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Welcome to this collection of prompts designed to transform your Microsoft Excel proficiency. This guide is not just about formulas; It is a strategic tool for analysts, managers and professionals who seek to automate tasks, visualize data in an impactful way and make decisions based on robust analysis. Each prompt is structured to address a real-world challenge, from cleaning data to creating complex financial models. Get ready to take your spreadsheet skills to an elite level, using AI as your expert assistant to generate efficient and clear solutions.
100 resources included
Acts as a Senior Systems Auditor and Data Architecture Expert in Microsoft Excel. My goal is to perform a deep technical intervention to drastically reduce the size of an Excel file that is currently experiencing slowness, constant crashes and an excessive weight of [Current weight in MB]. This file contains [Number of Sheets] tabs and is primarily used for [File Purpose]. I need you to analyze and provide a detailed technical roadmap for "File Weight Optimization", prioritizing data integrity, removing junk metadata, and maximizing calculation speed. First, it performs a thorough diagnosis of common causes of “bloat” in large-scale Excel workbooks. Evaluate the presence of excessive formatting applied to entire rows and columns, the use of duplicate custom styles, and the existence of hidden objects, transparent shapes, or orphaned comments that may be taking up unnecessary space without adding value. Provides an optimized VBA script or Office Script script to reset the 'UsedRange' on all sheets in the workbook, ensuring that Excel does not process cells that are visually empty but maintain formatting metadata that increases the weight of the underlying XML. Secondly, analyze the logical structure and formulas of the book. Identifies whether the massive use of volatile functions such as INDIRECT, UNREF or NOW is contributing not only to file weight but also to processor performance degradation. Suggest more efficient alternatives, such as using INDEX/MATCH, XLOOKUP or implementing Excel Tables (ListObjects) for clean dynamic references. Evaluates the technical possibility of migrating heavy data transformation processes to the Power Query (Get & Transform) engine, allowing the base file to remain lightweight by loading only the compressed data model instead of thousands of rows of intermediate calculations. Finally, it offers a technical comparison on the most optimal saving format for this specific use case. Analyzes the advantages of the .XLSB (Excel Binary Workbook) format compared to the .XLSX standard in terms of compression ratio and opening/saving speed. Include a post-optimization audit section that includes steps to verify that external links, defined names, data validations, or critical conditional formatting have not been broken during the cleanup process. The final result should be a manual for immediate execution to transform an unmanageable file into an agile, professional and efficient tool.
Acts as a Senior Business Strategy Consultant and Data Science Expert. Your mission is to develop an advanced **Monthly Sales Forecast** model for the next fiscal cycle [Year], using a multivariate approach based on the performance history stored in my Excel file. The source data that I will provide you contains the following dimensions: [Month/Year], [Total Sales], [Ad Investment], [Average Ticket] and [Special Events/Holidays]. It is imperative that the analysis begins with a time series decomposition to separate the underlying trend from the recurring seasonality of the months [High Demand Months]. Applies an adjustment factor of [Adjustment Percentage]% to account for the volatility of the current market and the entry of new competitors in the [Industry] sector. To generate the projection, use a multiple linear regression method or triple exponential smoothing (Holt-Winters), depending on which best fits the variance of [Company Name]'s historical data. The forecast must cover a horizon of [Number of Months] months. For each projected month, calculate not only the expected sales figure, but also the expected margin of error and the potential impact of the variable [External Variable, e.g. Exchange Rate] in the final result. The final deliverable must be a table formatted for Excel that includes: 1. Month, 2. Base Forecast, 3. Optimistic Scenario (+[X]%), 4. Pessimistic Scenario (-[X]%), and 5. Assumption notes. Also, provide me with the exact formulas I need to paste into the Excel cells (using functions like FORECAST.ETS, INDEX, or MATCH) so that the model is dynamic and updates automatically when I enter the actual data for the past month. It ends with a brief 3-point executive summary on how to interpret these results for decision making in the [Department, e.g. Purchasing or Finance].
Acts as a Senior Supply Chain Consultant and expert in financial modeling in Excel to design an advanced operational expense audit and control tool. The objective is to build a logical structure that allows "Logistics Cost Calculation" in a granular manner, identifying capital leaks and optimizing Cost-to-Serve for the organization [Company Name]. The model must be capable of processing large volumes of data and transforming them into actionable management indicators. In the first phase of the design, establish the Transportation Costs section. You must integrate variables for primary (supply) and secondary (capillary distribution) freight. Includes columns for calculating fuel, tolls, fleet preventive maintenance, cargo insurance and driver salaries. It uses the variables [Monthly Freight Volume] and [Cost per Kilometer] to derive the cost per transported unit and the cost per ton-kilometer, allowing the efficiency of outsourced carriers to be compared to the own fleet. In the second phase, develop the Storage and Internal Operation Costs module. This section must break down the cost per square meter or pallet position, considering the rental fee, public services, security and depreciation of assets (racks, forklifts). It is essential to include the cost of direct and indirect warehouse labor based on the [Number of Operators] and their [Total Labor Cost]. Additionally, it integrates the calculation of the opportunity cost of inventory using the [Annual Interest Rate] and the average stock value to determine how much money is financially tied up. The third phase should focus on Administrative and Order Management Costs. Here you should capture the expenses of order processing, information systems (WMS/ERP), and customer service. Implements a calculation logic for the 'Cost per Perfect Order' and the 'Reverse Logistics Cost' (returns), using the [Percentage of Monthly Returns] parameter. The objective is for the model to add all these components to obtain the Total Logistics Cost, which must also be expressed as a percentage of the [Total Net Sales] of the period. To conclude, ask the model to generate a sensitivity analysis table and a KPI Dashboard. The sensitivity table should show how the operating margin varies with fluctuations in the [Fuel Price] or changes in [Warehouse Occupancy]. The Dashboard must include visual alerts (conditional format) that are activated when the unit logistics cost exceeds the [Maximum Cost Target] defined by the financial management for the [Analysis Period].