A text similarity score can look perfectly reasonable even when it's wrong.

Suppose a script ranks a set of candidate records and writes the results to JSONL. Another step reads that file, transforms it, or passes it downstream. How do we know the saved scores and rankings still agree with the original inputs?

That's the problem I explored with JSONL Candidate Align , a small open-source Python CLI.

GitHub: https://github.com/xmmyyx/jsonl-candidate-align

Two commands, two responsibilities

The tool separates candidate generation from output validation.

jsonl-align align reads already-structured query and candidate records. It uses character trigrams and numeric overlap to calculate lexical scores, applies type compatibility and admission rules, and writes up to eight candidates per query.

jsonl-align validate reads the original inputs and a saved candidate result.…