gbrain/skills/maintain/SKILL.md

Maintain Skill

Periodic brain health checks and cleanup.

Workflow

1. Run health check. Check gbrain health to get the dashboard.
2. Check each dimension:

Stale pages

Pages where compiled_truth is older than the latest timeline entry. The assessment hasn't been updated to reflect recent evidence.

• Check the health output for stale page count
• For each stale page: read the page from gbrain, review timeline, determine if compiled_truth needs rewriting

Orphan pages

Pages with zero inbound links. Nobody references them.

• Review orphans: are they genuinely isolated or just missing links?
• Add links in gbrain from related pages or flag for deletion

Dead links

Links pointing to pages that don't exist.

• Remove dead links in gbrain

Missing cross-references

Pages that mention entity names but don't have formal links.

• Read compiled_truth from gbrain, extract entity mentions, create links in gbrain

Back-link enforcement

Check that the back-linking iron law is being followed:

• For each recently updated page, check if entities mentioned in it have corresponding back-links FROM those entity pages
• A mention without a back-link is a broken brain
• Fix: add the missing back-link to the entity's Timeline or See Also section
• Format: - **YYYY-MM-DD** | Referenced in [page title](path) -- brief context

Filing rule violations

Check for common misfiling patterns (see skills/_brain-filing-rules.md):

• Content with clear primary subjects filed in sources/ instead of the appropriate directory (people/, companies/, concepts/, etc.)
• Use gbrain search to find pages in sources/ that reference specific people, companies, or concepts -- these may be misfiled
• Flag misfiled pages for review or re-filing

Citation audit

Spot-check pages for missing [Source: ...] citations:

• Read 5-10 recently updated pages
• Check that compiled truth (above the line) has inline citations
• Check that timeline entries have source attribution
• Flag pages where facts appear without provenance

Tag consistency

Inconsistent tagging (e.g., "vc" vs "venture-capital", "ai" vs "artificial-intelligence").

• Standardize to the most common variant using gbrain tag operations

Embedding freshness

Chunks without embeddings, or chunks embedded with an old model.

• For large embedding refreshes (>1000 chunks), use nohup: nohup gbrain embed refresh > /tmp/gbrain-embed.log 2>&1 &
• Then check progress: tail -1 /tmp/gbrain-embed.log

Security (RLS verification)

Run gbrain doctor --json and check the RLS status. All tables should show RLS enabled. If not, run gbrain init again.

Schema health

Check that the schema version is up to date. gbrain doctor --json reports the current version vs expected. If behind, gbrain init runs migrations automatically.

File storage health

Check the integrity of stored files and redirect pointers:

• Run gbrain files verify to check all DB records have valid data
• Run gbrain files status to see migration state (local, mirrored, redirected)
• Check for orphan .redirect.yaml pointers that reference missing storage files
• Check for large binary files (>= 100 MB) still in git that should be in cloud storage
• If storage backend is configured: verify redirect pointers resolve (download test)

Open threads

Timeline items older than 30 days with unresolved action items.

• Flag for review

Benchmark Testing

Periodically verify search quality hasn't regressed. Run a battery of test queries across difficulty tiers:

• Tier 1 (entity lookup): known names -- should always resolve
• Tier 2 (topic recall): concepts, topics -- keyword search should handle
• Tier 3 (semantic): queries with no exact keyword match -- needs embeddings
• Tier 4 (cross-domain): relational/connection queries -- only semantic handles

Compare results from gbrain search (keyword) vs gbrain query (hybrid). Quality matters more than speed (2.5s right > 200ms wrong).

When to run benchmarks:

• After major brain imports or re-imports
• After gbrain version upgrades
• After embedding regeneration
• Monthly to track quality drift

Heartbeat Integration

For production agents running on a schedule, integrate gbrain health checks into your operational heartbeat.

On every heartbeat (hourly or per-session)

Run gbrain doctor --json and check for degradation. Report any failing checks to the user. Key signals: connection health, schema version, RLS status, embedding staleness.

Weekly maintenance

Run gbrain embed --stale to refresh embeddings for pages that have changed since their last embedding. For large brains (>5000 pages), run this with nohup:

nohup gbrain embed --stale > /tmp/gbrain-embed.log 2>&1 &

Daily verification

Verify sync is running: check gbrain stats and confirm last_sync is within the last 24 hours. If sync has stopped, the brain is drifting from the repo.

Stale compiled truth detection

Flag pages where compiled truth is >30 days old but the timeline has recent entries. This means new evidence exists that hasn't been synthesized. These pages need a compiled truth rewrite (see the maintain workflow above).

Report Storage

After maintenance runs, save a report:

• Health check results (before/after scores for each dimension)
• Back-link violations found and fixed
• Filing rule violations found
• Citation gaps flagged
• Benchmark results (if run)
• Outstanding issues requiring user attention

This creates an audit trail for brain health over time.

Quality Rules

• Never delete pages without confirmation
• Log all changes via timeline entries
• Check gbrain health before and after to show improvement

Tools Used

• Check gbrain health (get_health)
• List pages in gbrain with filters (list_pages)
• Read a page from gbrain (get_page)
• Check backlinks in gbrain (get_backlinks)
• Link entities in gbrain (add_link)
• Remove links in gbrain (remove_link)
• Tag a page in gbrain (add_tag)
• Remove a tag in gbrain (remove_tag)
• View timeline in gbrain (get_timeline)