Excel Reframe — Clean, Merge & Reformat

    1

    Local CLI-based skill to clean, merge, and transform messy Excel/CSV data and calculation lines into structured files.

    $19

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Excel Reframe — Clean, Merge & Reformat

    Example session with this skill installed

    I have two sales exports, sales_jan.csv and sales_feb.csv. Merge them, remove duplicates, and give me a summary of the final row count.

    • Read your context and instructions
    • Compiled the excel reframe
    • Generated the data export

    I have merged the two sales exports and cleaned the resulting dataset. The duplicates have been removed, and the data is now consolidated into a single XLSX file.

    Data Processing Summary

    ActionDetailResult
    Mergesales_jan.csv, sales_feb.csvSuccess
    DeduplicationRows removed42
    CleaningWhitespace trimmedYes
    Final Row CountTotal records1,850
    Output PathLocal file./merged_sales_cleaned.xlsx

    Profile Report
    The data profiling step confirms that all headers match across both inputs. No merged cells were detected. The output file contains values only, as per the safety constraints.

    Next steps

    1. Open merged_sales_cleaned.xlsx to verify the column alignment.
    2. Use the profile command if you need a deeper breakdown of specific column types.
    3. Run the parse command if you have additional manual entry notes to add to this sheet.

    sales_jan.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Messy Excel exports and fragmented CSVs often require manual cleanup before they are usable in a pipeline. This skill automates the local processing of tabular data, handling everything from duplicate removal to transforming raw text calculations into structured workbooks.

    What it does

    • Parse raw text to convert unstructured lines or calculation strings into formatted XLSX files.
    • Clean workbooks by deduplicating rows, trimming whitespace, and dropping empty records automatically.
    • Merge datasets by combining multiple CSV or XLSX files sharing a common header structure.
    • Profile data to generate summary reports on row counts, merged cells, and potential formatting issues without modifying the source.

    How it works

    1. Analyze input to determine if the source is a file path or raw text lines requiring parsing.
    2. Execute scripts using specific flags like --dedupe or --mode calc to process the data locally.
    3. Verify output by checking row counts and ensuring all formulas were flattened to values-only for compatibility.
    4. Report summary providing a JSON result of rows processed and actions taken.

    Frameworks & tools

    The skill requires Python and the openpyxl library. It operates entirely on local file paths to avoid cloud dependency.

    Why this beats prompting it yourself

    Prompting an AI to "clean a file" often results in the agent trying to write custom one-off scripts that fail on large datasets or mixed delimiters. This skill provides a standardized, tested CLI interface that handles edge cases like merged cells and safe expression evaluation reliably.

    Use cases

    • Merge monthly sales CSV exports into a single master workbook.
    • Convert thousands of text-based calculation logs into a structured table with results.
    • Sanitize messy CRM exports by removing duplicates and trimming whitespace.
    • Profile large workbooks to identify empty rows before importing into a database.

    Known limitations

    Does not support complex BI dashboards, pivot tables, or password-protected workbooks. Output files are values-only and do not preserve original Excel formulas or complex cell merging.

    Requires local Python environment with openpyxl. Does not support password-protected files or preserve complex formatting/formulas. Output is values-only.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Security scan passed 16 days ago

    Permissions

    Terminal / Shell
    Read Files
    Listed16 days ago

    What's inside

    Frequently Asked Questions

    This skill supports standard .xlsx and .csv formats, making it ideal for processing CRM exports, sales logs, and raw text calculation strings. It focuses on cleaning and restructuring the data rather than handling complex objects like pivot tables or macro-enabled workbooks.

    Unlike generic prompts, this skill uses a standardized CLI interface to handle edge cases like merged cells and large datasets that often crash one-off AI scripts. It ensures your data remains local and avoids the reliability issues common when asking an AI to write custom code from scratch.

    This is a local, CLI-based skill that requires a Python environment with the openpyxl library installed. It is designed to be called by any AI agent capable of executing local scripts, ensuring your sensitive data never leaves your machine.

    The skill flattens all formulas into values-only results during the transformation process to ensure maximum compatibility with downstream databases and tools. While it will process the results of your calculations, it will not preserve the original Excel formulas in the final output file.

    You will receive the full CLI toolset, the necessary Python scripts, and a detailed integration guide to connect the skill to your preferred AI agent. The package includes all logic for deduplication, merging, and data profiling.

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