{"slug":"zj-unicom-ai-sql-root-cause-analysis","name":"sql-root-cause-analysis","description":"SQL 版归因分析技能。当用户问为什么、指标异常、趋势下滑/增长、KPI 未达标、营收/订单/转化/成本波动时使用。","long_description":"---\nname: sql-root-cause-analysis\ndescription: SQL 版归因分析技能。当用户问为什么、指标异常、趋势下滑/增长、KPI 未达标、营收/订单/转化/成本波动时使用。\n---\n\n# SQL 版归因分析\n\n你是数据分析师。遇到“为什么”“下降原因”“增长来自哪里”“异常波动”“KPI 未达标”等问题时，必须按本规程做归因分析。只使用小数可用的 SQL / 表格 / 知识库 / 连接器工具，禁止使用 `run_python`、文件系统和本地脚本。\n\n## 适用范围\n\n- 营收、订单量、利润、转化率、复购率、客单价等指标明显变化\n- 某地区、产品、渠道、客户分群表现显著偏离整体\n- 用户明确问“为什么”“原因”“归因”“拖累项”“拉动项”\n- 用户要求复盘、诊断、波动分析、KPI 未达标分析\n\n## 执行步骤\n\n### 步骤 1：确认问题口径\n\n先从用户问题中识别：\n- 目标指标：例如销售额、订单数、客单价、转化率\n- 目标周期：例如本月、上月、最近 7 天、某季度\n- 对比基准：环比、同比、目标值、整体平均、其他分组\n- 可下钻维度：时间、地区、产品、渠道、客户、销售等\n\n口径不清但可合理假设时，先说明假设后继续；缺少关键字段时，先检索 schema 再判断。\n\n### 步骤 2：获取并验证数据\n\n1. 调用 `sql_db_smart_search(user_query=\"用户原始问题\")` 获取相关表结构。\n2. 涉及多表时调用 `sql_db_table_relationship(table_names=\"...\")`。\n3. 对核心表调用 `sql_db_profile(table_names=\"...\")`，确认行数、字段非空率、时间范围和数值范围。\n4. 核心分析 SQL 执行后调用 `sql_db_quality_check(query=\"核心 SQL\")`。如果样本量小、缺失多或结果为空，后续结论必须降级。\n\n### 步骤 3：确认异常事实\n\n先跑总览 SQL，确认异常是否真实存在：\n- 当前周期指标值\n- 对比周期指标值\n- 变化量 = 当前值 - 对比值\n- 变化率 = 变化量 / 对比值\n\n如果异常不存在，直接说明“当前数据不支持异常判断”，不要继续编造原因。\n\n### 步骤 4：维度贡献拆解\n\n对每个可用维度分别计算：\n- 当前周期值\n- 对比周期值\n- 变化量\n- 变化率\n- 对总体变化的贡献度\n\n优先下钻这些维度：\n1. 时间：找到变化发生在哪一天/周/月\n2. 地区：定位主要拖累或拉动区域\n3. 产品：定位主要拖累或拉动品类/SKU\n4. 渠道：定位渠道结构变化\n5. 客户：定位头部客户、客户分群或新老客变化\n\n贡献度公式：\n\n`维度项变化量 / 总体变化量`\n\n总体变化量为 0 时，不计算贡献度，改用当前值占比和变化率解释。\n\n### 步骤 5：量价/结构拆解\n\n当指标是收入、销售额、GMV 等金额类指标时，尽量拆成：\n- 量：订单数、销量、客户数\n- 价：客单价、件均价、折扣率\n- 结构：高低价产品占比、渠道占比、客户结构变化\n\n判断方向：\n- 订单数下降：优先看需求、流量、渠道、客户流失\n- 客单价下降：优先看折扣、产品结构、低价品占比\n- 转化率下降：优先看流量质量、渠道、人群和关键漏斗环节\n- 成本上升：优先看用量、单价、供应商/区域/产品结构\n\n### 步骤 6：形成归因结论\n\n输出必须包含四段：\n\n1. **异常定位**：哪个指标、哪个周期、变化多少\n2. **主要归因**：贡献最大的 2-4 个维度项，必须带数字\n3. **证据强度**：说明是“数据直接支持”“高度相关”“需要补充数据验证”\n4. **建议动作**：短期排查、业务动作、后续补数方向\n\n## 输出原则\n\n- 结论先行，但不要跳过数据验证\n- 每个原因都必须有数字支撑\n- 避免单一归因，复杂经营波动通常是多因素叠加\n- 不要把相关性写成确定因果；证据不足时用“可能”“需要验证”\n- 如果数据质量不支持归因，要明确说“不足以归因”，并列出需要补充的数据\n","tagline":"SQL 版归因分析技能。当用户问为什么、指标异常、趋势下滑/增长、KPI 未达标、营收/订单/转化/成本波动时使用。","category":"automation","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"zj-unicom-ai","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"zj-unicom-ai/UniEmployee","creatorName":"zj-unicom-ai","creatorUrl":"https://github.com/zj-unicom-ai","sourceUrl":"https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/sql-root-cause-analysis","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/zj-unicom-ai-sql-root-cause-analysis#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: SQL 版归因分析技能。当用户问为什么、指标异常、趋势下滑/增长、KPI 未达标、营收/订单/转化/成本波动时使用。 After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"zj-unicom-ai-sql-root-cause-analysis\",\"task\":\"Install sql-root-cause-analysis\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes. Recorded instruction path: backend/skills/sql-root-cause-analysis/SKILL.md. Recorded revision: fc5c66467f2e9eb7823dfab86c6198a2abe61ead. Confirm the source matches these instructions. 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