AI coding mission · PDF text extraction

Generate a Python script to extract text from PDFs

Create a reusable Python script for extracting selectable text from PDF documents. Describe your input folder, naming rules and output format, then let a PlushCrew Mission work on the implementation and tests. The result is a project to inspect and download, with test output you can review before processing your own documents.

Try this mission free

Fills the task box; you choose when to send. Guest access: 1 mission per IP in a rolling 24 hours.

Describe the result you need

Decide whether you want one text file per PDF or one combined file. Tell the agents how to order documents, whether to include page boundaries and how to handle a file that cannot be read. Markers such as a filename and page number make the resulting text easier to trace back to its source. Ask for a dry-run or summary if you need to inspect the input list first.

Example prompt

Create a Python script that extracts selectable text from PDFs in a folder and combines it into a UTF-8 text file. Include a filename and page marker, handle unreadable or encrypted PDFs clearly, document dependencies, and add tests with small fixtures.

Example project files and output

The exact files depend on your request. A project for this prompt could include:

extract_pdf_text.pyFolder traversal and text extraction with documented options.
tests/test_extract_pdf_text.pyTests for page markers, empty documents and unreadable files.
requirements.txtThe PDF parsing dependency used by the script.

=== example-report.pdf | page 1 ===
Quarterly notes
This is illustrative extracted text.

=== example-report.pdf | page 2 ===
Next steps

Illustrative UTF-8 output showing document and page markers, not text extracted from an uploaded user document.

What to check in the tests

Review the test output in the mission report and open the generated test files. Useful checks for this task include:

Files and pages appear in a predictable order with traceable markers.
Unreadable files are reported without losing output from the files that succeeded.
The output encoding handles accented names and other Unicode text.

Passing tests show what the supplied cases covered. They do not guarantee that every input will work or that generated code is safe.

Inputs and limitations

Scanned image-only PDFs need OCR; ordinary text extraction does not turn pixels into words. Tables and multi-column layouts may also need additional handling. Say whether those cases matter in your prompt. The mission creates the extraction project; you run it against your local PDF folder after reviewing the code and its dependency instructions.

Review first, then download

Use Mission for a new project. The existing agent pipeline, sandbox and logic checks run as usual. Open each file in the browser, expand the report to inspect test results, then download the ZIP to use the project locally. A dispatched guest mission consumes the free slot even if it fails or you cancel it.

Chat is for questions; guests have five messages per IP in a rolling 24-hour window. For fixing an existing codebase, Audit accepts a code ZIP from a Pro Max account. See plans and limits for account details.

Fill this example task