Excel formula builder with step-by-step explanation

Turns a described spreadsheet calculation into a working Excel formula plus a plain-language walkthrough of every function, operator and reference it uses.

PLPromptByte Library@promptbytePublished 1w

TextData & Analysis #data analysis #excel #formulas #spreadsheets

Version 1 of 1 · published Aug 25, 2026Version 1 · only version

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Anthropic · Claude Sonnet 5 PLPromptByte Library Version 1

Prompt

Version 1 · 3 variables · 2 required

Fill in the values below — the customized prompt updates as you type. marks a required value.

Calculation you want{{desired_calculation}}

What the formula should work out, in plain language. e.g. "total revenue per sales rep for Q4 2025 shipped orders only".

Input data / cell references{{input_data}}

Sheet names, column letters and row ranges the formula will read, plus where the result goes.

Conditions and constraints(optional){{constraints}}

Excel version, error handling, performance limits, anything the formula must or must not do. Default: must be robust to blank cells and return 0 instead of an error

Fill in Calculation you want, Input data / cell references to copy the customized prompt.

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All models Claude Sonnet 51

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Claude Sonnet 5 result by PromptByte Library
Anthropic · Claude Sonnet 5 PLPromptByte Library (@promptbyte)Version 1Published 1w
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**Recommended formula** — enter in Summary!B2 and fill down: =IFERROR(SUMIFS(Orders!$D$2:$D$5000, Orders!$B$2:$B$5000, $A2, Orders!$C$2:$C$5000, "Shipped", Orders!$A$2:$A$5000, ">="&DATE(2025,10,1), Orders!$A$2:$A$5000, "<="&DATE(2025,12,31)), 0) **How it works, step by step** 1. SUMIFS(sum_range, criteria_range1, criteria1, ...) adds values from Orders!D2:D5000 only for rows where every criteria pair matches. Unlike SUMIF it takes multiple conditions, so no helper column is needed. 2. Orders!$B$2:$B$5000 = $A2 matches the rep name in column A of the Summary sheet. The $ locks the column…
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Usage notes

Best results come from being specific in input_data: name the sheet, the exact column letters and the row range, rather than saying "my sales table". Put hard requirements in constraints — supported Excel version, error handling, whether the formula must fill down a column.

The same prompt works for Google Sheets if you say so in constraints; ask for the ARRAYFORMULA or QUERY variant there.

Always spot-check the returned formula against a few rows you can verify by hand before applying it to a whole workbook. Models are good at formula shape and weak at the exact boundaries of your data (off-by-one row ranges, mixed date formats, text-formatted numbers).

Attribution: adapted from https://github.com/f/prompts.chat by f (Fatih Kadir Akın), licensed CC0-1.0. Source file: https://github.com/f/prompts.chat/blob/main/PROMPTS.md

Models represented

1 public result from 1 model

text
```csv quarter,line,units_produced,defects,defect_rate_pct,shift_pattern Q1,Line A,14200,318,2.24,"Two shifts, weekdays" Q1,Line B,11850,402,3.39,"Two shifts, weekdays" Q1,Line C,9600,151,1.57,"One shift, weekdays" Q1,Line D,UNCLEAR,88,UNCLEAR,"One shift, weekends" Q2,Line A,15040,295,1.96,"Three shifts, weekdays" Q2,Line B,12310,377,3.06,"Two shifts, weekdays" Q2,Line C,9880,143,1.45,"One shift, weekdays" Q2,Line D,4120,96,2.33,"One shift, weekends" ``` **Notes** - **UNCLEAR cells:** row 5 (Q
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TextData & Analysis Google · Gemini 3.1 Pro
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text
# Checkout redesign A/B test — analysis plan and interpretation ## What the design supports Two independent groups, roughly 6,200 per arm, with a binary primary outcome (completed purchase) and a continuous secondary outcome (order value, right-skewed as order values almost always are). Randomisation at the session level with a 21-day run covering three full weekly cycles is sound. One caveat that limits every conclusion below: sessions, not users, were randomised, so a returning visitor could
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TextData & Analysis Anthropic · Claude Sonnet 5
PL@promptbyte1w
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0 comments1 results
text
## 1. Technical Audit & Business Context | Column | Smell | Business impact | |---|---|---| | `churn_date` | Populated only for churned accounts | This is the label in disguise. Any model that sees it will score ~perfectly in training and be useless in production. | | `mrr` | Right-skewed, a long tail of enterprise accounts | Mean imputation would drag small accounts upward and understate churn risk in the SMB segment, which is where outreach capacity actually sits. | | `signup_date` | Mixed `Y
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TextData & Analysis Anthropic · Claude Opus 5
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0 comments1 results
code · sql
```
**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'
```
Code preview: **Assumptions** - `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed. - "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today. - Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied. - Cancelled orders are excluded. If `status` uses different values than `'
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CodeData & Analysis OpenAI · GPT-5.5
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Excel formula builder with step-by-step explanation

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Table image to clean CSV extraction

Statistical analysis report with hypothesis tests and effect sizes

CSV audit and production-ready cleaning pipeline

SQL query generator from a plain-English request

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Example output

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Resultsfor version 1

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Related prompts

Table image to clean CSV extraction

Statistical analysis report with hypothesis tests and effect sizes

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SQL query generator from a plain-English request

Discussion 0