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Choose a file or folder, then launch a tool to get started.

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Files are processed locally in your browser and never uploaded.

Quick actions

Run a check or get the resources you need.

Validation

RSF Test

Run the RSF script on the selected file(s).

Resources

ICAI Download

Open the download workflow for ICAI resources.

Validation

Payee Test

Run the Payee script on the selected file(s).

Lookup

GSTIN Check

Check the structure of a GSTIN identifier.

Ready. Select a file, then run a test.

Script output

Scripts & language

Your script runs inside the browser on the selected file(s). JavaScript works instantly and offline. Python uses an in-browser Python runtime that is downloaded from the internet on first use (about 10 MB) and may be blocked by some company networks. Edits are saved in this browser.

Run scripts with:

// Port of rsfrevisedscript.py (runs offline, no libraries needed).
// Built-in helpers:  xlsx.read(file) -> { "Sheet name": [[row values]] }
//                    xlsx.write({...}) -> Blob        download(name, blob)
const RSF_THRESHOLD = 50;                 // keep only customers with RSF above this value
const INPUT_SHEET = "RSF Input Data";
const OUTPUT_FILE = "Customer RSF Output.xlsx";

const input = files.find(f => /\.xlsx$/i.test(f.name));
if (!input) throw new Error("Please select an .xlsx file (e.g. Customer sales values.xlsx).");

const book = await xlsx.read(input.file);
const rows = book[INPUT_SHEET];
if (!rows) throw new Error(`Sheet "${INPUT_SHEET}" not found. Sheets in this file: ${Object.keys(book).join(", ")}`);

// Columns A:B -> Customer Name, Value (row 1 is the header)
const groups = new Map();                 // grouped without case sensitivity
for (let i = 1; i < rows.length; i++) {
  const r = rows[i] || [];
  const name = r[0] == null ? "" : String(r[0]).trim();
  let value = r[1];
  if (typeof value === "string") value = value.trim() === "" ? NaN : Number(value);
  if (typeof value !== "number" || !Number.isFinite(value)) continue;
  if (name === "") continue;
  const key = name.toLowerCase();
  if (!groups.has(key)) groups.set(key, { name, values: [] });
  groups.get(key).values.push(value);
}

const results = [];
for (const { name, values } of groups.values()) {
  values.sort((a, b) => b - a);
  if (values.length < 2) continue;        // need at least two values
  const [max1, max2] = values;
  if (max2 === 0) continue;               // non-zero second maximum
  const rsf = max1 / max2;
  if (rsf > RSF_THRESHOLD) results.push([name, max1, max2, rsf]);
}
results.sort((a, b) => b[3] - a[3]);      // RSF high to low

const blob = xlsx.write({
  sheetName: "RSF Output",
  headers: ["Customer Name", "Maximum 1 Value", "Maximum 2 Value", "RSF"],
  rows: results,
  widths: [30, 20, 20, 22],
});
download(OUTPUT_FILE, blob);

print("RSF calculation completed.");
print(`Customers with RSF above ${RSF_THRESHOLD}: ${results.length}`);
print(`Output saved to: ${OUTPUT_FILE}  (use the download button below)`);
if (results.length) {
  print("");
  print("Top results:");
  results.slice(0, 10).forEach(r => print(`  ${r[0]}  |  ${r[1]}  |  ${r[2]}  |  ${r[3].toFixed(2)}`));
}

import sys
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Font, PatternFill

# The file you select in the page is passed in as sys.argv[1]
input_file = sys.argv[1] if len(sys.argv) > 1 else "Customer sales values.xlsx"
output_file = "Customer RSF Output.xlsx"
RSF_THRESHOLD = 50  # keep only customers with RSF above this value

# Read input worksheet
df = pd.read_excel(
    input_file,
    sheet_name="RSF Input Data",
    usecols="A:B"
)

df.columns = ["Customer Name", "Value"]
df["Customer Name"] = df["Customer Name"].astype("string").str.strip()
df["Value"] = pd.to_numeric(df["Value"], errors="coerce")

df = df[
    df["Customer Name"].notna()
    & (df["Customer Name"] != "")
    & df["Value"].notna()
].copy()

# Group customer names without case sensitivity
df["Customer Key"] = df["Customer Name"].str.casefold()

results = []

for _, group in df.groupby("Customer Key", sort=False):
    name = group["Customer Name"].iloc[0]
    values = sorted(group["Value"].tolist(), reverse=True)

# Need at least two values, and a non-zero second maximum
    if len(values) < 2:
        continue

max1, max2 = values[0], values[1]
    if max2 == 0:
        continue

rsf = max1 / max2

# Keep only customers whose RSF is above the threshold
    if rsf > RSF_THRESHOLD:
        results.append({
            "Customer Name": name,
            "Maximum 1 Value": max1,
            "Maximum 2 Value": max2,
            "RSF": rsf
        })

# Build output (empty-safe, sorted by RSF high to low)
output = pd.DataFrame(
    results,
    columns=["Customer Name", "Maximum 1 Value", "Maximum 2 Value", "RSF"]
)
output = output.sort_values("RSF", ascending=False).reset_index(drop=True)

# Create a separate workbook
with pd.ExcelWriter(output_file, engine="openpyxl") as writer:
    output.to_excel(
        writer,
        sheet_name="RSF Output",
        index=False
    )

# Format output worksheet
wb = load_workbook(output_file)
ws = wb["RSF Output"]

ws.freeze_panes = "A2"
ws.auto_filter.ref = ws.dimensions

for cell in ws[1]:
    cell.font = Font(bold=True)
    cell.fill = PatternFill("solid", fgColor="D9E2F3")

for col, width in {"A": 30, "B": 20, "C": 20, "D": 22}.items():
    ws.column_dimensions[col].width = width

for row in ws.iter_rows(min_row=2):
    for cell in row[1:]:
        if isinstance(cell.value, (int, float)):
            cell.number_format = "#,##0.00"

wb.save(output_file)

print("RSF calculation completed.")
print(f"Customers with RSF above {RSF_THRESHOLD}: {len(output)}")
print(f"Output saved to: {output_file}")