I want to take the repetitive, manual steps out of my current text-data workflow and replace them with a clean, well-documented Python 3 script. The focus is pure automation: the code should read raw text files (or a folder of them), carry out the agreed processing steps—think cleaning, parsing, and structured export—and then hand me a ready-to-use CSV or JSON. I will share a sample dataset and the exact transformation rules once we start, but in broad strokes the script should: • run from the command line with a few clear arguments or a simple config file • handle varying file sizes without choking on memory • rely only on standard libraries or popular, easily installed ones (pandas, regex, nltk, etc.)—list any extras in requirements.txt • log basic progress so I can trace issues quickly • exit gracefully on bad input and flag the error rather than crash Deliverables 1. The Python (.py) script, fully commented. 2. requirements.txt with exact versions if you add dependencies. 3. A short README showing setup, one-line usage, and an example of expected input/output. Acceptance criteria – I can drop new text files into a folder, run one command, and receive the processed output with no errors. – The script reproduces the sample result we agree on at the start. If you have questions about edge cases or performance constraints, let me know early so we can lock in the spec before you dive into coding.