data-cleaning-pass
Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible. Use when asked clean this export, why is my pivot double-counting, these names don't match between sheets, or prep this data for analysis. Produces the profile of what's wrong, the ordered cleaning plan, the join-key repairs, and the cleaning log.
npx skills add mohitagw15856/pm-claude-skills --skill data-cleaning-pass --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 impor
- What This Skill Produces
- Required Inputs
- Framework: The Pass Rules
- Output Format
- The Profile
- The Plan (ordered)
- Join Repair (if joining)
- The Log
- Quality Checks
- Anti-Patterns
What does the data-cleaning-pass skill do?
Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible. Use when asked clean this export, why is my pivot double-counting, these names don't match between sheets, or prep this data for analysis. Produces the profile of what's wrong, the ordered cleaning plan, the join-key repairs, and the cleaning log.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-cleaning-pass --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.