You will get a clean and reliable Excel spreadsheet with QA notes
Project details
You will receive a cleaned, reliable spreadsheet plus a concise QA note. I preserve the original file, standardize dates and text, check duplicate keys and missing values, and reconcile input and output row counts. The $15 starter tier covers one CSV, Excel, or Google Sheets file up to 500 rows, with one revision and delivery within two days after scope confirmation.
Data Entry Type
Data Cleansing, Error DetectionData Entry Tool
Google Sheets, Microsoft ExcelWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$75
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 1 | 1 |
Number of Hours of Work | 1 | 2 | 4 |
Formatting & Clean Up | |||
Graph & Table Creation | - | - | - |
About Agas
Spreadsheet Reliability & Python Automation Specialist
Tel Aviv, Israel - 3:26 pm local time
Spreadsheet Reliability & Python Automation Specialist
Remote | Asynchronous, text-first collaboration | Portfolio available on request
PROFILE
I build practical tools for spreadsheet, file, and browser-based workflows. My work covers data cleanup, validation,
recurring report preparation, Python scripting, API connections, and controlled browser automation. I work from written
requirements and sample files, then deliver a tested result with concise documentation and a QA summary.
WORKING METHOD
1. Review the source artifact.
2. Confirm output and acceptance rules in
writing.
3. Work on a copy or test environment.
4. Test normal and failure cases.
5. Deliver the artifact, QA notes, exceptions,
and instructions.
No meeting is normally required. Communication, scope
Steps for completing your project
After purchasing the project, send requirements so Agas can start the project.
Delivery time starts when Agas receives requirements from you.
Agas works on your project following the steps below.
Revisions may occur after the delivery date.
Clean, validate, and return files
I will preserve the original, clean and standardize the data, check duplicate keys and row counts, then return the corrected file with a concise QA note.