
+    assert len(report.issues) == 2
+
+
+def test_finance_build_deterministic_under_input_shuffle():
+    entries = [
+        _entry(level_1="客户", level_2="个人", level_3="个人自然信息", leaf="个人基本概况信息", raw_level="3", row=3),
+        _entry(level_1="业务", level_2="账户信息", level_3="", leaf="基本信息", raw_level="2", row=40),
+        _entry(level_1="经营管理", level_2="技术管理", level_3="系统管理信息", leaf="配置信息", raw_level="l", row=99),
+    ]
+    a, _ = build_finance_standard(list(entries), source_file="f", source_sheet="Table 1")
+    b, _ = build_finance_standard(list(reversed(entries)), source_file="f", source_sheet="Table 1")
+    assert a.fingerprint() == b.fingerprint()
+    assert [e.standard_entry_id for e in a.entries] == [e.standard_entry_id for e in b.entries]
+    assert a.to_mapping()["entries"] == b.to_mapping()["entries"]
+
+
+def test_finance_deterministic_with_duplicate_category_id_entries():
+    # Same training category, five distinct 三级 entries: order must not matter
+    # (the fact layer keeps every entry, so "first seen" decides nothing).
+    entries_a = [
+        _entry(level_1="业务", level_2="合约协议", level_3=l3, leaf="基本信息", raw_level="2", row=row)
+        for l3, row in [("合同通用信息", 56), ("贷款业务信息", 57), ("中间业务信息", 67), ("资金业务信息", 74), ("其他支付业务信息", 79)]
+    ]
+    entries_b = list(reversed(entries_a))
+    a, airep = build_finance_standard(entries_a, source_file="f", source_sheet="Table 1")
+    b, brep = build_finance_standard(entries_b, source_file="f", source_sheet="Table 1")
+    assert len(a.entries) == 5 == len(b.entries)
+    assert a.trainable_category_count() == 1
+    assert a.fingerprint() == b.fingerprint()
+    # all five paths are preserved (no first-wins collapse)
+    paths = {e.path[-2] for e in a.entries}
+    assert paths == {"合同通用信息", "贷款业务信息", "中间业务信息", "资金业务信息", "其他支付业务信息"}
+    assert airep.standard_entries_out == 5
+
+
+def test_reader_issues_merged_into_build_report():
+    entries = [_entry(level_1="客户", level_2="个人", level_3="个人自然信息", leaf="个人基本概况信息", raw_level="3", row=3)]
+    _, report = build_finance_standard(
+        entries, source_file="f", source_sheet="Table 1",
+        reader_issues=["finance row 999: too few columns"],
+    )
+    issues = report.to_mapping()["issues"]
+    assert any(i["kind"] == "reader_issue" and "row 999" in i["detail"] for i in issues)
+
+
+def _entry_with_hierarchy(levels, leaf, row, **extra):
+    e = _entry(level_1="业务", level_2="金融监管和服务", level_3="反洗钱业务信息",
+               leaf=leaf, raw_level="3", row=row)
+    e.update(extra)
+    return e
+
+
+def test_finance_hierarchy_definitions_kept_in_raw_fields_with_provenance():
+    prov = {
+        "level_2_definition": {"source_cell": "D93", "merged_range": "D93:D132", "start_row": 93, "end_row": 132, "inherited": True},
+        "level_3_definition": {"source_cell": "F93", "merged_range": "F93:F99", "start_row": 93, "end_row": 99, "inherited": True},
+    }
+    entries = [
+        _entry_with_hierarchy(
+            ["业务", "金融监管和服务", "反洗钱业务信息"], "分类考核评级信息", 93,
+            level_2_definition="金融监管和服务域定义",
+            level_3_definition="反洗钱业务定义",
+            provenance=prov,
+        )
+    ]
+    standard, _ = build_finance_standard(entries, source_file="f", source_sheet="Table 1")
+    entry = standard.entries[0]
+    assert entry.raw_fields["level_2_definition"] == {
+        "value": "金融监管和服务域定义", "source_cell": "D93",
+        "merged_range": "D93:D132", "start_row": 93, "end_row": 132, "inherited": True,
+    }
+    assert entry.raw_fields["level_3_definition"]["source_cell"] == "F93"
+    # remark is NOT a leaf-private raw field (it is a scoped annotation)
+    assert "remark" not in entry.raw_fields
+
+
+def test_finance_scoped_annotations_group_by_merged_range():
+    # three sightings: two share the J93:J132 merged range, one is a single cell
+    prov_merged = {"remark": {"source_cell": "J93", "merged_range": "J93:J132", "start_row": 93, "end_row": 132}}
+    prov_single = {"remark": {"source_cell": "J55", "merged_range": None, "start_row": 55, "end_row": 55}}
+    entries = [
+        _entry_with_hierarchy(["业务", "金融监管和服务", "反洗钱业务信息"], "分类考核评级信息", 93, remark="宜从高设置", provenance=prov_merged),
+        _entry_with_hierarchy(["业务", "金融监管和服务", "反洗钱业务信息"], "行政监管信息", 100, remark="宜从高设置", provenance=prov_merged),
+        _entry(level_1="客户", level_2="个人", level_3="个人身份鉴别信息", leaf="特有账户信息", remark="宜从高设置", provenance=prov_single, row=55),
+    ]
+    standard, _ = build_finance_standard(entries, source_file="f", source_sheet="Table 1")
+    assert len(standard.scoped_annotations) == 2
+    by_id = {a.annotation_id: a for a in standard.scoped_annotations}
+    merged = by_id["finance-remark-93-132"]
+    assert merged.merged_range == "J93:J132"
+    assert merged.start_row == 93 and merged.end_row == 132
+    assert len(merged.applies_to_standard_entry_ids) == 2
+    single = by_id["finance-remark-55-55"]
+    assert single.merged_range is None
+    assert len(single.applies_to_standard_entry_ids) == 1
+
+
+def test_finance_empty_department_opinion_produces_no_annotation():
+    entries = [_entry(level_1="客户", level_2="个人", level_3="个人身份鉴别信息", leaf="特有账户信息", row=55)]
+    standard, _ = build_finance_standard(entries, source_file="f", source_sheet="Table 1")
+    assert standard.scoped_annotations == ()
+
+
+def test_finance_identical_text_in_two_unmerged_cells_is_two_annotations():
+    # regression: J5 and J10 both carry the same text but are NOT merged; they
+    # must stay two separate scoped annotations (each its own row), never one
+    # annotation spanning rows 5..10.
+    def sighting(row):
+        return _entry(
+            level_1="客户", level_2="个人", level_3="个人自然信息",
+            leaf="个人基本概况信息" if row == 5 else "个人财产信息",
+            raw_level="3", row=row,
+            remark="相同的备注文字",
+            provenance={"remark": {"source_cell": "J%d" % row, "merged_range": None,
+                                    "start_row": row, "end_row": row}},
+        )
+
+    standard, _ = build_finance_standard(
+        [sighting(5), sighting(10)], source_file="f", source_sheet="Table 1"
+    )
+    by_id = {a.annotation_id: a for a in standard.scoped_annotations}
+    assert set(by_id) == {"finance-remark-5-5", "finance-remark-10-10"}
+    assert by_id["finance-remark-5-5"].merged_range is None
+    assert by_id["finance-remark-5-5"].start_row == 5
+    assert by_id["finance-remark-10-10"].start_row == 10
+    assert len(standard.scoped_annotations) == 2
diff --git a/tests/standards/test_build_shougang.py b/tests/standards/test_build_shougang.py
new file mode 100644
index 0000000..d65acd8
--- /dev/null
+++ b/tests/standards/test_build_shougang.py
@@ -0,0 +1,141 @@
+"""Phase 1 canonical standard — shougang build tests (dict fixtures, hermetic)."""
+
+from __future__ import annotations
+
+from agent.standards.build import build_shougang_standard
+
+
+def _entry(**kw):
+    base = dict(sheet="数据分类分级", row=1, content="", raw_level="", resource="")
+    base.update(kw)
+    return base
+
+
+def test_shougang_code_name_path_level():
+    entries = [
+        _entry(
+            level_1="研发数据域（A）", level_2="产品研发（A1）", level_3="科研检验（A1-1）",
+            leaf="科研设备预约管理（A1-1-1）", description="指设备预约",
+            content="设备预约信息", raw_level="2", row=7,
+        )
+    ]
+    standard, report = build_shougang_standard(
+        entries, source_file="data/raw/c.xlsx", source_sheet="数据分类分级"
+    )
+    entry = standard.entries[0]
+    assert entry.standard_entry_id == "A1-1-1"
+    assert entry.category_id == "A1-1-1"
+    assert entry.code == "A1-1-1"
+    assert entry.name == "科研设备预约管理"
+    assert entry.path == ("研发数据域", "产品研发", "科研检验", "科研设备预约管理")
+    assert entry.description == "指设备预约"
+    assert entry.content == "设备预约信息"
+    assert entry.standard_data_level == "L2"
+    assert report.issues == []
+
+
+def test_shougang_leaf_at_level_three_no_invented_fourth_level():
+    # catalog encodes 三级-level leaves with a literal "——" in the 四级 cell
+    entries = [
+        _entry(
+            level_1="生产数据域（B）", level_2="生产合同（订单）（B1）",
+            level_3="合同归并（B1-2）", leaf="——",
+            description="指按照合同加工途径", raw_level="3", row=9,
+        ),
+        _entry(
+            level_1="管理数据域（C）", level_2="生产质量管理（C1）",
+            level_3="质保书管理（C1-5）", leaf="——", raw_level="1", row=108,
+        ),
+    ]
+    standard, report = build_shougang_standard(
+        entries, source_file="c", source_sheet="数据分类分级"
+    )
+    by_id = standard.by_entry_id()
+    merged = by_id["B1-2"]
+    assert merged.name == "合同归并"
+    # real hierarchy depth is 3; the "——" marker does NOT become a 4th level
+    assert merged.path == ("生产数据域", "生产合同（订单）", "合同归并")
+    assert merged.standard_data_level == "L3"
+    assert by_id["C1-5"].standard_data_level == "L1"
+
+
+def test_shougang_no_code_real_leaf_reported_and_skipped():
+    entries = [
+        _entry(level_1="管理数据域（C）", level_2="经营管理（C8）", level_3="综合管理",
+               leaf="无法归类的真实名称", raw_level="2", row=90),
+    ]
+    standard, report = build_shougang_standard(
+        entries, source_file="c", source_sheet="数据分类分级"
+    )
+    assert len(standard.entries) == 0
+    assert any(issue.kind == "no_code" for issue in report.issues)
+
+
+def test_shougang_build_deterministic_under_input_shuffle():
+    entries = [
+        _entry(level_1="研发数据域（A）", level_2="产品研发（A1）", level_3="科研检验（A1-1）", leaf="科研设备预约管理（A1-1-1）", raw_level="2", row=7),
+        _entry(level_1="生产数据域（B）", level_2="生产合同（订单）（B1）", level_3="合同归并（B1-2）", leaf="——", raw_level="3", row=9),
+        _entry(level_1="管理数据域（C）", level_2="生产质量管理（C1）", level_3="检化验管理（C1-4）", leaf="认证管理（C1-4-4）", raw_level="3", row=105),
+        _entry(level_1="管理数据域（C）", level_2="经营管理（C8）", level_3="无码", leaf="无码类别", raw_level="2", row=90),
+    ]
+    a, _ = build_shougang_standard(list(entries), source_file="c", source_sheet="数据分类分级")
+    b, _ = build_shougang_standard(list(reversed(entries)), source_file="c", source_sheet="数据分类分级")
+    assert a.fingerprint() == b.fingerprint()
+    assert [e.standard_entry_id for e in a.entries] == [e.standard_entry_id for e in b.entries]
+
+
+def test_reader_issues_merged_into_build_report():
+    entries = [_entry(level_1="研发数据域（A）", level_2="产品研发（A1）", level_3="科研检验（A1-1）", leaf="科研设备预约管理（A1-1-1）", raw_level="2", row=7)]
+    _, report = build_shougang_standard(
+        entries, source_file="c", source_sheet="数据分类分级",
+        reader_issues=["shougang row 7: too few columns"],
+    )
+    issues = report.to_mapping()["issues"]
+    assert any(i["kind"] == "reader_issue" and "row 7" in i["detail"] for i in issues)
+
+
+def test_shougang_hierarchy_definitions_and_resource_preserved():
+    prov = {
+        "level_1_definition": {"source_cell": "C3", "merged_range": "C3:C6"},
+        "level_2_definition": {"source_cell": "E3", "merged_range": "E3:E4"},
+        "level_3_definition": {"source_cell": "G3", "merged_range": "G3:G3"},
+        "resource": {"source_cell": "L3", "merged_range": None, "start_row": None, "end_row": None, "inherited": False},
+    }
+    entries = [
+        _entry(
+            level_1="研发数据域（A）", level_2="产品研发（A1）", level_3="科研检验（A1-1）",
+            leaf="科研设备预约管理（A1-1-1）", raw_level="2", row=3,
+            level_1_definition="研发域定义", level_2_definition="产品研发定义",
+            level_3_definition="科研检验定义", resource="科研实验室",
+            provenance=prov,
+        )
+    ]
+    standard, _ = build_shougang_standard(entries, source_file="c", source_sheet="数据分类分级")
+    entry = standard.entries[0]
+    assert entry.raw_fields["level_1_definition"]["source_cell"] == "C3"
+    assert entry.raw_fields["level_2_definition"]["merged_range"] == "E3:E4"
+    assert entry.raw_fields["level_3_definition"]["value"] == "科研检验定义"
+    assert entry.raw_fields["resource"] == {
+        "value": "科研实验室", "source_cell": "L3", "merged_range": None,
+        "start_row": None, "end_row": None, "inherited": False,
+    }
+
+
+def test_shougang_level_three_leaf_keeps_inherited_definitions_and_resource():
+    entries = [
+        _entry(
+            level_1="生产数据域（B）", level_2="生产合同（订单）（B1）",
+            level_3="合同归并（B1-2）", leaf="——", raw_level="3", row=9,
+            level_1_definition="生产域定义", level_2_definition="合同域定义",
+            level_3_definition="合同归并定义", resource="制造管理系统",
+            provenance={
+                "level_1_definition": {"source_cell": "C7", "merged_range": "C7:C82"},
+                "resource": {"source_cell": "L9", "merged_range": None},
+            },
+        )
+    ]
+    standard, _ = build_shougang_standard(entries, source_file="c", source_sheet="数据分类分级")
+    entry = standard.by_entry_id()["B1-2"]
+    assert entry.raw_fields["level_1_definition"]["value"] == "生产域定义"
+    assert entry.raw_fields["resource"]["value"] == "制造管理系统"
+    assert entry.raw_fields["level_3_definition"]["value"] == "合同归并定义"
diff --git a/tests/standards/test_checksum.py b/tests/standards/test_checksum.py
new file mode 100644
index 0000000..0bf16a1
--- /dev/null
+++ b/tests/standards/test_checksum.py
@@ -0,0 +1,51 @@
+"""Phase 1 canonical standard — raw-source checksum verification (hermetic).
+
+Covers Blocker-2 option B: CLI refuses to build from a missing manifest /
+mismatched file, so a silently wrong raw workbook can never produce a
+"canonical" standard. Uses tmp files only (no repo data dependency).
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+
+import pytest
+
+from script.standard.cli import _verify_checksums
+
+
+def _write(path, text: bytes):
+    path.write_bytes(text)
+    return hashlib.sha256(text).hexdigest()
+
+
+def test_checksum_mismatch_raises(tmp_path):
+    manifest = tmp_path / "checksums.json"
+    good = tmp_path / "a.xlsx"
+    expected = _write(good, b"A" * 100)
+
+    other = tmp_path / "b.xlsx"
+    _write(other, b"B" * 100)
+    manifest.write_text(json.dumps({"a.xlsx": expected}), encoding="utf-8")
+
+    with pytest.raises(ValueError, match="sha256 mismatch"):
+        _verify_checksums({"a.xlsx": other}, manifest, allow_skip=False)
+
+
+def test_checksum_match_passes(tmp_path):
+    manifest = tmp_path / "checksums.json"
+    good = tmp_path / "a.xlsx"
+    expected = _write(good, b"A" * 100)
+    manifest.write_text(json.dumps({"a.xlsx": expected}), encoding="utf-8")
+    _verify_checksums({"a.xlsx": good}, manifest, allow_skip=False)  # no raise
+
+
+def test_checksum_missing_manifest_requires_override(tmp_path):
+    manifest = tmp_path / "checksums.json"  # deliberately absent
+    good = tmp_path / "a.xlsx"
+    _write(good, b"A" * 100)
+    with pytest.raises(FileNotFoundError, match="checksum manifest not found"):
+        _verify_checksums({"a.xlsx": good}, manifest, allow_skip=False)
+    # explicit override is allowed for offline tweaks
+    _verify_checksums({"a.xlsx": good}, manifest, allow_skip=True)
diff --git a/tests/standards/test_contracts.py b/tests/standards/test_contracts.py
new file mode 100644
index 0000000..3c79949
--- /dev/null
+++ b/tests/standards/test_contracts.py
@@ -0,0 +1,160 @@
+"""Phase 1 canonical standard — contract unit tests (hermetic, no IO / Excel)."""
+
+from __future__ import annotations
+
+import copy
+
+import pytest
+
+from agent.standards.contracts import (
+    CanonicalStandard,
+    SourceRef,
+    StandardCategory,
+    compact,
+    normalize_standard_level,
+    strip_code,
+)
+
+
+def _category(entry_id: str, level: str | None = None, row: int = 1, **kw) -> StandardCategory:
+    return StandardCategory(
+        standard_entry_id=entry_id,
+        category_id=kw.get("category_id", entry_id),
+        name=kw.get("name", entry_id),
+        path=kw.get("path", (entry_id,)),
+        standard_data_level=level,
+        raw_level=level or "",
+        source=SourceRef(file="f.xlsx", sheet="s", row=row),
+    )
+
+
+def test_normalize_standard_level_valid_forms():
+    for raw, expected in {
+        "1": "L1", "2": "L2", "3": "L3", "4": "L4",
+        "L1": "L1", "LEVEL3": "L3", "1级": "L1", "4级": "L4",
+    }.items():
+        level, kept = normalize_standard_level(raw)
+        assert level == expected
+        assert kept == raw.strip()
+    assert normalize_standard_level(None) == (None, "")
+    assert normalize_standard_level("") == (None, "")
+
+
+def test_normalize_standard_level_unparseable_never_guessed():
+    # finance guide anomalies: 'l' (typo of 1) and '3 4' (ambiguous) must stay
+    # None and keep the raw text — the builder reports them, never fixes.
+    for raw in ("l", "3 4", "敏感级", "高"):
+        level, kept = normalize_standard_level(raw)
+        assert level is None
+        assert kept == raw
+
+
+def test_compact_and_strip_code():
+    assert compact("经营 管理　技术") == "经营管理技术"
+    assert strip_code("科研设备预约管理（A1-1-1）") == "科研设备预约管理"
+    assert strip_code("生产数据域\n（B）") == "生产数据域"
+    assert strip_code("基本信息（公开）") == "基本信息（公开）"  # parens without code kept
+
+
+def test_standard_round_trip_stable():
+    standard = CanonicalStandard(
+        dataset="finance",
+        id_strategy="path",
+        standard_name="指南",
+        standard_source=SourceRef(file="data/raw/f.xlsx", sheet="Table 1", row=None),
+        entries=(
+            _category("finance:业务.交易信息.交易通用信息.交易基本信息", "L2"),
+            _category("finance:业务.账户信息..基本信息", "L1"),
+        ),
+    )
+    mapping = standard.to_mapping()
+    mapping["entries"] = list(reversed(mapping["entries"]))  # scramble order
+    rebuilt = CanonicalStandard.from_mapping(mapping)
+    assert rebuilt.dataset == standard.dataset
+    assert rebuilt.id_strategy == standard.id_strategy
+    assert rebuilt.fingerprint() == standard.fingerprint()
+
+
+def test_round_trip_preserves_level_and_source():
+    entry = StandardCategory(
+        standard_entry_id="A1-1-1",
+        category_id="A1-1-1",
+        name="科研设备预约管理",
+        path=("研发数据域", "产品研发", "科研检验", "科研设备预约管理"),
+        description="描述",
+        code="A1-1-1",
+        standard_data_level="L3",
+        raw_level="3",
+        content="资源说明",
+        source=SourceRef(file="x.xlsx", sheet="数据分类分级", row=7),
+        raw_fields={
+            "level_3_definition": {
+                "value": "科研检验定义", "source_cell": "G7", "merged_range": None,
+            }
+        },
+    )
+    rebuilt = StandardCategory.from_mapping(copy.deepcopy(entry.to_mapping()))
+    assert rebuilt == entry
+
+
+def test_round_trip_preserves_scoped_annotations():
+    from agent.standards.contracts import ScopedAnnotation
+
+    annotation = ScopedAnnotation(
+        annotation_id="finance-remark-93-132",
+        type="remark",
+        text="鉴于其特殊性",
+        source_cell="J93",
+        merged_range="J93:J132",
+        start_row=93,
+        end_row=132,
+        applies_to_standard_entry_ids=("a", "b"),
+    )
+    standard = CanonicalStandard(
+        dataset="finance", id_strategy="path", standard_source=SourceRef(),
+        entries=(_category("a", "L2"), _category("b", "L3")),
+        scoped_annotations=(annotation,),
+    )
+    mapping = standard.to_mapping()
+    rebuilt = CanonicalStandard.from_mapping(copy.deepcopy(mapping))
+    assert rebuilt.scoped_annotations == (annotation,)
+    assert rebuilt.fingerprint() == standard.fingerprint()
+
+
+def test_fingerprint_includes_raw_fields_and_annotations():
+    a = _category("A", "L1")
+    b = StandardCategory(
+        standard_entry_id="A", category_id="A", name="A", path=("A",),
+        raw_fields={"level_2_definition": {"value": "x", "source_cell": "D2"}},
+    )
+    sa = CanonicalStandard("s", "code", standard_source=SourceRef(), entries=(a,))
+    sb = CanonicalStandard("s", "code", standard_source=SourceRef(), entries=(b,))
+    assert sa.fingerprint() != sb.fingerprint()  # raw_fields are content, not source
+
+
+def test_fingerprint_independent_of_source_rows():
+    a = _category("A", "L1", row=1)
+    b = _category("A", "L1", row=99)  # same content, different source row
+    sa = CanonicalStandard("s", "code", standard_source=SourceRef(), entries=(a,))
+    sb = CanonicalStandard("s", "code", standard_source=SourceRef(), entries=(b,))
+    assert sa.fingerprint() == sb.fingerprint()
+
+
+def test_training_projection_groups_entries_by_category_id():
+    standard = CanonicalStandard(
+        dataset="finance",
+        id_strategy="path",
+        standard_source=SourceRef(),
+        entries=(
+            _category("finance:业务.合约协议.合同通用信息.基本信息", "L2", category_id="finance:业务.合约协议.基本信息"),
+            _category("finance:业务.合约协议.贷款业务信息.基本信息", "L2", category_id="finance:业务.合约协议.基本信息"),
+            _category("finance:业务.交易信息.交易通用信息.交易基本信息", "L2", category_id="finance:业务.交易信息.交易基本信息"),
+        ),
+    )
+    projection = standard.training_projection()
+    assert standard.trainable_category_count() == 2
+    assert projection["finance:业务.合约协议.基本信息"] == [
+        "finance:业务.合约协议.合同通用信息.基本信息",
+        "finance:业务.合约协议.贷款业务信息.基本信息",
+    ]
+    assert len(standard.entries_by_category_id()["finance:业务.合约协议.基本信息"]) == 2
diff --git a/tests/standards/test_merged_resolver.py b/tests/standards/test_merged_resolver.py
new file mode 100644
index 0000000..6e72488
--- /dev/null
+++ b/tests/standards/test_merged_resolver.py
@@ -0,0 +1,64 @@
+"""Phase 1 canonical standard — MergedCellResolver unit tests (hermetic).
+
+Builds a tiny workbook on the fly, so these tests need openpyxl but no repo
+data (skipped when openpyxl is missing).
+"""
+
+from __future__ import annotations
+
+import pytest
+
+openpyxl = pytest.importorskip("openpyxl")
+
+from agent.standards.sources import MergedCellResolver  # noqa: E402
+
+
+@pytest.fixture()
+def wb(tmp_path):
+    """A sheet with one vertical merge B2:B4 and one plain cell C2."""
+    path = tmp_path / "t.xlsx"
+    workbook = openpyxl.Workbook()
+    ws = workbook.active
+    ws.title = "S"
+    ws["B2"] = "group-value"
+    ws["C2"] = "plain-value"
+    ws["C3"] = "plain-other"
+    ws.merge_cells("B2:B4")
+    workbook.save(path)
+    workbook.close()
+    return path
+
+
+def test_anchor_cell(wb):
+    resolver = MergedCellResolver(openpyxl.load_workbook(wb, data_only=True), "S")
+    info = resolver.cell(2, "B")
+    assert info.value == "group-value"
+    assert info.anchor_cell == "B2"
+    assert info.merged_range == "B2:B4"
+    assert info.start_row == 2 and info.end_row == 4
+    assert info.inherited is False
+    assert info.to_mapping()["value"] == "group-value"
+
+
+def test_inherited_cell(wb):
+    resolver = MergedCellResolver(openpyxl.load_workbook(wb, data_only=True), "S")
+    info = resolver.cell(4, "B")  # non-anchor inside the merge
+    assert info.value == "group-value"  # anchor value expanded
+    assert info.anchor_cell == "B2"
+    assert info.merged_range == "B2:B4"
+    assert info.inherited is True
+
+
+def test_plain_cell(wb):
+    resolver = MergedCellResolver(openpyxl.load_workbook(wb, data_only=True), "S")
+    info = resolver.cell(2, "C")
+    assert info.value == "plain-value"
+    assert info.anchor_cell == "C2"
+    assert info.merged_range is None
+    assert info.inherited is False
+    assert info.start_row is None
+
+
+def test_column_letter_or_index_accepted(wb):
+    resolver = MergedCellResolver(openpyxl.load_workbook(wb, data_only=True), "S")
+    assert resolver.cell(2, "B").value == resolver.cell(2, 2).value == "group-value"
diff --git a/tests/standards/test_real_xlsx.py b/tests/standards/test_real_xlsx.py
new file mode 100644
index 0000000..b05495b
--- /dev/null
+++ b/tests/standards/test_real_xlsx.py
@@ -0,0 +1,224 @@
+"""Phase 1 canonical standard — integration tests against real raw workbooks.
+
+These read the ORIGINAL standard workbooks under data/raw (gitignored) and the
+canonical dataset layer. They are skipped when the raw files are absent (CI /
+fresh clone) so the suite stays green without the data provider's files.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from agent.standards.align import align_dataset_to_standard, load_canonical_records
+from agent.standards.build import (
+    build_finance_standard,
+    build_shougang_standard,
+    resolve_standard_dataset,
+)
+from agent.standards.sources import (
+    read_finance_standard_guide,
+    read_guanji_catalog,
+)
+from agent.task import LeafRegistry
+
+ROOT = Path(__file__).resolve().parents[2]
+RAW = ROOT / "data" / "raw"
+FIN_XLSX = RAW / "金融行业数据安全分类分级标准指南.xlsx"
+SHG_XLSX = RAW / "关基-数据分类分级目录.xlsx"
+CANON = ROOT / "data" / "canonical"
+REG = ROOT / "cfg" / "task" / "registry"
+
+pytestmark = pytest.mark.skipif(
+    not (FIN_XLSX.is_file() and SHG_XLSX.is_file()),
+    reason="raw standard workbooks not present (data/raw is gitignored)",
+)
+
+
+@pytest.fixture(scope="module")
+def built():
+    finance_raw = read_finance_standard_guide(FIN_XLSX)
+    shougang_raw = read_guanji_catalog(SHG_XLSX)
+    finance, finance_report = build_finance_standard(
+        finance_raw.entries, source_file=str(FIN_XLSX), source_sheet="Table 1",
+        reader_issues=finance_raw.issues,
+    )
+    shougang, shougang_report = build_shougang_standard(
+        shougang_raw.entries, source_file=str(SHG_XLSX), source_sheet="数据分类分级",
+        reader_issues=shougang_raw.issues,
+    )
+    return finance, finance_report, shougang, shougang_report
+
+
+def test_finance_standard_is_lossless_237_entries_233_projection(built):
+    finance, finance_report, _, _ = built
+    registry = LeafRegistry.from_path(REG / "finance.registry.json")
+    assert len(finance.entries) == 237  # one per real standard row — NO collapse
+    assert finance.trainable_category_count() == 233
+    assert finance_report.standard_entries_out == 237
+    assert finance_report.training_categories == 233
+    # training alias set == registry identity set (join stays compatible)
+    assert {e.category_id for e in finance.entries} == set(registry.ids)
+    # real hierarchy path depth preserved (dict compressed it)
+    depths = {len(e.path) for e in finance.entries}
+    assert 4 in depths and 3 in depths
+
+
+def test_finance_five_level4_leaf_entries_kept_with_distinct_level3(built):
+    finance, _, _, _ = built
+    bucket = [
+        e for e in finance.entries
+        if e.category_id == "finance:业务.合约协议.基本信息"
+    ]
+    assert len(bucket) == 5  # 合同通用/贷款业务/中间业务/资金业务/其他支付业务
+    assert len({e.standard_entry_id for e in bucket}) == 5
+    # every entry keeps its own source row (nothing collapsed)
+    assert len({e.source.row for e in bucket}) == 5
+
+
+def test_finance_unparseable_levels_reported_not_fixed(built):
+    _, finance_report, _, _ = built
+    unparseable = [i for i in finance_report.issues if i.kind == "level_unparseable"]
+    assert len(unparseable) == 2
+    assert all("standard_data_level=null (not guessed)" in i.detail for i in unparseable)
+
+
+def test_finance_alignment_reproduces_known_outliers(built):
+    finance, _, _, _ = built
+    records = load_canonical_records(CANON / "finance" / "all.json")
+    report = align_dataset_to_standard(records, finance)
+    counts = report["sample_counts"]
+    assert counts["total"] == 568
+    assert counts["resolved"] == 531
+    assert counts["matched"] == 529
+    assert counts["mismatched"] == 2
+    assert counts["standard_missing"] == 0
+    fields = {m["field"] for m in report["mismatched_samples"]}
+    assert fields == {"AMONEY", "HXTRADENO"}
+
+
+def test_finance_unresolved_evidence_lists_candidates(built):
+    finance, _, _, _ = built
+    records = load_canonical_records(CANON / "finance" / "all.json")
+    report = align_dataset_to_standard(records, finance)
+    assert report["sample_counts"]["unresolved"] == 37
+    assert report["unresolved_by_status"] == {"missing_leaf": 34, "path_mismatch": 3}
+    # evidence-only mapping exists and never repairs
+    assert report["unresolved_evidence"]
+    for item in report["unresolved_evidence"]:
+        assert "candidate_standard_categories" in item
+
+
+def test_shougang_standard_covers_registry_plus_lost_b3_6(built):
+    _, _, shougang, _ = built
+    registry = LeafRegistry.from_path(REG / "shougang.registry.json")
+    standard_codes = {e.standard_entry_id for e in shougang.entries}
+    assert len(shougang.entries) == 234
+    assert set(registry.ids) <= standard_codes
+    assert standard_codes - set(registry.ids) == {"B3-6"}  # 中厚板作业计划 lost by legacy
+
+
+def test_shougang_alignment_100_percent(built):
+    _, _, shougang, _ = built
+    records = load_canonical_records(CANON / "shougang" / "all.json")
+    report = align_dataset_to_standard(records, shougang)
+    counts = report["sample_counts"]
+    assert counts["total"] == 19415
+    assert counts["resolved"] == 18393
+    assert counts["matched"] == 18393
+    assert counts["mismatched"] == 0
+    assert counts["standard_missing"] == 0
+
+
+def test_infra_reuses_shougang_standard(built):
+    _, _, shougang, _ = built
+    assert resolve_standard_dataset("infra") == "shougang"
+    records = load_canonical_records(CANON / "infra" / "all.json")
+    report = align_dataset_to_standard(records, shougang)
+    counts = report["sample_counts"]
+    assert counts["total"] == 64
+    assert counts["matched"] == 64
+    assert counts["mismatched"] == 0
+
+
+def test_pers_info_has_no_canonical_standard():
+    assert resolve_standard_dataset("pers_info") is None
+
+
+def test_build_deterministic_on_real_inputs(built):
+    _, _, _, _ = built
+    # rebuild from the same raw readers — identical fingerprints
+    f2, _ = build_finance_standard(
+        read_finance_standard_guide(FIN_XLSX).entries,
+        source_file=str(FIN_XLSX), source_sheet="Table 1",
+    )
+    s2, _ = build_shougang_standard(
+        read_guanji_catalog(SHG_XLSX).entries,
+        source_file=str(SHG_XLSX), source_sheet="数据分类分级",
+    )
+    assert len(f2.entries) == 237
+    assert len(s2.entries) == 234
+
+
+@pytest.mark.parametrize(
+    "annotation_id,expected_range,expected_applies",
+    [
+        ("finance-remark-55-55", None, 1),
+        ("finance-remark-93-132", "J93:J132", 40),
+        ("finance-remark-168-169", "J168:J169", 2),
+    ],
+)
+def test_finance_remark_scope_reproduced(built, annotation_id, expected_range, expected_applies):
+    finance, _, _, _ = built
+    by_id = {a.annotation_id: a for a in finance.scoped_annotations}
+    assert annotation_id in by_id
+    annotation = by_id[annotation_id]
+    assert annotation.merged_range == expected_range
+    assert len(annotation.applies_to_standard_entry_ids) == expected_applies
+    assert all(e.standard_entry_id in annotation.applies_to_standard_entry_ids
+               for e in finance.entries
+               if annotation.start_row <= e.source.row <= annotation.end_row)
+
+
+def test_finance_row93_inherited_definitions_with_provenance(built):
+    finance, _, _, _ = built
+    entry = finance.by_entry_id()["finance:业务.金融监管和服务.反洗钱业务信息.分类考核评级信息"]
+    l2 = entry.raw_fields["level_2_definition"]
+    l3 = entry.raw_fields["level_3_definition"]
+    assert l2["source_cell"] == "D93" and l2["merged_range"] == "D93:D132"
+    assert l3["source_cell"] == "F93"
+    # remark is a scoped annotation, NOT a leaf raw field
+    assert "remark" not in entry.raw_fields
+
+
+def test_shougang_inherited_definitions_and_resource_preserved(built):
+    _, _, shougang, _ = built
+    for code in ("A1-1-1", "B1-2"):
+        entry = shougang.by_entry_id()[code]
+        for key in ("level_1_definition", "level_2_definition", "level_3_definition", "resource"):
+            assert key in entry.raw_fields, (code, key)
+        assert entry.raw_fields["resource"]["value"]
+        assert entry.raw_fields["level_1_definition"]["source_cell"].startswith("C")
+
+
+@pytest.mark.parametrize(
+    "xlsx,mins",
+    [
+        (RAW / "部分金融数据.xlsx", 1),          # one title merge (A1:A2) only
+        (RAW / "带分级分类的个人基础信息样本190条.xlsx", 0),
+        (RAW / "关基设施数据分类分级-不包含训练-用于测试(1).xlsx", 0),
+    ],
+)
+def test_sample_sources_have_no_business_merged_cells(xlsx, mins):
+    openpyxl = pytest.importorskip("openpyxl")
+    if not xlsx.is_file():
+        pytest.skip("raw sample workbook not present")
+    workbook = openpyxl.load_workbook(xlsx)
+    try:
+        ws = workbook[workbook.sheetnames[0]]
+        # sample sources must not use group-level merges; the finance sample's
+        # single A1:A2 is a title, not a business column
+        assert len(ws.merged_cells.ranges) <= mins
+    finally:
+        workbook.close()