--- name: list-cleaning description: "Use this skill when a prospect or contact list needs cleaning before outreach or import: \"clean this CSV\", \"dedupe my leads\", \"fix the names and titles\", \"why are my bounces high\". Normalises names, titles, companies and domains, removes duplicates, invalid and risky emails, role addresses, customers and opt-outs, flags stale contacts, and outputs a send-ready list with a cleaning report. Protects sender reputation and stops embarrassing merge-field mistakes." license: MIT metadata: title: List cleaning category: prospecting tags: "RevOps, SDR, Founders" author: prosyo version: "1.0.0" keywords: "clean lead list, dedupe contacts, email list hygiene, claude data cleaning skill, crm import cleanup" inputs: "Raw list (CSV or export), Customer and opt-out lists, Target campaign" outputs: "Clean send-ready list, Cleaning report, Rows needing review" --- Use this before any list touches a sequence or a CRM import. It produces a clean list and a report of exactly what was removed or changed and why. ## Inputs - The raw list. - Suppression sources: customers, open opportunities, opt-outs, do-not-contact, recent contacts. - The campaign it is for, so you know which columns must be filled. ## The play ### 1. Profile the file Count rows, columns, blanks per column, and obvious junk (test rows, header repeats, rows with only an email). Note the encoding problems: broken accents in names are common and visible in a first line. ### 2. Normalise - **Names**: proper case, strip titles and suffixes (Dr., PhD, MBA), split full names, fix all-caps. Keep particles like "van", "de", "Mc" correct. - **Companies**: strip legal suffixes for display (Inc., Ltd, GmbH, LLC) while keeping the original in another column. "Acme Holdings Inc." becomes "Acme" in an email. - **Titles**: keep the original, add a normalised seniority and function column. - **Domains**: lower case, strip www and paths, derive from email when missing. Read [references/cleaning-rules.md](references/cleaning-rules.md) for the full normalisation and risk rules with edge cases. ### 3. Deduplicate Match on email first, then on LinkedIn URL, then on first name + last name + domain. Keep the most complete record and merge the rest. ### 4. Remove risk Drop invalid emails, disposable domains, role addresses (info@, sales@, admin@, support@), personal domains for B2B campaigns, and anything that bounced before. Flag catch-all domains rather than dropping them; decide by campaign size. ### 5. Suppress Remove customers, open deals, opt-outs and anyone contacted in the last 90 days. Match suppression on email and on domain where the list is account-level. ### 6. Report Show what happened to every removed row and hand back a list of rows that need a human decision. ## Output format ``` Input: N rows → Output: N send-ready rows Fixed: N names, N company names, N domains Removed: N duplicates | N invalid | N role | N personal | N disposable | N suppressed (by reason) Flagged for review: N catch-all | N missing first name | N possible job change Files: clean list, removed rows with reason, review list ``` ## What good looks like - A random 20-row sample reads perfectly in a merge preview: "Hi Ana," at "Northwind", never "Hi ANA" at "Northwind Holdings, Inc.". - Removed rows keep their reason, so nothing disappears silently. - Bounce rate on the cleaned list stays under 2%. - The common mistake is deduplicating only on email, which leaves the same person in the list twice with a work and an old address. ## Rules - MUST keep the original values alongside normalised ones. - MUST output a removed-rows file with reasons. - NEVER delete records from the user's CRM; cleaning produces new files or proposed changes for approval. - NEVER guess an email address and mark it as verified. ## Example prompts - "Clean this 3,500-row CSV for a cold email campaign." - "Dedupe these two lists and remove our customers." - "Fix the names and company names so merge fields look natural." --- # Cleaning rules ## Names | Raw | Clean | Rule | | --- | --- | --- | | JOHN SMITH | John Smith | Proper case all-caps names | | john o'neil | John O'Neil | Capitalise after apostrophe | | Dr. Sarah Lee, PhD | Sarah Lee | Strip honorifics and post-nominals | | Ludwig van Beethoven | Ludwig van Beethoven | Keep lowercase particles: van, von, de, da, di, le | | mcdonald | McDonald | Mc and Mac prefixes (check Mack, Macy exceptions) | | J. | (flag) | Initial only: not usable as a first name | | Jos├⌐ | José | Fix encoding before anything else | ## Companies Strip for display: Inc, Inc., LLC, Ltd, Ltd., Limited, GmbH, AG, SA, S.A., BV, Pty Ltd, PLC, Corp, Corporation, Co., Holdings, Group (only when it is a suffix and the remainder is still recognisable). Keep: "The" at the start only when it is how the company refers to itself. Brand casing like "eBay" or "iRobot" should be kept exactly as the company writes it on its site. ## Email risk | Status | Action for cold email | | --- | --- | | Valid | Keep | | Invalid, unknown mailbox | Remove | | Catch-all (accept-all domain) | Keep for small Tier 1 lists; remove for volume campaigns or send to them last | | Role address (info@, sales@, hello@, admin@, support@, careers@) | Remove | | Disposable or temporary domain | Remove | | Personal domain on a B2B list | Remove, or keep only if the person is a sole founder and the domain is their business | | Previously bounced | Remove | ## Stale contacts Flag for review when: - The last verification is over 90 days old. - The title or company in the list differs from the current public profile. - The email domain differs from the company domain (possible acquisition or old job). ## Duplicate merge priority When merging, keep values from the record with, in order: verified email, most recent update, most filled columns. Never merge two people who share only a first name and a domain.