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CRM duplicate record review

Finds and explains likely duplicate leads, contacts, accounts, and opportunities across CRM exports without merging or deleting records. Use for CRM deduplication, duplicate review, data hygiene, account matching, or pre-migration record cleanup.

by adlass TemplatesVersion 1Uses adlass toolsUniversal

Published Aug 21, 2026 · Updated Aug 26, 2026

Helpful · 0View raw SKILL.md

Requirements

Map record types and identity fields. Define match thresholds, aliases and disposition statuses.

Skill document

The full SKILL.md your agent reads and follows.

CRM duplicate record review

Purpose

Identify probable duplicate CRM records and provide a safe, evidence-based disposition for review. Preserve the source records and show exactly why two records were considered related.

Scope

Accounts, contacts, leads, opportunities, identity fields, ownership, activity, stage, timestamps, and conflicting values.

Excluded: merging, deleting, overwriting CRM records, or resolving ambiguous identities by guesswork.

Data basis

  • CRM exports or record tables in scope.
  • Company matching rules and field mapping.
  • Optional reference table for known account aliases.

Result

A duplicate-candidate sheet and a concise disposition report.

Quality criteria

  • Each candidate pair or group lists matching fields and confidence.
  • Exact matches are separated from fuzzy candidates.
  • Conflicting owners, stages, or activities are visible.
  • Every input record receives a reviewed status.
  • No source value is lost.

Instructions

Apply exact identifiers first, then conservative combinations of normalized name, domain, email, phone, address, and account identifiers. Never use a shared company domain alone to merge people. Treat different opportunities at one account as separate unless the opportunity identity matches. Keep threshold values in the mapping rules.

Adapt before use

Keep a clear audit trail throughout the run. Use the source wording and field values that support each material conclusion, and retain uncertainty when the evidence does not decide the issue. Separate a missing record from a negative result and a contradictory record from an exception. Prefer a short, prioritised result over unsupported completeness. Check that counts, identifiers, dates, and labels agree across the written result and the structured output. When a rule, source, or mapping is unavailable, name the limitation and explain how it affects interpretation. Do not silently infer ownership, approval, timing, or business intent.

  • Map record types and identity fields.
  • Define exact and fuzzy match thresholds.
  • Add known aliases and the review-status vocabulary.

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