processing-data

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Processes CSV files and pandas DataFrames. Use when working with CSV files, tabular data, spreadsheets, or when the user asks to query, analyze, or manipulate structured data.

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[Spreadsheets]

When & Why to Use This Skill

This Claude skill provides a robust suite of tools for processing, analyzing, and manipulating structured data using CSV files and pandas DataFrames. It bridges the gap between raw data storage and actionable insights by enabling SQL-based querying via DuckDB, complex pandas operations for data transformation, and comprehensive file management within the Claude environment.

Use Cases

  • Financial Data Analysis: Execute SQL SELECT queries on large CSV transaction logs to filter specific records or calculate totals without leaving the chat.
  • Automated Data Cleaning: Use pandas operations like 'query', 'filter', and 'dropna' to prepare messy spreadsheets for professional reporting.
  • Statistical Summarization: Quickly generate descriptive statistics (mean, sum, count, describe) from tabular data to understand dataset distributions and key metrics.
  • Data Format Conversion: Seamlessly import data from various formats such as Excel, JSON, or Parquet into DataFrames and export them as optimized CSV files.
nameprocessing-data
descriptionProcesses CSV files and pandas DataFrames. Use when working with CSV files, tabular data, spreadsheets, or when the user asks to query, analyze, or manipulate structured data.

Data Processing Tools

Tools for working with CSV files and pandas DataFrames.

CSV Operations

List available CSV files

result = await list_csv_files()
# Returns: {"success": True, "data": ["file1", "file2"], "count": 2}

Read CSV content

result = await read_csv_file("mydata", row_limit=100)
# Returns rows as list of dicts

Query with SQL (DuckDB)

result = await query_csv_file("mydata", "SELECT * FROM mydata WHERE value > 10")

Security: Only SELECT queries allowed. INSERT, UPDATE, DELETE rejected.

Add/remove CSV files

await add_csv_file("/path/to/file.csv")
await remove_csv_file("filename")

Pandas Operations

Create DataFrame

# From CSV
result = await create_pandas_dataframe(
    dataframe_name="sales",
    create_using_function="read_csv",
    function_parameters={"filepath_or_buffer": "data.csv"}
)

# From dict
result = await create_pandas_dataframe(
    dataframe_name="mydf",
    create_using_function="DataFrame",
    function_parameters={"data": {"col1": [1, 2], "col2": [3, 4]}}
)

Allowed functions: DataFrame, read_csv, read_json, read_excel, read_parquet, read_feather, read_orc, read_html, read_xml, read_table, read_sql.

Run operations

# Get first rows
result = await run_dataframe_operation("sales", "head", {"n": 5})

# Filter data
result = await run_dataframe_operation("sales", "query", {"expr": "amount > 100"})

# Get statistics
result = await run_dataframe_operation("sales", "describe", {})

Allowed operations: head, tail, describe, query, filter, groupby, sort_values, mean, sum, count, and 100+ more safe operations.

Export DataFrame

result = await export_dataframe(
    dataframe_name="sales",
    export_function="to_csv",
    export_parameters={"path_or_buf": "output.csv", "index": False}
)

Manage DataFrames

await list_dataframes()           # List all in memory
await get_dataframe_info("sales") # Get details
await delete_dataframe("sales")   # Remove from memory

Security Notes

  • CSV queries: Only SELECT statements allowed
  • Pandas: Operations restricted to allowlist of safe methods
  • No arbitrary code execution via getattr