slack-memory-cleanup

To deduplicate memory entries in Slack-like data stores, the Skill detects duplicates by matching email, user_id, or channel_id and consolidates them into a single profile or task, updating index references.

To fix misclassified files in AI memory stores, the Skill parses YAML frontmatter to flag entries placed in wrong folders and moves them to their proper locations such as users, channels, tasks, or decisions.

Yes, you can run a dry-run on your memory store to inspect potential deduplication and rehousing changes before executing the actual cleanup flow, ensuring safe data organization.

The best way to prune stale Slack memory is to identify and remove duplicate or misclassified entries while retaining relevant historical context, ensuring ongoing projects maintain accurate memory quality.

Yes, memory cleanup works by parsing YAML frontmatter to detect duplicates and misclassifications across users, channels, projects, tasks, and decisions, then moves files to proper folders while updating index references.

Misclassified data accumulates in Slack memory stores when entries are incorrectly filed across users, channels, tasks, and decisions, requiring automated flagging and rehousing to maintain proper memory organization.