fuzzy-matching

Reconcile inventory items using PostgreSQL trigram pre-filtering and multi-factor similarity scoring.

Updated Apr 14, 2026

Search Tags:#pipeline #inventory #similarity #postgres #fuzzy-matching #entity-resolution #data-reconciliation

Ready to run

npx skills add https://github.com/princegarg001/digital-Lige-identifier --skill fuzzy-matching-princegarg001

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fuzzy-matching
Source: https://github.com/princegarg001/digital-Lige-identifier/tree/main/.agents/skills/fuzzy-matching
Command: npx skills add https://github.com/princegarg001/digital-Lige-identifier --skill fuzzy-matching-princegarg001

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reconcile and deduplicate inventory items and product records using a robust multi-stage fuzzy matching pipeline.

Core Features & Use Cases

Three-stage pipeline: PostgreSQL trigram pre-filter, salient overlap check, and multi-factor similarity scoring for accurate item reconciliation.
Python and SQL-based implementation with a normalization layer and scalable scoring routine.
Use cases include matching vendor SKUs across catalogs, merging duplicate product entries, and reconciling records from multiple data sources.

Quick Start

Run the FuzzyItemMatcher against a target item name to retrieve the top matching inventory items.

Frequently Asked Questions about fuzzy-matching

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reconcile similar inventory items across datasets with fuzzy matching?▼

Fuzzy matching reconciles similar inventory items by using a three-stage pipeline: PostgreSQL trigram pre-filtering, salient overlap checks, and multi-factor similarity scoring to identify and match vendor SKUs and product records.

Do I need PostgreSQL to run the fuzzy matching pipeline?▼

Yes, the fuzzy matching pipeline requires a PostgreSQL database with the pg_trgm extension installed and a normalized_name column to execute trigram pre-filtering and multi-factor similarity scoring successfully.

What is the best way to match vendor SKUs across multiple catalogs?▼

The best way to match vendor SKUs is using a Python and SQL-based normalization layer paired with scalable scoring routines to process product names and reconcile records from multiple data sources.

How does the multi-factor similarity scoring work for entity resolution?▼

Multi-factor similarity scoring works by applying a Python-based normalization and scoring pipeline that evaluates salient overlap after an initial PostgreSQL trigram pre-filter identifies potential inventory matches.

Can I use this fuzzy matching approach to deduplicate product entries?▼

Yes, you can use this fuzzy matching pipeline to deduplicate product entries by running the FuzzyItemMatcher against a target item name to retrieve the top matching inventory items for reconciliation.

What are the limitations of using trigram pre-filtering for data reconciliation?▼

Trigram pre-filtering for data reconciliation requires a normalized_name column and the pg_trgm extension, meaning it is limited to PostgreSQL environments configured for text similarity indexing and scalable scoring routines.