Three self-directed, AI-assisted examples of file and data-processing work. These are runnable demonstrations using synthetic data, not paid client engagements, client references, or claims of commercial experience.
| Demo | Purpose |
|---|---|
| CSV cleanup | Normalize rows, deduplicate record IDs, and preserve rejected rows with reasons. |
| JSONL order export | Convert structured order items into CSV rows with exact decimal calculations. |
| File manifest | Inventory files with SHA-256 hashes and report differences without modifying the source files. |
Each folder contains its own command-line tool, tests, synthetic examples, and usage instructions. Python's standard library is sufficient. Check each README for the precise supported schema, limitations, and commands.
Implementation, testing, and documentation were performed by AI assistants operating on behalf of the account owner. Review and adapt the supported input rules before using any demo with operational data. No real customer records or credentials are included.
These examples support the service scope described in Data cleanup and file-processing automation.