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transaction-classification-debugger

Debug transaction classification using difflib SequenceMatcher fuzzy similarity at an 85% threshold.

Updated Aug 23, 2026
Search Tags:#debugging#python#backend-testing#fuzzy-matching#transaction-classification#difflib
One-click install
npx skills add https://github.com/fedickinson/budget-buddy-2 --skill transaction-classification-debugger
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: transaction-classification-debugger
Source: https://github.com/fedickinson/budget-buddy-2/tree/main/.claude/skills/transaction-classification-debugger
Command: npx skills add https://github.com/fedickinson/budget-buddy-2 --skill transaction-classification-debugger

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Debug and understand Budget Buddy's transaction classification using fuzzy matching at an 85% similarity threshold to support smart batch updates.

Core Features & Use Cases

  • Inspect the get_similar_unclassified_transactions implementation to understand how unclassified transactions are matched by merchant_name or description similarity.
  • Test and visualize fuzzy similarity between transactions to diagnose misclassifications and validate threshold behavior across merchants.
  • Debug the smart batch update workflow by tracing how similar transactions are surfaced and selected for batch classification.

Quick Start

Run a quick sanity check by comparing two sample descriptions with 85% similarity using Python's difflib SequenceMatcher.

Frequently Asked Questions about transaction-classification-debugger

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

FAQPage Schema
How does fuzzy matching work for transaction classification in Python?▼

Fuzzy transaction classification uses Python's difflib SequenceMatcher to compare merchant names or descriptions, calculating a similarity ratio to match unclassified transactions at an 85% threshold.

How do I debug misclassified transactions in my backend service?▼

Debug misclassified transactions by inspecting the get_similar_unclassified_transactions function in backend/services/database_service.py, tracing how fuzzy similarity scores surface and select similar transactions for batch classification.

Why does my 85% similarity threshold fail to match some merchant name variations?▼

The 85% similarity threshold may fail to match merchant variations when Python's difflib SequenceMatcher calculates a ratio below the cutoff, preventing similar unclassified transactions from surfacing in smart batch updates.

Can I test fuzzy similarity between two transaction descriptions in Python?▼

Test fuzzy similarity between two descriptions using Python's difflib SequenceMatcher to calculate the ratio and validate whether it meets the 85% similarity threshold for accurate transaction classification.

What is the best way to validate transaction matching for smart batch updates?▼

Validate transaction matching for smart batch updates by testing fuzzy similarity across merchant name variations and tracing how the get_similar_unclassified_transactions function surfaces similar transactions for classification.

Does Python difflib SequenceMatcher require external dependencies for fuzzy matching?▼

Python difflib SequenceMatcher requires no external dependencies for fuzzy matching, operating as a built-in standard library module to calculate similarity ratios between transaction descriptions and merchant names.

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