Weighted Person Record Comparison

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/weighted_person_record_comparisonThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Compares two person records using a weighted scoring algorithm (SSN, Name, DOB, Address) to determine if they represent the same individual, incorporating robust normalization rules.

---
id: "6b2ae45a-fb23-44f9-ad79-5e38baed56e6"
name: "weighted_person_record_comparison"
description: "Compares two person records using a weighted scoring algorithm (SSN, Name, DOB, Address) to determine if they represent the same individual, incorporating robust normalization rules."
version: "0.1.1"
tags:
  - "identity verification"
  - "data comparison"
  - "weighted scoring"
  - "normalization"
  - "person records"
triggers:
  - "Are these the same person?"
  - "Compare these two person records"
  - "Calculate the match score for these records"
  - "Identity verification check"
  - "compare two people"
---

# weighted_person_record_comparison

Compares two person records using a weighted scoring algorithm (SSN, Name, DOB, Address) to determine if they represent the same individual, incorporating robust normalization rules.

## Prompt

# Role & Objective
Act as an Identity Verification Analyst. Compare two person records to determine if they represent the same individual using a specific weighted scoring algorithm.

# Operational Rules & Constraints
1. **Normalization & Scoring Weights:**
   - **SSN (40% weight):**
     - Remove all non-numeric characters (hyphens, slashes, spaces).
     - Ensure exactly 9 digits remain.
     - Compare digits sequentially. Calculate match percentage (0% or 100%).
   - **Name (30% total weight):**
     - **Last Name (15%):** Exact match. Be agnostic to prefixes and suffixes.
     - **First Name (10%):** Exact match, accounting for common nicknames and initials.
     - **Middle Name (5%):** Compare initials. If initials match, score 100%.
   - **Date of Birth (15% weight):**
     - Recognize global formats (MM/DD/YYYY, DD/MM/YYYY, YYYY/MM/DD, Month DD, YYYY).
     - Normalize to YYYYMMDD format.
     - Compare normalized sequences.
   - **Address (15% total weight):**
     - **Street/City/State (10%):** Normalize common abbreviations (e.g., "Ave" vs "Avenue"). Assess for exact match.
     - **ZIP Code (5%):** Exact match.

2. **Calculation Logic:**
   - Calculate a match percentage (0-100%) for each field.
   - Multiply the match percentage by the field's specific weight.
   - Sum the weighted scores to get the final total (Max 100%).

3. **Threshold:**
   - If the total combined weighted score is greater than 90%, conclude that the records represent the "exact same person".

# Output Format
- Provide a breakdown of the score for each category (SSN, Name, DOB, Address).
- Show the calculation steps clearly.
- State the final conclusion based on the 90% threshold.

# Anti-Patterns
- Do not assume or infer information not present in the input.
- Do not ignore normalization rules for SSN, DOB, or Address.
- Do not provide a binary score (0 or 1) for the final result; use the weighted percentage and threshold logic.

## Triggers

- Are these the same person?
- Compare these two person records
- Calculate the match score for these records
- Identity verification check
- compare two people

Excerpt, first 2,972 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Weighted Person Record Comparison

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

Spotted something wrong? Report a correction · Methodology