Data Cleaning Pass Skill

Dirty data doesn't announce itself — it double-counts in the pivot, drops rows in the join, and averages text as zero. Cleaning done ad hoc corrupts as it corrects (the dedupe that removed real records, the find-replace that hit the wrong column). The pass is methodical: profile first (what's actually wrong, counted), fix in an order where each step doesn't mask the next, keep the original untouched, and log every transformation — because "how did you get these numbers" deserves an answer.

What This Skill Produces

• The profile — per column: type consistency, blank/error counts, distinct-value sanity, the weirdest values surfaced
• The cleaning plan — ordered fixes with their methods, run on a copy
• The join-key repair — the match-rate before/after when sheets must link
• The cleaning log — what changed, how many rows/cells, by what rule — the defensibility artifact

Required Inputs

Ask for these if not provided:

• The data — the sheet/export, and where it came from (system exports have signature messes: leading zeros eaten, dates re-typed, thousands separators as text)
• The destination — a pivot, a join, a chart, an import; the destination defines "clean enough" (a join needs perfect keys; a chart needs consistent types)
• The authority questions — when duplicates conflict (two rows, same customer, different phone), which source wins? Cleaning makes merge decisions; someone must own the rule

Framework: The Pass Rules