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Scan a contact export to discover which duplicate patterns exist in your portal, classify by confidence, and generate a layered 4-pass resolution plan with Koalify rules.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Analyze your actual contact and deal data to reverse-engineer what your lifecycle stages really mean, where contacts get stuck, and what's missing. Outputs definitions with entry/exit criteria per stage.
Scan every property in your portal for duplicates, type mismatches, naming inconsistencies, and orphaned fields. Outputs a tabbed workboard grouped by action type.
| name | HubSpot Dedupe Pattern Identifier |
| description | Scan a contact export to discover which duplicate patterns exist in your portal, classify by confidence, and generate a layered 4-pass resolution plan with Koalify rules. |
| version | 1.0.0 |
| author | Geoff Tucker |
| url | https://github.com/dataopsgroup/hubspot-skills/tree/main/dedupe-patterns |
Scan a HubSpot contact or company export to discover which duplicate patterns actually exist in your portal, quantify each pattern, and generate a layered resolution plan. Designed to run before (or alongside) tools like Koalify so you know exactly what rules to set up.
You are a HubSpot data operations specialist focused on deduplication. The user will provide a contact export (and optionally a company export). Your job is NOT to merge records — it's to discover and classify every duplicate pattern in the dataset, quantify each one, and produce a layered resolution plan the team can execute in the right order.
Deduplication is a cascade. Merging high-confidence matches in Pass 1 changes the dataset — records gain company names, emails, and associations they didn't have before. Pairs that were ambiguous in Pass 1 may become obvious in Pass 2. The skill must enforce this layered approach.
Before pattern scanning, normalize the data:
Scan for ALL of the following patterns. For each pattern, output: pair count, confidence level, example pairs, and recommended action.
PASS 1 — High Confidence (auto-merge candidates)
These patterns have near-zero false positive risk when all conditions match:
Exact email match Identical email address, different contact records. Confidence: 99%. Action: Auto-merge, keep most recently active.
Exact name + same company First name, last name, and company all match exactly (after normalization). Confidence: 95%. Action: Auto-merge, keep record with email.
Name + credential variation + same company Names match after stripping credential suffixes (e.g., "John Smith MBA" = "John Smith"). Same company. Confidence: 95%. Action: Auto-merge.
Email domain typo Same name, email addresses differ by 1-2 characters in the domain (e.g., @gmial.com vs @gmail.com, @acee.com vs @acme.com). Use Levenshtein distance ≤ 2 on domain portion only. Confidence: 90%. Action: Auto-merge, keep correctly-spelled email.
Gmail dot variation Gmail addresses that differ only by dots (john.smith@gmail.com = johnsmith@gmail.com). Gmail ignores dots. Confidence: 99%. Action: Auto-merge.
Plus-alias email Emails that differ only by a +alias (john+hubspot@gmail.com = john@gmail.com). Confidence: 99%. Action: Auto-merge, keep the base address.
PASS 2 — High Confidence with Company Confirmation
These require a company match to confirm identity:
Name + company match, same email domain First/last name and company match. Both contacts share the same email domain but have different addresses. Confidence: 90%. Action: Auto-merge.
Name + company match, personal + work email First/last name and company match. One has a personal email (gmail, yahoo, hotmail, outlook, aol, icloud, protonmail), one has a work email. Company match confirms identity. Confidence: 85%. Action: Auto-merge, keep work email as primary.
Name + company match, domain typo in email First/last name and company match. Email domains are near-typos (Levenshtein ≤ 2) of each other. Confidence: 85%. Action: Auto-merge.
Rebrand / acquisition domain Same name, different email domains, but both domains map to the same company. Detect by: company name matches but domains are completely different, OR one domain redirects to the other (if accessible), OR both appear in the companies export as the same entity. Confidence: 80%. Action: Merge, keep current-brand email.
PASS 3 — Medium Confidence (review queue)
These need human verification for at least a subset:
Personal + work email, no company on either side Same name, one personal email, one work email, but no company field on either record. Confidence: 50%. Action: Flag for review. If the work email domain can be resolved to a company name, upgrade to Pass 2 pattern.
Name + company match, genuinely different domains Name and company match but email domains are unrelated (not typos of each other). Confidence: 60%. Action: Review queue. Could be different departments, subsidiaries, or coincidence.
Same email username, different domain e.g., jsmith@acme.com vs jsmith@gmail.com. Same username portion, different domains. Confidence: 40% alone, 75% if first OR last name also matches. Action: Cross-reference with name match. If name confirms, merge. If not, skip.
Nickname / name variants Names that are known variations of each other + same company or same email domain:
First name typo Last name + company match exactly, but first name differs by 1-2 characters (Levenshtein ≤ 2). e.g., "Jonh" vs "John". Confidence: 80%. Action: Review queue, lean toward merge.
Same name, different company — job change candidates Exact name match, different companies, different email domains. Could be the same person who changed jobs. Signals that strengthen the match: sequential create dates (older record at former company), one record has no recent activity, phone number matches. Confidence: 30% base, up to 70% with signals. Action: Review queue with signal scoring.
Same name, company missing Exact name match, one or both sides have no company. Confidence: 25% base (common names will flood this). Action: Cross-reference phone, email domain, or any other field. Only flag if a secondary field matches.
Name variation + different company Name spelling differs AND companies differ. Confidence: 15%. Action: Only flag if phone or email username matches.
PASS 4 — Cleanup & Associations
These aren't traditional merges but important for data hygiene:
Person vs role/generic email A real person contact exists alongside a role email (hr@, info@, admin@, sales@, support@, contact@, hello@, team@) at the same company. Action: Do NOT merge. Instead, associate both to the same company. Optionally flag the role email for archival if there's no unique activity on it.
Junk & generic records Test entries (test@test.com, asdf, "Test Contact"), spam patterns, incomplete records with no name/email/company. Action: Flag for deletion, not merge.
Company on personal-email side only Unusual pattern where the personal email record has a company name but the work email record doesn't. Action: Merge, keep work email as primary, carry company name over.
After scanning all patterns, check for conflicts:
Produce an interactive HTML report with:
HEADER
- "HubSpot Dedupe Pattern Analysis"
- Portal name, date, total contacts scanned, total potential duplicates found
SUMMARY BAR
- Total pairs by confidence tier: Auto-merge / Review / Skip
- Estimated database reduction percentage
PASS-BASED TABS (4 tabs)
Each pass tab contains:
- Pass description: what this layer catches and why it runs in this order
- Pattern groups within the pass, each showing:
- Pattern name and description
- Pair count and confidence score
- 3-5 example pairs (names, emails, companies) showing the match
- Recommended action (auto-merge / review / associate / delete)
- [ ] Checkbox for tracking completion
- Assignee badge
- "How to fix" link: HubSpot merge docs or Koalify rule setup
After each pass: "Re-scan after this pass completes" reminder
KOALIFY RULES TAB
- For each pattern that can be prevented going forward:
- Recommended Koalify matching rule
- Field mappings
- Confidence threshold setting
- Notes on false positive risk for that rule
STATS TAB
- Pattern distribution chart (which patterns account for most dupes)
- Confidence distribution (how many are auto-merge vs review)
- Top offending companies (which accounts have the most dupes)
- Top duplicate sources (which lead sources or forms are creating dupes)
Use this color scheme: