> ## Documentation Index
> Fetch the complete documentation index at: https://docs.newenergycoder.club/llms.txt
> Use this file to discover all available pages before exploring further.

# data-analysis

> Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot…

| 项目 | 内容                                                                                                                                                                                                                                                                   |
| -- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 来源 | [skills/data-analysis/SKILL.md](https://github.com/new-energy-coder-club/new_energy_coder_club/blob/Skill/skills/data-analysis/SKILL.md)                                                                                                                             |
| 分类 | TRAE 内置 & 独立工具                                                                                                                                                                                                                                                       |
| 安装 | 克隆 [Skill 分支](https://github.com/new-energy-coder-club/new_energy_coder_club/tree/Skill) 后，将 `skills/data-analysis/` 目录复制到 `~/.claude/skills/`（Claude Code）、`~/.trae/skills/`（Trae IDE）或 `~/.qclaw/skills/`（QClaw/OpenClaw），详见 [Skill 安装方式](/community/skill-branch) |

# Data Analysis Skill

## Overview

This skill analyzes user-uploaded Excel/CSV files using DuckDB — an in-process analytical SQL engine. It supports schema inspection, SQL-based querying, statistical summaries, and result export, all through a single Python script.

## Core Capabilities

* Inspect Excel/CSV file structure (sheets, columns, types, row counts)
* Execute arbitrary SQL queries against uploaded data
* Generate statistical summaries (mean, median, stddev, percentiles, nulls)
* Support multi-sheet Excel workbooks (each sheet becomes a table)
* Export query results to CSV, JSON, or Markdown
* Handle large files efficiently with DuckDB's columnar engine

## Workflow

### Step 1: Understand Requirements

When a user uploads data files and requests analysis, identify:

* **File location**: Path(s) to uploaded Excel/CSV files under `/mnt/user-data/uploads/`
* **Analysis goal**: What insights the user wants (summary, filtering, aggregation, comparison, etc.)
* **Output format**: How results should be presented (table, CSV export, JSON, etc.)
* You don't need to check the folder under `/mnt/user-data`

### Step 2: Inspect File Structure

First, inspect the uploaded file to understand its schema:

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action inspect
```

This returns:

* Sheet names (for Excel) or filename (for CSV)
* Column names, data types, and non-null counts
* Row count per sheet/file
* Sample data (first 5 rows)

### Step 3: Perform Analysis

Based on the schema, construct SQL queries to answer the user's questions.

#### Run SQL Query

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action query \
  --sql "SELECT category, COUNT(*) as count, AVG(amount) as avg_amount FROM Sheet1 GROUP BY category ORDER BY count DESC"
```

#### Generate Statistical Summary

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action summary \
  --table Sheet1
```

This returns for each numeric column: count, mean, std, min, 25%, 50%, 75%, max, null\_count.
For string columns: count, unique, top value, frequency, null\_count.

#### Export Results

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/data.xlsx \
  --action query \
  --sql "SELECT * FROM Sheet1 WHERE amount > 1000" \
  --output-file /mnt/user-data/outputs/filtered-results.csv
```

Supported output formats (auto-detected from extension):

* `.csv` — Comma-separated values
* `.json` — JSON array of records
* `.md` — Markdown table

### Parameters

| Parameter       | Required      | Description                              |
| --------------- | ------------- | ---------------------------------------- |
| `--files`       | Yes           | Space-separated paths to Excel/CSV files |
| `--action`      | Yes           | One of: `inspect`, `query`, `summary`    |
| `--sql`         | For `query`   | SQL query to execute                     |
| `--table`       | For `summary` | Table/sheet name to summarize            |
| `--output-file` | No            | Path to export results (CSV/JSON/MD)     |

> \[!NOTE]
> Do NOT read the Python file, just call it with the parameters.

## Table Naming Rules

* **Excel files**: Each sheet becomes a table named after the sheet (e.g., `Sheet1`, `Sales`, `Revenue`)
* **CSV files**: Table name is the filename without extension (e.g., `data.csv` → `data`)
* **Multiple files**: All tables from all files are available in the same query context, enabling cross-file joins
* **Special characters**: Sheet/file names with spaces or special characters are auto-sanitized (spaces → underscores). Use double quotes for names that start with numbers or contain special characters, e.g., `"2024_Sales"`

## Analysis Patterns

### Basic Exploration

```sql theme={null}
-- Row count
SELECT COUNT(*) FROM Sheet1

-- Distinct values in a column
SELECT DISTINCT category FROM Sheet1

-- Value distribution
SELECT category, COUNT(*) as cnt FROM Sheet1 GROUP BY category ORDER BY cnt DESC

-- Date range
SELECT MIN(date_col), MAX(date_col) FROM Sheet1
```

### Aggregation & Grouping

```sql theme={null}
-- Revenue by category and month
SELECT category, DATE_TRUNC('month', order_date) as month,
       SUM(revenue) as total_revenue
FROM Sales
GROUP BY category, month
ORDER BY month, total_revenue DESC

-- Top 10 customers by spend
SELECT customer_name, SUM(amount) as total_spend
FROM Orders GROUP BY customer_name
ORDER BY total_spend DESC LIMIT 10
```

### Cross-file Joins

```sql theme={null}
-- Join sales with customer info from different files
SELECT s.order_id, s.amount, c.customer_name, c.region
FROM sales s
JOIN customers c ON s.customer_id = c.id
WHERE s.amount > 500
```

### Window Functions

```sql theme={null}
-- Running total and rank
SELECT order_date, amount,
       SUM(amount) OVER (ORDER BY order_date) as running_total,
       RANK() OVER (ORDER BY amount DESC) as amount_rank
FROM Sales
```

### Pivot-style Analysis

```sql theme={null}
-- Pivot: monthly revenue by category
SELECT category,
       SUM(CASE WHEN MONTH(date) = 1 THEN revenue END) as Jan,
       SUM(CASE WHEN MONTH(date) = 2 THEN revenue END) as Feb,
       SUM(CASE WHEN MONTH(date) = 3 THEN revenue END) as Mar
FROM Sales
GROUP BY category
```

## Complete Example

User uploads `sales_2024.xlsx` (with sheets: `Orders`, `Products`, `Customers`) and asks: "Analyze my sales data — show top products by revenue and monthly trends."

### Step 1: Inspect the file

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/sales_2024.xlsx \
  --action inspect
```

### Step 2: Top products by revenue

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/sales_2024.xlsx \
  --action query \
  --sql "SELECT p.product_name, SUM(o.quantity * o.unit_price) as total_revenue, SUM(o.quantity) as total_units FROM Orders o JOIN Products p ON o.product_id = p.id GROUP BY p.product_name ORDER BY total_revenue DESC LIMIT 10"
```

### Step 3: Monthly revenue trends

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/sales_2024.xlsx \
  --action query \
  --sql "SELECT DATE_TRUNC('month', order_date) as month, SUM(quantity * unit_price) as revenue FROM Orders GROUP BY month ORDER BY month" \
  --output-file /mnt/user-data/outputs/monthly-trends.csv
```

### Step 4: Statistical summary

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/sales_2024.xlsx \
  --action summary \
  --table Orders
```

Present results to the user with clear explanations of findings, trends, and actionable insights.

## Multi-file Example

User uploads `orders.csv` and `customers.xlsx` and asks: "Which region has the highest average order value?"

```bash theme={null}
python /mnt/skills/public/data-analysis/scripts/analyze.py \
  --files /mnt/user-data/uploads/orders.csv /mnt/user-data/uploads/customers.xlsx \
  --action query \
  --sql "SELECT c.region, AVG(o.amount) as avg_order_value, COUNT(*) as order_count FROM orders o JOIN Customers c ON o.customer_id = c.id GROUP BY c.region ORDER BY avg_order_value DESC"
```

## Output Handling

After analysis:

* Present query results directly in conversation as formatted tables
* For large results, export to file and share via `present_files` tool
* Always explain findings in plain language with key takeaways
* Suggest follow-up analyses when patterns are interesting
* Offer to export results if the user wants to keep them

## Caching

The script automatically caches loaded data to avoid re-parsing files on every call:

* On first load, files are parsed and stored in a persistent DuckDB database under `/mnt/user-data/workspace/.data-analysis-cache/`
* The cache key is a SHA256 hash of all input file contents — if files change, a new cache is created
* Subsequent calls with the same files will use the cached database directly (near-instant startup)
* Cache is transparent — no extra parameters needed

This is especially useful when running multiple queries against the same data files (inspect → query → summary).

## Notes

* DuckDB supports full SQL including window functions, CTEs, subqueries, and advanced aggregations
* Excel date columns are automatically parsed; use DuckDB date functions (`DATE_TRUNC`, `EXTRACT`, etc.)
* For very large files (100MB+), DuckDB handles them efficiently without loading everything into memory
* Column names with spaces are accessible using double quotes: `"Column Name"`