Code to Prompt Converter
Trim code and errors before pasting.
Keep last: lines (0 keeps everything)
About Code to Prompt Converter
Code to Prompt Converter trims code and error logs into the smallest version an AI still needs. In Clean code mode it strips comments, blank lines, trailing spaces, and shared indentation, with comment removal tuned for JavaScript-family and Python syntax. In Clean error log mode it removes timestamps, ANSI color codes, and repeated lines, and can keep only the last N lines of a long stack trace. Stats show characters before and after, the percentage saved, and a rough token count, so you can see exactly what the cleanup bought you before pasting.
How to Use Code to Prompt Converter
Paste code or a log
Paste into the left panel. Pick Clean code or Clean error log in the toolbar; the option chips change to match the mode.
Choose the cleanup level
For code, toggle Strip comments, Remove blank lines, Dedent, and Trim trailing spaces. For logs, pick timestamp, color, and duplicate removal, and optionally keep only the last lines.
Add your question
Type the actual question in Your question to the AI. It is placed above the code so the model reads intent before detail.
Copy with or without a fence
Leave Markdown code fence on for chat UIs, or off for plain contexts. Click Copy and paste it into your AI chat.
Why Use Code to Prompt Converter: Common Use Cases
Asking about a long file
A 400-line file pasted whole burns context and attention. Stripped to code without comments and blank lines, the same question often fits and lands better.
Debugging from verbose logs
Log lines full of timestamps and color codes hide the error. Clean the log, keep the last 50 lines, and the AI reads the actual failure.
Working inside tight token budgets
The stats row shows the rough token count after cleanup, which matters when a model's context or your per-request cost is the limit.
Sharing snippets in issues and docs
The markdown-fenced output pastes cleanly into GitHub issues and internal wikis, not only AI chats.
Code to Prompt Converter Specifications
| Input Formats | Text (code or logs) |
|---|---|
| Output Formats | Text |
| File Size Limit | No strict limit (dependent on device memory) |
| Processing Engine | 100% Client-side (Runs locally in your browser) |
| Data Retention | Files never leave your device |
| Batch Processing | Single file processing |
Tips for Code to Prompt Converter
Comment stripping is best-effort: it recognizes // and /* */ for JavaScript-family code and # for Python, and skips URLs. Check the result when your code mixes languages or strings that look like comments.
Keep the last 40 to 60 lines for stack traces; the cause usually sits near the end and the AI rarely needs the start of the log.
For APIs that demand machine-readable output, define the response shape with JSON Schema Generator.
Estimate what the cleaned prompt costs per request with AI Cost & Token Calculator.
Frequently Asked Questions
Will stripping comments break my code?
The output is for pasting into a chat, not for running. The heuristics avoid URLs and strings in most cases, but edge cases exist, so never write the cleaned version back over your file.
How does the tool detect Python versus JavaScript?
It looks for Python markers such as def, elif, and self, and the absence of function or const. When in doubt it applies // and /* */ rules. Mixed-language files should be checked manually.
What are ANSI color codes?
Escape sequences terminals use to color log output. They appear as garbage characters when a log is copied out of a terminal, and the error mode removes them.
How is the token count calculated?
Characters divided by four, the usual approximation for English text and most code. Provider tokenizers differ, so treat it as a size comparison, not an invoice.
Does the tool send my code anywhere?
No. All cleanup runs in your browser. Proprietary code, keys accidentally in logs, and internal stack traces stay on your device.
Why place my question above the code?
Models weight the explicit request heavily. Stating the question first, then the evidence, produces more focused answers than a code dump followed by why does this break.