feat: 新增 data-warehouse-modeling 数仓建模 skill#47
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数仓建模实战方法论，覆盖： - 5 种建模方法论对比选择（Kimball/Inmon/Data Vault 2.0/OneData/Medallion） - 4 种分层架构（国内标准/Medallion/dbt/Data Vault） - 维度建模、SCD 处理、指标体系、总线矩阵 - 命名规范与词根词典 - 各层 SQL 模板（Hive/Spark） - 实时数仓（Kafka+Flink+OLAP） - 反模式识别（P0/P1/P2 分级） - dbt 工程化完整指南 - 云平台最佳实践（Snowflake/BigQuery/Databricks/Redshift） - 数据治理与合规（GDPR/CCPA/PII） - 数仓文档模板与评审清单 实战语气，非教科书摘要。
优化内容： - 方法论选择改为「行业特性 → 团队规模 → 方法论」三层决策 - 新增 industry-patterns.md（6 个行业建模模式、BOM 桥接表、 FHIR 映射、三库架构、时序数据降采样、物候维度、OT/IT 融合） - 反模式新增传统行业特有陷阱（源数据未 Profile、 递归 CTE 处理层级、时序直入 Hive、合规后置） - 新增传统行业数据格式处理（时序/GIS/图/非结构化/层级递归） - SQL 模板表名补全库名前缀（ods./dwd./dws./dim./ads.） - 去 AI 味，实战语气
- 方法论选择：企业阶段 → 行业特性 → 团队规模三层次 - 数字化成熟度四阶段：初始级→部门级→企业级→数据驱动 - MDM 三阶段：Registry→Consolidation→Coexistence - 渐进式三步走：MVP→主题域覆盖→智能化 - 纸电鸿沟、遗留系统孤岛处理方案 - 三个转型行业的详细建模路径
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你要解决什么问题？
superpowers-zh 目前覆盖了 AI 编程通用工作流，但缺少数据仓库建模方法论 skill。数仓建模是数据工程师的高频需求，当前 AI 助手往往直接写 SQL，缺少系统化决策框架。
这个 PR 做了什么改变？
新增 data-warehouse-modeling skill，覆盖 5 种方法论对比、4 种分层架构、维度建模、指标体系、命名规范、SQL 模板、实时数仓、反模式、dbt、云平台、数据治理。特别引进了制造业/医疗/政务/能源/农业/零售行业建模经验。
这个改变适合放在核心库中吗？
是的。数仓建模是跨行业通用方法论，和 superpowers-zh 现有 skill 互补。
文件结构
SKILL.md (152 行) + 11 个 references + 1 个 scripts/sql-templates.md
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