Csv Data Summarizer
- 62 installs
- 263 repo stars
- Updated July 22, 2026
- zrt-ai-lab/opencode-skills
Helps with ai & agent building tasks during AI-assisted development.
About
csv-data-summarizer is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- csv-data-summarizer
- AI & Agent Building
- AI-coding skill
Csv Data Summarizer by the numbers
- 62 all-time installs (skills.sh)
- Ranked #6,163 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 3, 2026 (Skillselion catalog sync)
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| Installs | 62 |
|---|---|
| repo stars | ★ 263 |
| Last updated | July 22, 2026 |
| Repository | zrt-ai-lab/opencode-skills ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
CSV 数据分析器
此技能分析 CSV 文件并提供包含统计洞察和可视化的全面摘要。
何时使用此技能
当用户：
- 上传或提到 CSV 文件
- 要求汇总、分析或可视化表格数据
- 请求从 CSV 数据中获取洞察
- 想了解数据结构和质量
工作原理
⚠️ 关键行为要求 ⚠️
不要问用户想用数据做什么。 不要提供选项或选择。 不要说"您想让我帮您做什么？" 不要列出可能的分析选项。
立即自动执行： 1. 运行全面分析 2. 生成所有相关可视化 3. 展示完整结果 4. 不提问、不给选项、不等待用户输入
用户想要立即获得完整分析 - 直接做就行。
自动分析步骤：
该技能通过先检查数据，然后确定最相关的分析，智能适应不同的数据类型和行业。
1. 加载并检查 CSV 文件到 pandas DataFrame 2. 识别数据结构 - 列类型、日期列、数值列、类别 3. 根据数据内容确定相关分析：
- 销售/电商数据（订单日期、收入、产品）：时间序列趋势、收入分析、产品表现
- 客户数据（人口统计、细分、区域）：分布分析、细分、地理模式
- 财务数据（交易、金额、日期）：趋势分析、统计摘要、相关性
- 运营数据（时间戳、指标、状态）：时间序列、绩效指标、分布
- 调查数据（分类响应、评分）：频率分析、交叉表、分布
- 通用表格数据：根据找到的列类型调整
4. 只创建对特定数据集有意义的可视化：
- 时间序列图仅在存在日期/时间戳列时
- 相关性热图仅在存在多个数值列时
- 类别分布仅在存在分类列时
- 数值分布的直方图（相关时）
5. 自动生成全面输出包括：
- 数据概览（行数、列数、类型）
- 与数据类型相关的关键统计和指标
- 缺失数据分析
- 多个相关可视化（仅适用的那些）
- 基于此特定数据集中发现的模式的可操作洞察
6. 一次性展示所有内容 - 不追问
适应示例：
- 带患者ID的医疗数据 → 专注于人口统计、治疗模式、时间趋势
- 带库存水平的库存数据 → 专注于数量分布、补货模式、SKU分析
- 带时间戳的网站分析 → 专注于流量模式、转化指标、时段分析
- 调查响应 → 专注于响应分布、人口统计细分、情感模式
行为指南
✅ 正确方法 - 这样说：
- "我现在对这些数据进行全面分析。"
- "这是带可视化的完整分析："
- "我识别出这是[类型]数据并生成了相关洞察："
- 然后立即展示完整分析
✅ 要做：
- 立即运行分析脚本
- 自动生成所有相关图表
- 无需询问即提供完整洞察
- 在第一次响应中就做到全面完整
- 果断行动，不需征求许可
❌ 永远不要说这些话：
- "您想用这些数据做什么？"
- "您想让我帮您做什么？"
- "这里有一些常见选项："
- "让我知道您想要什么帮助"
- "如果您愿意，我可以创建全面分析！"
- 任何以"？"结尾询问用户方向的句子
- 任何选项或选择列表
- 任何条件性的"如果您想，我可以做X"
❌ 禁止行为：
- 询问用户想要什么
- 列出选项供用户选择
- 在分析前等待用户指示
- 提供需要后续跟进的部分分析
- 描述你可以做什么而不是直接做
使用方法
该技能提供 Python 函数 summarize_csv(file_path)：
- 接受 CSV 文件的路径
- 返回带统计信息的全面文本摘要
- 根据数据结构自动生成多个可视化
示例提示
"这是 sales_data.csv。你能汇总这个文件吗？""分析这个客户数据 CSV 并展示趋势。"
"你能从 orders.csv 中发现什么洞察？"示例输出
数据集概览
- 5,000 行 × 8 列
- 3 个数值列，1 个日期列
统计摘要
- 平均订单价值：$58.2
- 标准差：$12.4
- 缺失值：2%（100个单元格）
洞察
- 销售随时间呈上升趋势
- Q4活动达到峰值
（附：趋势图）
文件
analyze.py- 核心分析逻辑requirements.txt- Python 依赖resources/sample.csv- 用于测试的示例数据集resources/README.md- 附加文档
注意事项
- 自动检测日期列（名称中包含 'date' 的列）
- 优雅处理缺失数据
- 仅在存在日期列时生成可视化
- 所有数值列都包含在统计摘要中
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
def summarize_csv(file_path):
"""
Comprehensively analyzes a CSV file and generates multiple visualizations.
Args:
file_path (str): Path to the CSV file
Returns:
str: Formatted comprehensive analysis of the dataset
"""
df = pd.read_csv(file_path)
summary = []
charts_created = []
# Basic info
summary.append("=" * 60)
summary.append("📊 DATA OVERVIEW")
summary.append("=" * 60)
summary.append(f"Rows: {df.shape[0]:,} | Columns: {df.shape[1]}")
summary.append(f"\nColumns: {', '.join(df.columns.tolist())}")
# Data types
summary.append(f"\n📋 DATA TYPES:")
for col, dtype in df.dtypes.items():
summary.append(f" • {col}: {dtype}")
# Missing data analysis
missing = df.isnull().sum().sum()
missing_pct = (missing / (df.shape[0] * df.shape[1])) * 100
summary.append(f"\n🔍 DATA QUALITY:")
if missing:
summary.append(f"Missing values: {missing:,} ({missing_pct:.2f}% of total data)")
summary.append("Missing by column:")
for col in df.columns:
col_missing = df[col].isnull().sum()
if col_missing > 0:
col_pct = (col_missing / len(df)) * 100
summary.append(f" • {col}: {col_missing:,} ({col_pct:.1f}%)")
else:
summary.append("✓ No missing values - dataset is complete!")
# Numeric analysis
numeric_cols = df.select_dtypes(include='number').columns.tolist()
if numeric_cols:
summary.append(f"\n📈 NUMERICAL ANALYSIS:")
summary.append(str(df[numeric_cols].describe()))
# Correlations if multiple numeric columns
if len(numeric_cols) > 1:
summary.append(f"\n🔗 CORRELATIONS:")
corr_matrix = df[numeric_cols].corr()
summary.append(str(corr_matrix))
# Create correlation heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0,
square=True, linewidths=1)
plt.title('Correlation Heatmap')
plt.tight_layout()
plt.savefig('correlation_heatmap.png', dpi=150)
plt.close()
charts_created.append('correlation_heatmap.png')
# Categorical analysis
categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
categorical_cols = [c for c in categorical_cols if 'id' not in c.lower()]
if categorical_cols:
summary.append(f"\n📊 CATEGORICAL ANALYSIS:")
for col in categorical_cols[:5]: # Limit to first 5
value_counts = df[col].value_counts()
summary.append(f"\n{col}:")
for val, count in value_counts.head(10).items():
pct = (count / len(df)) * 100
summary.append(f" • {val}: {count:,} ({pct:.1f}%)")
# Time series analysis
date_cols = [c for c in df.columns if 'date' in c.lower() or 'time' in c.lower()]
if date_cols:
summary.append(f"\n📅 TIME SERIES ANALYSIS:")
date_col = date_cols[0]
df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
date_range = df[date_col].max() - df[date_col].min()
summary.append(f"Date range: {df[date_col].min()} to {df[date_col].max()}")
summary.append(f"Span: {date_range.days} days")
# Create time-series plots for numeric columns
if numeric_cols:
fig, axes = plt.subplots(min(3, len(numeric_cols)), 1,
figsize=(12, 4 * min(3, len(numeric_cols))))
if len(numeric_cols) == 1:
axes = [axes]
for idx, num_col in enumerate(numeric_cols[:3]):
ax = axes[idx] if len(numeric_cols) > 1 else axes[0]
daily_data = df.groupby(date_col)[num_col].agg(['mean', 'sum', 'count'])
daily_data['mean'].plot(ax=ax, label='Average', linewidth=2)
ax.set_title(f'{num_col} Over Time')
ax.set_xlabel('Date')
ax.set_ylabel(num_col)
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('time_series_analysis.png', dpi=150)
plt.close()
charts_created.append('time_series_analysis.png')
# Distribution plots for numeric columns
if numeric_cols:
n_cols = min(4, len(numeric_cols))
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes = axes.flatten()
for idx, col in enumerate(numeric_cols[:4]):
axes[idx].hist(df[col].dropna(), bins=30, edgecolor='black', alpha=0.7)
axes[idx].set_title(f'Distribution of {col}')
axes[idx].set_xlabel(col)
axes[idx].set_ylabel('Frequency')
axes[idx].grid(True, alpha=0.3)
# Hide unused subplots
for idx in range(len(numeric_cols[:4]), 4):
axes[idx].set_visible(False)
plt.tight_layout()
plt.savefig('distributions.png', dpi=150)
plt.close()
charts_created.append('distributions.png')
# Categorical distributions
if categorical_cols:
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
axes = axes.flatten()
for idx, col in enumerate(categorical_cols[:4]):
value_counts = df[col].value_counts().head(10)
axes[idx].barh(range(len(value_counts)), value_counts.values)
axes[idx].set_yticks(range(len(value_counts)))
axes[idx].set_yticklabels(value_counts.index)
axes[idx].set_title(f'Top Values in {col}')
axes[idx].set_xlabel('Count')
axes[idx].grid(True, alpha=0.3, axis='x')
# Hide unused subplots
for idx in range(len(categorical_cols[:4]), 4):
axes[idx].set_visible(False)
plt.tight_layout()
plt.savefig('categorical_distributions.png', dpi=150)
plt.close()
charts_created.append('categorical_distributions.png')
# Summary of visualizations
if charts_created:
summary.append(f"\n📊 VISUALIZATIONS CREATED:")
for chart in charts_created:
summary.append(f" ✓ {chart}")
summary.append("\n" + "=" * 60)
summary.append("✅ COMPREHENSIVE ANALYSIS COMPLETE")
summary.append("=" * 60)
return "\n".join(summary)
if __name__ == "__main__":
# Test with sample data
import sys
if len(sys.argv) > 1:
file_path = sys.argv[1]
else:
file_path = "resources/sample.csv"
print(summarize_csv(file_path))
month,year,quarter,product_line,total_revenue,cost_of_goods_sold,gross_profit,gross_margin_pct,marketing_expense,sales_expense,rd_expense,admin_expense,total_operating_expenses,operating_income,operating_margin_pct,interest_expense,tax_expense,net_income,net_margin_pct,customer_acquisition_cost,customer_lifetime_value,units_sold,avg_selling_price,headcount,revenue_per_employee
Jan,2023,Q1,SaaS Platform,450000,135000,315000,70.0,65000,85000,45000,35000,230000,85000,18.9,5000,16000,64000,14.2,125,2400,1200,375,45,10000
Jan,2023,Q1,Enterprise Solutions,280000,112000,168000,60.0,35000,55000,25000,20000,135000,33000,11.8,3000,6600,23400,8.4,450,8500,450,622,45,6222
Jan,2023,Q1,Professional Services,125000,50000,75000,60.0,15000,22000,8000,12000,57000,18000,14.4,1500,3600,12900,10.3,200,3200,95,1316,45,2778
Feb,2023,Q1,SaaS Platform,475000,142500,332500,70.0,68000,89000,47000,36000,240000,92500,19.5,5200,18500,68800,14.5,120,2500,1300,365,47,10106
Feb,2023,Q1,Enterprise Solutions,295000,118000,177000,60.0,38000,58000,27000,22000,145000,32000,10.8,3200,6400,22400,7.6,440,8600,470,628,47,6277
Feb,2023,Q1,Professional Services,135000,54000,81000,60.0,16000,24000,9000,13000,62000,19000,14.1,1600,3800,13600,10.1,195,3300,105,1286,47,2872
Mar,2023,Q1,SaaS Platform,520000,156000,364000,70.0,75000,95000,52000,40000,262000,102000,19.6,5500,19250,77250,14.9,115,2650,1450,359,50,10400
Mar,2023,Q1,Enterprise Solutions,325000,130000,195000,60.0,42000,63000,30000,25000,160000,35000,10.8,3500,7000,24500,7.5,425,8800,520,625,50,6500
Mar,2023,Q1,Professional Services,148000,59200,88800,60.0,18000,26000,10000,14000,68000,20800,14.1,1800,4160,14840,10.0,190,3400,115,1287,50,2960
Apr,2023,Q2,SaaS Platform,555000,166500,388500,70.0,80000,100000,55000,42000,277000,111500,20.1,5800,22300,83400,15.0,110,2750,1550,358,52,10673
Apr,2023,Q2,Enterprise Solutions,340000,136000,204000,60.0,45000,65000,32000,26000,168000,36000,10.6,3700,7200,25100,7.4,420,9000,540,630,52,6538
Apr,2023,Q2,Professional Services,158000,63200,94800,60.0,19000,27000,11000,15000,72000,22800,14.4,1900,4560,16340,10.3,185,3500,125,1264,52,3038
May,2023,Q2,SaaS Platform,590000,177000,413000,70.0,85000,105000,58000,44000,292000,121000,20.5,6000,24200,90800,15.4,105,2850,1650,358,55,10727
May,2023,Q2,Enterprise Solutions,365000,146000,219000,60.0,48000,68000,35000,28000,179000,40000,11.0,4000,8000,28000,7.7,410,9200,580,629,55,6636
May,2023,Q2,Professional Services,172000,68800,103200,60.0,21000,29000,12000,16000,78000,25200,14.7,2100,5040,18060,10.5,180,3600,135,1274,55,3127
Jun,2023,Q2,SaaS Platform,625000,187500,437500,70.0,90000,110000,62000,46000,308000,129500,20.7,6200,25850,97450,15.6,100,2950,1750,357,58,10776
Jun,2023,Q2,Enterprise Solutions,385000,154000,231000,60.0,50000,70000,37000,29000,186000,45000,11.7,4200,9000,31800,8.3,400,9400,610,631,58,6638
Jun,2023,Q2,Professional Services,185000,74000,111000,60.0,22000,31000,13000,17000,83000,28000,15.1,2200,5580,20220,10.9,175,3700,145,1276,58,3190
Jul,2023,Q3,SaaS Platform,665000,199500,465500,70.0,95000,115000,65000,48000,323000,142500,21.4,6500,28500,107500,16.2,95,3050,1850,359,60,11083
Jul,2023,Q3,Enterprise Solutions,410000,164000,246000,60.0,53000,73000,40000,31000,197000,49000,12.0,4400,9800,34800,8.5,390,9600,650,631,60,6833
Jul,2023,Q3,Professional Services,198000,79200,118800,60.0,24000,33000,14000,18000,89000,29800,15.1,2400,5960,21440,10.8,170,3800,155,1277,60,3300
Aug,2023,Q3,SaaS Platform,705000,211500,493500,70.0,100000,120000,68000,50000,338000,155500,22.1,6800,31100,117600,16.7,90,3150,1950,362,63,11190
Aug,2023,Q3,Enterprise Solutions,435000,174000,261000,60.0,56000,76000,42000,33000,207000,54000,12.4,4600,10800,38600,8.9,380,9800,690,630,63,6905
Aug,2023,Q3,Professional Services,210000,84000,126000,60.0,25000,35000,15000,19000,94000,32000,15.2,2500,6400,23100,11.0,165,3900,165,1273,63,3333
Sep,2023,Q3,SaaS Platform,750000,225000,525000,70.0,108000,128000,72000,53000,361000,164000,21.9,7200,33360,123440,16.5,88,3250,2080,360,65,11538
Sep,2023,Q3,Enterprise Solutions,465000,186000,279000,60.0,60000,80000,45000,35000,220000,59000,12.7,5000,11800,42200,9.1,370,10000,735,633,65,7154
Sep,2023,Q3,Professional Services,225000,90000,135000,60.0,27000,37000,16000,20000,100000,35000,15.6,2700,6920,25380,11.3,160,4000,175,1286,65,3462
Oct,2023,Q4,SaaS Platform,795000,238500,556500,70.0,115000,135000,75000,55000,380000,176500,22.2,7500,35870,133130,16.7,85,3350,2200,361,68,11691
Oct,2023,Q4,Enterprise Solutions,490000,196000,294000,60.0,63000,83000,47000,36000,229000,65000,13.3,5200,13000,46800,9.6,360,10200,770,636,68,7206
Oct,2023,Q4,Professional Services,238000,95200,142800,60.0,29000,39000,17000,21000,106000,36800,15.5,2800,7360,26640,11.2,158,4100,185,1286,68,3500
Nov,2023,Q4,SaaS Platform,840000,252000,588000,70.0,122000,142000,78000,58000,400000,188000,22.4,7800,38440,141760,16.9,82,3450,2320,362,70,12000
Nov,2023,Q4,Enterprise Solutions,520000,208000,312000,60.0,67000,87000,50000,38000,242000,70000,13.5,5500,14100,50400,9.7,355,10400,815,638,70,7429
Nov,2023,Q4,Professional Services,252000,100800,151200,60.0,31000,41000,18000,22000,112000,39200,15.6,3000,7728,28472,11.3,155,4200,195,1292,70,3600
Dec,2023,Q4,SaaS Platform,895000,268500,626500,70.0,130000,150000,82000,62000,424000,202500,22.6,8200,41145,153155,17.1,80,3550,2480,361,72,12431
Dec,2023,Q4,Enterprise Solutions,555000,222000,333000,60.0,72000,92000,53000,40000,257000,76000,13.7,6000,15400,54600,9.8,350,10600,870,638,72,7708
Dec,2023,Q4,Professional Services,268000,107200,160800,60.0,33000,43000,19000,23000,118000,42800,16.0,3200,8352,31248,11.7,152,4300,205,1307,72,3722
Jan,2024,Q1,SaaS Platform,925000,277500,647500,70.0,135000,155000,85000,64000,439000,208500,22.5,8500,42070,157930,17.1,78,3650,2550,363,75,12333
Jan,2024,Q1,Enterprise Solutions,575000,230000,345000,60.0,75000,95000,55000,42000,267000,78000,13.6,6200,15760,56040,9.7,345,10800,900,639,75,7667
Jan,2024,Q1,Professional Services,280000,112000,168000,60.0,34000,45000,20000,24000,123000,45000,16.1,3300,8770,32930,11.8,150,4400,215,1302,75,3733
Feb,2024,Q1,SaaS Platform,965000,289500,675500,70.0,140000,160000,88000,66000,454000,221500,23.0,8800,44510,168190,17.4,75,3750,2660,363,77,12532
Feb,2024,Q1,Enterprise Solutions,600000,240000,360000,60.0,78000,98000,57000,43000,276000,84000,14.0,6400,16800,60800,10.1,340,11000,940,638,77,7792
Feb,2024,Q1,Professional Services,295000,118000,177000,60.0,36000,47000,21000,25000,129000,48000,16.3,3500,9420,35080,11.9,148,4500,225,1311,77,3831
Mar,2024,Q1,SaaS Platform,1020000,306000,714000,70.0,148000,168000,92000,69000,477000,237000,23.2,9200,47880,179920,17.6,73,3850,2810,363,80,12750
Mar,2024,Q1,Enterprise Solutions,635000,254000,381000,60.0,82000,103000,60000,45000,290000,91000,14.3,6800,18200,66000,10.4,335,11200,990,641,80,7938
Mar,2024,Q1,Professional Services,312000,124800,187200,60.0,38000,49000,22000,26000,135000,52200,16.7,3700,10230,38270,12.3,145,4600,240,1300,80,3900
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📊 CSV Data Summarizer - Claude Skill
A powerful Claude Skill that automatically analyzes CSV files and generates comprehensive insights with visualizations. Upload any CSV and get instant, intelligent analysis without being asked what you want!
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🚀 Features
- 🤖 Intelligent & Adaptive - Automatically detects data type (sales, customer, financial, survey, etc.) and applies relevant analysis
- 📈 Comprehensive Analysis - Generates statistics, correlations, distributions, and trends
- 🎨 Auto Visualizations - Creates multiple charts based on what's in your data:
- Time-series plots for date-based data
- Correlation heatmaps for numeric relationships
- Distribution histograms
- Categorical breakdowns
- ⚡ Proactive - No questions asked! Just upload CSV and get complete analysis immediately
- 🔍 Data Quality Checks - Automatically detects and reports missing values
- 📊 Multi-Industry Support - Adapts to e-commerce, healthcare, finance, operations, surveys, and more
📥 Quick Download
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Get Started in 2 Steps
1️⃣ Download the Skill 
2️⃣ Try the Demo Data 
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📦 What's Included
csv-data-summarizer-claude-skill/
├── SKILL.md # Claude Skill definition
├── analyze.py # Comprehensive analysis engine
├── requirements.txt # Python dependencies
├── examples/
│ └── showcase_financial_pl_data.csv # Demo P&L financial dataset (15 months, 25 metrics)
└── resources/
├── sample.csv # Example dataset
└── README.md # Usage documentation🎯 How It Works
1. Upload any CSV file to Claude.ai 2. Skill activates automatically when CSV is detected 3. Analysis runs immediately - inspects data structure and adapts 4. Results delivered - Complete analysis with multiple visualizations
No prompting needed. No options to choose. Just instant, comprehensive insights!
📥 Installation
For Claude.ai Users
1. Download the latest release: `csv-data-summarizer.zip` 2. Go to Claude.ai → Settings → Capabilities → Skills 3. Upload the zip file 4. Enable the skill 5. Done! Upload any CSV and watch it work ✨
For Developers
git clone git@github.com:coffeefuelbump/csv-data-summarizer-claude-skill.git
cd csv-data-summarizer-claude-skill
pip install -r requirements.txt📊 Sample Dataset Highlights
The included demo CSV contains 15 months of P&L data with:
- 3 product lines (SaaS, Enterprise, Services)
- 25 financial metrics including revenue, expenses, margins, CAC, LTV
- Quarterly trends showing business growth
- Perfect for showcasing time-series analysis, correlations, and financial insights
🎨 Example Use Cases
- 📊 Sales Data → Revenue trends, product performance, regional analysis
- 👥 Customer Data → Demographics, segmentation, geographic patterns
- 💰 Financial Data → Transaction analysis, trend detection, correlations
- ⚙️ Operational Data → Performance metrics, time-series analysis
- 📋 Survey Data → Response distributions, cross-tabulations
🛠️ Technical Details
Dependencies:
- Python 3.8+
- pandas 2.0+
- matplotlib 3.7+
- seaborn 0.12+
Visualizations Generated:
- Time-series trend plots
- Correlation heatmaps
- Distribution histograms
- Categorical bar charts
📝 Example Output
============================================================
📊 DATA OVERVIEW
============================================================
Rows: 100 | Columns: 15
📋 DATA TYPES:
• order_date: object
• total_revenue: float64
• customer_segment: object
...
🔍 DATA QUALITY:
✓ No missing values - dataset is complete!
📈 NUMERICAL ANALYSIS:
[Summary statistics for all numeric columns]
🔗 CORRELATIONS:
[Correlation matrix showing relationships]
📅 TIME SERIES ANALYSIS:
Date range: 2024-01-05 to 2024-04-11
Span: 97 days
📊 VISUALIZATIONS CREATED:
✓ correlation_heatmap.png
✓ time_series_analysis.png
✓ distributions.png
✓ categorical_distributions.png🌟 Connect & Learn More
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</div>
🤝 Contributing
Contributions are welcome! Feel free to:
- Report bugs
- Suggest new features
- Submit pull requests
- Share your use cases
📄 License
MIT License - feel free to use this skill for personal or commercial projects!
🙏 Acknowledgments
Built for the Claude Skills platform by Anthropic.
---
<div align="center">
Made with ❤️ for the AI community
⭐ Star this repo if you find it useful!
</div>
pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0
CSV Data Summarizer - Resources
---
🌟 Connect & Learn More
<div align="center">
🚀 Join Our Community
🔗 All My Links
🛠️ Become a Builder
🐦 Follow on Twitter
</div>
---
Sample Data
The sample.csv file contains example sales data with the following columns:
- date: Transaction date
- product: Product name (Widget A, B, or C)
- quantity: Number of items sold
- revenue: Total revenue from the transaction
- customer_id: Unique customer identifier
- region: Geographic region (North, South, East, West)
Usage Examples
Basic Summary
Analyze sample.csvWith Custom CSV
Here's my sales_data.csv file. Can you summarize it?Focus on Specific Insights
What are the revenue trends in this dataset?Testing the Skill
You can test the skill locally before uploading to Claude:
# Install dependencies
pip install -r ../requirements.txt
# Run the analysis
python ../analyze.py sample.csvExpected Output
The analysis will provide:
1. Dataset dimensions - Row and column counts 2. Column information - Names and data types 3. Summary statistics - Mean, median, std dev, min/max for numeric columns 4. Data quality - Missing value detection and counts 5. Visualizations - Time-series plots when date columns are present
Customization
To adapt this skill for your specific use case:
1. Modify analyze.py to include domain-specific calculations 2. Add custom visualization types in the plotting section 3. Include validation rules specific to your data 4. Add more sample datasets to test different scenarios
date,product,quantity,revenue,customer_id,region
2024-01-15,Widget A,5,129.99,C001,North
2024-01-16,Widget B,3,89.97,C002,South
2024-01-17,Widget A,7,181.98,C003,East
2024-01-18,Widget C,2,199.98,C001,North
2024-01-19,Widget B,4,119.96,C004,West
2024-01-20,Widget A,6,155.94,C005,South
2024-01-21,Widget C,1,99.99,C002,South
2024-01-22,Widget B,8,239.92,C006,East
2024-01-23,Widget A,3,77.97,C007,North
2024-01-24,Widget C,5,499.95,C003,East
2024-01-25,Widget B,2,59.98,C008,West
2024-01-26,Widget A,9,233.91,C004,West
2024-01-27,Widget C,3,299.97,C009,North
2024-01-28,Widget B,6,179.94,C010,South
2024-01-29,Widget A,4,103.96,C005,South
2024-01-30,Widget C,7,699.93,C011,East
2024-01-31,Widget B,5,149.95,C012,West
2024-02-01,Widget A,8,207.92,C013,North
2024-02-02,Widget C,2,199.98,C014,South
2024-02-03,Widget B,10,299.90,C015,East
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