Build your first AI-assisted tool: a CSV checker you can actually use
A complete beginner-friendly coding project: specify a CSV checker, build a local interface, handle messy inputs and verify downloadable results.
An ordinary spreadsheet annoyance is a very good software idea.
Suppose you receive a CSV export and want to know whether it contains missing values or repeated rows. You could inspect it manually every time. You could also build a small tool that performs the same checks and gives you an exceptions report. The live showcase on this site is a deliberately small example of that idea.
Define exactly what “check” means
A repeated customer name is not necessarily a duplicate transaction. A blank discount field may be valid. Decide what this first version can establish mechanically: the number of data rows, blank cells, rows with the wrong number of fields, and exact repeated rows. Label those as signals for review. Do not silently delete information.
Separate a warning from a correction. The initial tool should help a person inspect the file, leaving the original intact. A later cleaning step needs its own rule, such as “keep the earliest row for each transaction ID.” That rule belongs to the data owner, not a guess inferred from a tidy-looking table.
Give the agent a behavior specification
Start with synthetic data and ask for local processing. Describe the limit: a small UTF-8 comma-separated file, with a header row. Decide how the interface should handle quoted commas and empty files. A scoped format is easier to implement and explain than claiming to handle every spreadsheet export.
Ask for visible examples of both an ordinary file and a broken one. The UI should name what was checked and show the rows that need attention. Downloads should explain what they contain. This is where a short specification saves time: every label has a job.
A request you can adapt
Build a browser-based CSV inspection tool. Accept pasted CSV and an explicitly selected UTF-8 file up to 200 KB. Process the content in the browser without sending it to a service. Require a header. Handle quoted commas, escaped quotes and CRLF. Report row counts, blank cells, uneven rows and exact duplicate rows; do not clean automatically. Offer a plain-text report. Include a synthetic example and clear empty/error states. Test the parser independently and the main user flow in a browser.
Test the assumptions people will hit
Give the agent a row containing a comma inside a quoted name. Add a line break inside a quoted field. Try a quote that never closes. Add an exact duplicate row, then another row that differs by one space. Ask whether the tool trims values before comparing and make the chosen rule visible.
Test the interface as well as the parser. Select a file and then choose a different one. Remove all input. Use the sample again. Download the report and read it outside the browser. A correct parsing function does not prevent a stale result panel from showing the analysis of the previous file.
Grow it around a real recurring need
After the inspector works, choose one extension based on the export you actually use. You might add required columns, a date-format check or a comparison between two periods. Keep a tiny set of known examples that exercise the rules. Run them whenever the parser changes.
The interesting step is taking a repeated irritation seriously enough to give it an interface. You can make a vocabulary drill from a word list, a reading dashboard from saved links or a practice planner from a schedule using the same approach: define inputs, build one useful behavior, check edge cases, then expand.
Take this into your next task
The first version should make a narrow claim and prove it. Small, working software is a better foundation than a large mockup.
Questions people ask
Is the showcase powered by a live AI model?
No. It is a working browser tool built with AI assistance. The parser and checks run as ordinary code. This makes the behavior repeatable and lets you inspect what the tool does.
Sources and editorial notes
Reviewed September 6, 2026. Product capabilities depend on the app, plan, region and workspace settings. Workflows and prompts are authored teaching material, not recorded model results or measured time-saving claims.
Keep going
- AI Showcase: Working Tools and Project Blueprints
- Debugging and Testing with Codex
- AI for Spreadsheets: From Export to Checked Report
Related Power of AI pages
Keep reading with Start here, everyday uses, the tool guide, the writing workshop, and sources and standards.