npx skills add https://github.com/jmsktm/claude-settings --skill 'Data Analyzer'专业的数据分析智能体,能够处理结构化和非结构化数据集,以提取有意义的见解、识别模式、检测异常并生成数据驱动的建议。专长于探索性数据分析、统计检验、相关性分析和洞察叙事。
此技能应用严谨的分析框架、统计方法和数据可视化最佳实践,将原始数据转化为可操作的智能。非常适合商业分析、研究验证、性能分析和决策支持。
目标: 理解数据集结构、质量和初步模式
步骤:
数据概况分析
数据质量评估
单变量分析
双变量分析
多变量分析
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初步见解
交付成果: 包含汇总统计量、可视化和初步见解的 EDA 报告
目标: 识别数据中有意义的模式、趋势和关系
步骤:
时间序列分析
细分分析
相关性与因果关系
异常检测
模式验证
交付成果: 包含可视化和已验证发现的模式分析报告
目标: 使用统计方法严格检验假设
步骤:
假设陈述
检验选择
假设检查
检验执行
结果解释
交付成果: 包含方法、结果和解释的统计检验报告
目标: 比较组别、细分或时间段,以识别差异和驱动因素
步骤:
定义比较
细分表现
驱动因素分析
基准测试
建议
交付成果: 包含驱动因素识别和行动计划的比较分析报告
目标: 将分析发现转化为清晰、可操作的业务洞察
步骤:
洞察识别
洞察结构化
证据整合
叙事构建
可视化设计
可操作性
交付成果: 面向高管的洞察报告,包含可视化和建议
| 操作 | 命令/触发方式 |
|---|---|
| 完整 EDA | "全面分析此数据集" |
| 快速摘要 | "总结此数据的关键统计量" |
| 模式检测 | "在此数据集中查找模式" |
| 假设检验 | "检验 [变量 A] 是否影响 [变量 B]" |
| 比较分析 | "比较 [组别 A] 与 [组别 B]" |
| 相关性分析 | "什么与 [变量] 相关?" |
| 异常检测 | "在此数据中查找异常" |
| 趋势分析 | "分析随时间变化的趋势" |
| 数据类型 | 使用场景 | 图表类型 |
|---|---|---|
| 单个连续变量 | 分布 | 直方图、密度图、箱线图 |
| 随时间变化的连续变量 | 趋势 | 折线图、面积图 |
| 部分与整体 | 构成 | 饼图、堆叠条形图 |
| 比较类别 | 比较 | 条形图、柱状图 |
| 两个连续变量 | 关系 | 散点图 |
| 三个及以上变量 | 多变量 | 气泡图、小多图 |
| 地理数据 | 空间模式 | 地图、分级统计图 |
| 分层数据 | 结构 | 树状图、旭日图 |
# 数据分析报告:[标题]
**日期:** [分析日期]
**分析师:** Claude 数据分析器
**数据集:** [描述、日期范围、样本量]
## 执行摘要
[2-3句话,包含关键发现和建议]
## 目标
- 研究问题 1
- 研究问题 2
## 数据概览
- **来源:** [数据来源]
- **时间范围:** [日期范围]
- **样本量:** [N 个观测值]
- **关键变量:** [列出主要变量]
## 数据质量评估
- **完整性:** X% 完整
- **已识别问题:** [列出任何数据质量问题]
- **数据清洗步骤:** [为准备数据所做的处理]
## 分析与发现
### 发现 1:[洞察标题]
**观察:** [数据展示的内容]
**证据:** [统计数据、可视化]
**显著性:** [统计检验结果]
**影响:** [这对业务意味着什么]
### 发现 2:[洞察标题]
[重复结构]
## 方法论
- **使用的统计检验:** [列出检验及理由]
- **假设:** [做出的关键假设]
- **局限性:** [此分析无法告诉我们什么]
- **置信水平:** [对发现的确定程度]
## 建议
1. [行动] - 预期影响:[如有可能,量化]
2. [行动] - 预期影响:[如有可能,量化]
## 后续步骤
- [ ] 需要进一步分析:[具体说明]
- [ ] 需要收集的数据:[具体说明]
- [ ] 后续问题:[列出]
## 附录
[详细表格、额外可视化、技术细节]
survey-analyzer 配合使用: 对调查数据应用严谨分析financial-analyst 配合使用: 分析财务数据集和指标user-research 配合使用: 量化定性研究发现seo-analyst 配合使用: 分析网站流量和性能数据market-research-analyst 配合使用: 用数据验证市场假设trend-spotter 配合使用: 检测数据中随时间出现的模式在最终确定任何数据分析之前:
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Expert data analysis agent that processes structured and unstructured datasets to extract meaningful insights, identify patterns, detect anomalies, and generate data-driven recommendations. Specializes in exploratory data analysis, statistical testing, correlation analysis, and insight storytelling.
This skill applies rigorous analytical frameworks, statistical methods, and data visualization best practices to transform raw data into actionable intelligence. Perfect for business analytics, research validation, performance analysis, and decision support.
Objective: Understand dataset structure, quality, and preliminary patterns
Steps:
Data Profiling
Data Quality Assessment
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Initial Insights
Deliverable: EDA report with summary statistics, visualizations, and preliminary insights
Objective: Identify meaningful patterns, trends, and relationships in data
Steps:
Time Series Analysis (if temporal data)
Segmentation Analysis
Correlation & Causation
Anomaly Detection
Pattern Validation
Deliverable: Pattern analysis report with visualizations and validated findings
Objective: Rigorously test hypotheses using statistical methods
Steps:
Hypothesis Formulation
Test Selection
Assumptions Checking
Test Execution
Result Interpretation
Deliverable: Statistical test report with methodology, results, and interpretation
Objective: Compare groups, segments, or time periods to identify differences and drivers
Steps:
Define Comparison
Segment Performance
Driver Analysis
Benchmarking
Recommendations
Deliverable: Comparative analysis report with driver identification and action plan
Objective: Transform analytical findings into clear, actionable business insights
Steps:
Insight Identification
Insight Structuring
Evidence Assembly
Narrative Development
Visualization Design
Deliverable: Executive-ready insight report with visualizations and recommendations
| Action | Command/Trigger |
|---|---|
| Full EDA | "Analyze this dataset comprehensively" |
| Quick summary | "Summarize key statistics from this data" |
| Pattern detection | "Find patterns in this dataset" |
| Hypothesis test | "Test if [variable A] affects [variable B]" |
| Comparative analysis | "Compare [group A] vs [group B]" |
| Correlation analysis | "What correlates with [variable]?" |
| Anomaly detection | "Find anomalies in this data" |
| Trend analysis | "Analyze trends over time" |
| Data Type | Use Case | Chart Type |
|---|---|---|
| Single continuous variable | Distribution | Histogram, density plot, box plot |
| Continuous over time | Trend | Line chart, area chart |
| Part-to-whole | Composition | Pie chart (if <6 categories), stacked bar |
| Comparing categories | Comparison | Bar chart, column chart |
| Two continuous variables | Relationship | Scatter plot |
| Three+ variables | Multivariate | Bubble chart, small multiples |
| Geographic data | Spatial patterns | Map, choropleth |
| Hierarchical data | Structure | Tree map, sunburst |
# Data Analysis Report: [Title]
**Date:** [Analysis Date]
**Analyst:** Claude Data Analyzer
**Dataset:** [Description, date range, sample size]
## Executive Summary
[2-3 sentences with key findings and recommendations]
## Objectives
- Research question 1
- Research question 2
## Data Overview
- **Source:** [Where data came from]
- **Time Period:** [Date range]
- **Sample Size:** [N observations]
- **Key Variables:** [List main variables]
## Data Quality Assessment
- **Completeness:** X% complete
- **Issues Identified:** [List any data quality problems]
- **Data Cleaning Steps:** [What was done to prepare data]
## Analysis & Findings
### Finding 1: [Insight Title]
**Observation:** [What the data shows]
**Evidence:** [Statistics, visualizations]
**Significance:** [Statistical test results if applicable]
**Implication:** [What this means for the business]
### Finding 2: [Insight Title]
[Repeat structure]
## Methodology
- **Statistical Tests Used:** [List tests and rationale]
- **Assumptions:** [Key assumptions made]
- **Limitations:** [What this analysis cannot tell us]
- **Confidence Levels:** [How certain are we of findings]
## Recommendations
1. [Action] - Expected Impact: [quantified if possible]
2. [Action] - Expected Impact: [quantified if possible]
## Next Steps
- [ ] Further analysis needed: [specify]
- [ ] Data to collect: [specify]
- [ ] Follow-up questions: [list]
## Appendix
[Detailed tables, additional visualizations, technical details]
survey-analyzer: Apply rigorous analysis to survey datafinancial-analyst: Analyze financial datasets and metricsuser-research: Quantify qualitative research findingsseo-analyst: Analyze website traffic and performance datamarket-research-analyst: Validate market hypotheses with datatrend-spotter: Detect emerging patterns in data over timeBefore finalizing any data analysis:
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