数据 Analyst 工作流
- 作者仓库星标 1
- 作者仓库 claude-skills
Data Analyst Workflow
Overview
Run analysis with an explicit chain from raw data to plain-English conclusions.
Core Workflow
- Inspect schema, types, missingness, duplicate risk, and obvious quality issues before analyzing outcomes.
- Clean or normalize data carefully and document every meaningful assumption.
- Compute descriptive statistics and comparisons that answer the actual question, not just what is easy to chart.
- Check anomalies, outliers, seasonality, and segmentation effects before drawing conclusions.
- Present the findings in plain language with tables or charts only when they improve understanding.
Deliver
- Show the key numbers and what they mean.
- Call out data-quality limitations and assumption-sensitive results.
- Keep a clear distinction between observed facts and interpretation.
Guardrails
- Do not bury cleaning choices that materially change the result.
- Prefer reproducible steps over one-off spreadsheet-style reasoning.
- Avoid false precision when the dataset or method does not support it.
- 流狐分类
- 数据
- 作者声明 Agent
- 未找到明确声明；不据此推断已兼容或已测试
- 静态检查
- 88 / 100 · 启发式扫描，不代表运行安全
- 作者 / 版本 / 许可
- @00PrabalK00 · 未声明 license
- 流狐 Token 估算
- 低消耗
- 流狐接入估算
- 即装即用
- 是否需要外部 API Key
- 未发现要求
- 检测到的系统要求
- 未声明
- 底层运行要求
- 未声明
- 检测到的文件与系统行为
-
- 只读
- 检测到的网络行为
- 仅限本地
- 安装命令数
- 无（仅作为资料）
档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。
需要注意： 未限定 allowed-tools，默认拥有全部工具权限。
作者没有在当前 SKILL.md 中定义固定输出样例。Run analysis with an explicit chain from raw data to plain-English conclusions.
Inspect schema, types, missingness, duplicate risk, and obvious quality issues before analyzing outcomes. Clean or normalize data carefully and document every meaningful assumption. Compute descriptive statistics and comparisons that answer the actual…
Show the key numbers and what they mean. Call out data-quality limitations and assumption-sensitive results. Keep a clear distinction between observed facts and interpretation.
Do not bury cleaning choices that materially change the result. Prefer reproducible steps over one-off spreadsheet-style reasoning. Avoid false precision when the dataset or method does not support it.
# Data Analyst Workflow
## Overview
Run analysis with an explicit chain from raw data to plain-English conclusions.
## Core Workflow
1. Inspect schema, types, missingness, duplicate risk, and obvious quality issues before analyzing outcomes.
2. Clean or normalize data carefully and document every meaningful assumption.
3. Compute descriptive statistics and comparisons that answer the actual question, not just what is easy to chart.
4. Check anomalies, outliers, seasonality, and segmentation effects before drawing conclusions.
5. Present the findings in plain language with tables or charts only when they improve understanding.
## Deliver
- Show the key numbers and what they mean.
- Call out data-quality limitations and assumption-sensitive results.
- Keep a clear distinction between observed facts and interpretation.
## Guardrails
- Do not bury cleaning choices that materially change the result.
- Prefer reproducible steps over one-off spreadsheet-style reasoning.
- Avoid false precision when the dataset or method does not support it.作者原文负责流程事实；流狐只索引当前章节、要点、文件与命令。
章节 -> Overview → Core Workflow → Deliver → Guardrails
要点 -> Run analysis with an explicit chain from raw data to plain-English conclusions. · 1. Inspect schema, types, missingness, duplicate risk, and obvious quality issues before analyzing outcomes. · - Show the key numbers and what they mean. · - Do not bury cleaning choices that materially change the result.
文件/命令 -> 原文未列出明确文件或命令
内容 SHA-256 -> 3a9ae94a2918
方法与流程
适用与边界
原文中的明确线索