clean-datalisted

# Clean Data Per-row cleanup on a GTM list before any Explorium match or enrich call. Profile, standardize, validate. Never destructive: raw input is preserved, invalid rows are tagged with reason codes rather than deleted. Out of scope: deduplication and canonical entity resolution (that is what `match-business` and `match-prospects` are for). ## Input `$ARGUMENTS` is a path to a CSV, Excel, or JSON file. Parse the user message for optional sub-inputs: - Schema hints if column names are ambiguous: which column is company name, domain, email, phone, country. - Entity type: companies, contacts, or both. Default: infer from columns. - Whether to run an MX-record check on email domains (slower, requires DNS). Default: off. - Whether to produce a match-ready subset for downstream `match-business` or `match-prospects`. Default: no. Example phrasings: - "Clean this leads CSV before I import it to HubSpot." - "Normalize the company names and domains in /path/accounts.xlsx." - "Validate the emails in this file and tag the bad rows." - "Prep this list for match-prospects, only keep contactable corporate rows." - "Why does my country column have 200 different spellings." ## Workflow 1. **Copy raw input.** Before any transform, copy the input to `./00_raw/<filename>` and never write back. All transforms write to numbered phase folders: `01_profiled/`, `02_standardized/`, `03_validated/`. This is non-negotiable: the single most common failure mode in cleanup work is destructive t