zhuchaowe/qwen_agent
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'master' into onprem

Browse Source
This commit is contained in:
朱潮 2026-03-11 15:58:21 +08:00
commit 2047f11504
2 changed files with 1 additions and 248 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

247
skills/excel-analysis/SKILL.md
View File

@ -1,247 +0,0 @@
---
name: excel-analysis
description: Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
---
# Excel Analysis
## Quick start
Read Excel files with pandas:
```python
import pandas as pd
# Read Excel file
df = pd.read_excel("data.xlsx", sheet_name="Sheet1")
# Display first few rows
print(df.head())
# Basic statistics
print(df.describe())
```
## Reading multiple sheets
Process all sheets in a workbook:
```python
import pandas as pd
# Read all sheets
excel_file = pd.ExcelFile("workbook.xlsx")
for sheet_name in excel_file.sheet_names:
df = pd.read_excel(excel_file, sheet_name=sheet_name)
print(f"\n{sheet_name}:")
print(df.head())
```
## Data analysis
Perform common analysis tasks:
```python
import pandas as pd
df = pd.read_excel("sales.xlsx")
# Group by and aggregate
sales_by_region = df.groupby("region")["sales"].sum()
print(sales_by_region)
# Filter data
high_sales = df[df["sales"] > 10000]
# Calculate metrics
df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]
# Sort by column
df_sorted = df.sort_values("sales", ascending=False)
```
## Creating Excel files
Write data to Excel with formatting:
```python
import pandas as pd
df = pd.DataFrame({
"Product": ["A", "B", "C"],
"Sales": [100, 200, 150],
"Profit": [20, 40, 30]
})
# Write to Excel
writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
df.to_excel(writer, sheet_name="Sales", index=False)
# Get worksheet for formatting
worksheet = writer.sheets["Sales"]
# Auto-adjust column widths
for column in worksheet.columns:
max_length = 0
column_letter = column[0].column_letter
for cell in column:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
worksheet.column_dimensions[column_letter].width = max_length + 2
writer.close()
```
## Pivot tables
Create pivot tables programmatically:
```python
import pandas as pd
df = pd.read_excel("sales_data.xlsx")
# Create pivot table
pivot = pd.pivot_table(
df,
values="sales",
index="region",
columns="product",
aggfunc="sum",
fill_value=0
)
print(pivot)
# Save pivot table
pivot.to_excel("pivot_report.xlsx")
```
## Charts and visualization
Generate charts from Excel data:
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel("data.xlsx")
# Create bar chart
df.plot(x="category", y="value", kind="bar")
plt.title("Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.tight_layout()
plt.savefig("chart.png")
# Create pie chart
df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
plt.title("Market Share")
plt.ylabel("")
plt.savefig("pie_chart.png")
```
## Data cleaning
Clean and prepare Excel data:
```python
import pandas as pd
df = pd.read_excel("messy_data.xlsx")
# Remove duplicates
df = df.drop_duplicates()
# Handle missing values
df = df.fillna(0) # or df.dropna()
# Remove whitespace
df["name"] = df["name"].str.strip()
# Convert data types
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")
# Save cleaned data
df.to_excel("cleaned_data.xlsx", index=False)
```
## Merging and joining
Combine multiple Excel files:
```python
import pandas as pd
# Read multiple files
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")
# Concatenate vertically
combined = pd.concat([df1, df2], ignore_index=True)
# Merge on common column
customers = pd.read_excel("customers.xlsx")
sales = pd.read_excel("sales.xlsx")
merged = pd.merge(sales, customers, on="customer_id", how="left")
merged.to_excel("merged_data.xlsx", index=False)
```
## Advanced formatting
Apply conditional formatting and styles:
```python
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font
# Create Excel file
df = pd.DataFrame({
"Product": ["A", "B", "C"],
"Sales": [100, 200, 150]
})
df.to_excel("formatted.xlsx", index=False)
# Load workbook for formatting
wb = load_workbook("formatted.xlsx")
ws = wb.active
# Apply conditional formatting
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
for row in range(2, len(df) + 2):
cell = ws[f"B{row}"]
if cell.value < 150:
cell.fill = red_fill
else:
cell.fill = green_fill
# Bold headers
for cell in ws[1]:
cell.font = Font(bold=True)
wb.save("formatted.xlsx")
```
## Performance tips
- Use `read_excel` with `usecols` to read specific columns only
- Use `chunksize` for very large files
- Consider using `engine='openpyxl'` or `engine='xlrd'` based on file type
- Use `dtype` parameter to specify column types for faster reading
## Available packages
- **pandas** - Data analysis and manipulation (primary)
- **openpyxl** - Excel file creation and formatting
- **xlrd** - Reading older .xls files
- **xlsxwriter** - Advanced Excel writing capabilities
- **matplotlib** - Chart generation

2
skills/r2-upload/SKILL.md
View File

@ -1,6 +1,6 @@
--- ---
name: r2-upload name: r2-upload
description: Uploads files to Cloudflare R2, AWS S3, or any S3-compatible storage and returns a public or temporary URL. Use when you need to publish assets, share files, or provide upload helpers to other skills. description: Upload files and return a public or temporary URL. Use when you need to publish assets, share files, or provide upload helpers to other skills.
compatibility: Requires Python 3.8+ and PyYAML. Uses AWS SigV4 signing (no external SDK required). compatibility: Requires Python 3.8+ and PyYAML. Uses AWS SigV4 signing (no external SDK required).
metadata: metadata:
author: foundra author: foundra