Why are some of my files skipped during the synchronization process?

Files are typically skipped if they contain sensitive-looking content that triggers redaction rules, if they are excluded in your current config, or if they don't match the markdown-first criteria.

What is the difference between memory and knowledge ingestion modes?

The mode is determined by your ingestionMode configuration; memory is generally used for ephemeral or task-specific context, while knowledge is for long-term project documentation.

How can I see exactly what happened during a sync?

The skill provides a JSON report detailing the number of files scanned, synced, and skipped, as well as specific error messages for any failures.

When should I use sync-workspace instead of waiting for automatic sync?

You should use it when you have just finished significant updates to your documentation or codebase and want Claude to be aware of those changes immediately for the next prompt.

HydraDB Workspace Sync

Name: HydraDB Workspace Sync
Author: usecortex

byusecortex

0•

Database Management

Synchronizes local markdown-first workspace context into HydraDB for immediate AI memory and knowledge retrieval.

The sync-workspace skill allows developers to manually trigger a comprehensive synchronization of their local project context into HydraDB, bypassing automatic sync intervals for an immediate refresh. It intelligently scans workspace files, applies sensitive data redaction, and categorizes content into memory or knowledge bases depending on your specific ingestion configuration. This skill is essential for ensuring Claude has the most current understanding of your codebase and documentation, providing granular reports on file counts, skip reasons, and sync errors.

Key Features

01Configurable memory vs. knowledge ingestion modes

02Manual workspace context synchronization

03Detailed sync reporting and error logging

04Sensitive data redaction and security filtering

05Support for markdown-first project structures

060 GitHub stars

Use Cases

01Updating AI context immediately after major documentation or architecture changes

02Auditing which files are being indexed for AI recall and troubleshooting skips

03Forcing a clean state sync using the optional force argument

Key Features

01Configurable memory vs. knowledge ingestion modes

02Manual workspace context synchronization

03Detailed sync reporting and error logging

04Sensitive data redaction and security filtering

05Support for markdown-first project structures

060 GitHub stars

Use Cases

01Updating AI context immediately after major documentation or architecture changes

02Auditing which files are being indexed for AI recall and troubleshooting skips

03Forcing a clean state sync using the optional force argument