Migrate Skill

Contract

• Source data is never modified or deleted; migration is additive only.
• Every migrated page is verified round-trip: written to gbrain, read back, spot-checked.
• Cross-references from the source system (wikilinks, block refs, tags) are converted to gbrain equivalents.
• Migration is tested on a sample (5-10 files) before bulk execution.
• Post-migration health check confirms page count, link integrity, and embedding coverage.

Supported Sources

[[wikilinks]]

((block refs))

Phases

1. Assess the source. What format? How many files? What structure?
2. Plan the mapping. How do source fields map to gbrain fields (type, title, tags, compiled_truth, timeline)?
3. Test with a sample. Import 5-10 files, verify by reading them back from gbrain and exporting.
4. Bulk import. Import the full directory into gbrain.
5. Verify. Check gbrain health and statistics, spot-check pages.
6. Build links. Extract cross-references from content and create typed links in gbrain.

Obsidian Migration

1. Import the vault directory into gbrain (Obsidian vaults are markdown directories)
2. Wire the graph with native wikilink support (v0.12.1+):
   bash

   gbrain extract links --source db --dry-run | head -20    # preview
   gbrain extract links --source db                         # commit

   extract links

   natively parses

   [[relative/path]]

   and

   [[relative/path|Display Text]]

   alongside standard

   [text](page.md)

   markdown syntax. Ancestor-search resolution handles wiki KBs where authors omit one or more leading

   ../

   prefixes. The

   .md

   suffix is inferred automatically for wikilinks.

• Tags (

  #tag

  ) become gbrain tags
• Frontmatter properties map to gbrain frontmatter
• Attachments (images, PDFs) are noted but handled separately via file storage

Notion Migration

1. Export from Notion: Settings > Export > Markdown & CSV
2. Notion exports nested directories with UUIDs in filenames
3. Strip UUIDs from filenames for clean slugs
4. Map Notion's database properties to frontmatter
5. Import the cleaned directory into gbrain

CSV Migration

1. For each row in the CSV, create a page with column values as frontmatter
2. Use a designated column as the slug (e.g., name)
3. Use another column as compiled_truth (e.g., notes)
4. Store each page in gbrain

Verification

1. Check gbrain statistics to verify page count matches source
2. Check gbrain health for orphans and missing embeddings
3. Export pages from gbrain for round-trip verification
4. Spot-check 5-10 pages by reading them from gbrain
5. Test search: search gbrain for "someone you know is in the data"

Anti-Patterns

• Bulk import without sample test. Never import the full dataset before verifying with 5-10 files. The cost of cleaning up hundreds of bad pages is enormous.
• Destroying source data. Migration is additive. Never modify, move, or delete the source files.
• Ignoring cross-references. Wikilinks, block refs, and tags from the source system must be converted to gbrain equivalents. Dropping them loses the knowledge graph.
• Skipping verification. A migration without post-import health check, page count comparison, and spot-check reads is incomplete.

Output Format

MIGRATION REPORT -- [source] -> GBrain
=======================================

Source: [format] ([file count] files, [size])
Mapping: [field mapping summary]

Sample Test (N files):
- Imported: N/N
- Round-trip verified: N/N
- Cross-refs converted: N

Bulk Import:
- Total imported: N
- Skipped (duplicates/errors): N
- Links created: N
- Tags migrated: N

Verification:
- Page count match: [yes/no]
- Health check: [pass/fail]
- Search test: [query] -> [result count] hits

Tools Used

• Store/update pages in gbrain (put_page)
• Read pages from gbrain (get_page)
• Link entities in gbrain (add_link)
• Tag pages in gbrain (add_tag)
• Get gbrain statistics (get_stats)
• Check gbrain health (get_health)
• Search gbrain (query)