24 of the 30 duplicate groups were not duplicates
We grouped an asset database by title and found 30 groups of "duplicates." Content hashing dissolved 24 of them. Five were real. One was unjudgeable — because the rows had no content at all. This is a field note on deduplication judgment, from auditing a store of AI-agent assets (conversations, skills, memories, instruction files) collected across many platforms. The numbers are from a live audit…
The analysis of an asset database discovered that out of 30 groups of duplicate items, 24 of them were not actual duplicates. The database was composed of content such as conversations, skills, memories, and instruction files collected across various platforms as of 2026-08-19. The study focused on grouping assets by title, but found that this method was not sufficient to determine duplication.
Instead, four factors were considered: exact content match, similarity of the stripped body, provenance, and the creation-time gap. Of the 30 groups, five were real duplicates, six were different assets with the same title, seven were verification probes created for testing purposes, 12 rows had no content, and three pairs had collisions in the provenance field.
The study concluded that out of the 30 groups, 80% were not duplicates, highlighting the importance of considering multiple factors beyond just title matching when evaluating asset duplication.
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