| name | maintain |
| version | 1.0.0 |
| description | Brain health checks: back-link enforcement, citation audit, filing validation,
stale info detection, orphan pages, and benchmarks. Use when asked to check
brain health, run maintenance, or audit quality.
|
| triggers | ["brain health","check backlinks","maintenance","orphan pages","stale pages","extract links","build link graph","populate timeline","populate links","backfill graph","extract timeline entries","run dream","process today's session","process yesterday's transcripts","synthesize my conversations","what patterns did you see","did the dream cycle run","consolidate yesterday's conversations"] |
| tools | ["get_health","get_page","put_page","list_pages","get_backlinks","add_link","search"] |
| mutating | true |
Maintain Skill
Periodic brain health checks and cleanup.
Contract
This skill guarantees:
- All health dimensions are checked (stale, orphan, dead links, cross-refs, backlinks, citations, filing, tags)
- Each issue found has a specific fix action
- Back-link iron law is enforced
- Citation format is validated against the standard
- Results are reported with counts per dimension
Phases
Autonomous path (v0.36.4.0) — when you want to reach a target score
If the user asks "get my brain to 90/100" or "fix what's broken", prefer the
one-command loop over walking each dimension by hand:
gbrain doctor --remediation-plan --json
gbrain doctor --remediate --yes --target-score 90 --max-usd 5
--remediation-plan prints a dependency-ordered list (sync before extract,
embed after consolidate, etc.) with per-step est_seconds and est_usd_cost.
--remediate walks the plan, submitting each step as a Minion job, re-checking
score between every step. --max-usd N is a hard cost cap — submission refuses
when the plan would exceed the cap (prevents synthesize loops from burning
Anthropic credits unattended).
When the target score is unreachable for the brain (empty brain with no entity
pages → graph_coverage caps at 70; unconfigured embedding key → caps at 60),
the command bails with a list of what's missing rather than looping.
Use the per-dimension walk below (Phase 2 onward) when:
- The user explicitly asks for a dimension-by-dimension audit
- You're investigating why score is stuck below
--remediate's ceiling
- A specific dimension needs manual judgment that the auto path skips
Manual path
- Run health check. Check gbrain health to get the dashboard.
- 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
Link graph extraction
If link_count is 0 or low relative to page_count, run batch extraction:
gbrain extract links --dir ~/brain
This scans all markdown files for entity references, See Also sections, and
frontmatter fields, then creates typed links in the database.
Timeline extraction
If timeline_entry_count is 0, extract structured timeline from markdown:
gbrain extract timeline --dir ~/brain
Dream cycle (v0.23): synthesize + patterns
gbrain dream runs the full 8-phase maintenance cycle:
lint -> backlinks -> sync -> synthesize -> extract -> patterns -> embed -> orphans
The two new phases consolidate yesterday's conversations into long-term memory:
Synthesize phase: reads transcripts from dream.synthesize.session_corpus_dir,
runs a cheap Haiku verdict (cached in dream_verdicts) to filter routine
ops sessions, then fans out one Sonnet subagent per worth-processing
transcript. Each subagent writes reflections (wiki/personal/reflections/...),
originals (wiki/originals/ideas/...), and people timeline entries. The
orchestrator collects the slugs from subagent_tool_executions (NOT
pages.updated_at — that would pick up unrelated writes) and reverse-renders
each new page from DB → markdown on disk.
Patterns phase: runs after extract (so the graph state is fresh).
Reads recent reflections within dream.patterns.lookback_days (default 30),
runs a single Sonnet pass to surface recurring themes, and writes pattern
pages to wiki/personal/patterns/<theme> when ≥dream.patterns.min_evidence
(default 3) reflections support a pattern.
Quality bar (Iron Law for synthesis):
- Quote the user verbatim. Do not paraphrase memorable phrasings.
- Cross-reference compulsively: every new page MUST have at least one wikilink.
- Slug discipline: lowercase alphanumeric and hyphens only. NO underscores, NO file extensions.
- Edited transcripts produce NEW slugs (content-hash suffix changes) — never silently overwrite.
Trust boundary (allowed_slug_prefixes): the synthesis subagent runs with an
explicit allow-list of write paths sourced from _brain-filing-rules.json's
dream_synthesize_paths.globs. Even on prompt-injection success, the subagent
cannot write outside that list. Trust comes from PROTECTED_JOB_NAMES — MCP
cannot submit subagent jobs at all. Editing the JSON is the only way to add
a new directory the synthesizer can write to.
Idempotency + privacy: transcripts are keyed by (file_path, content_hash),
so re-running on the same content is a no-op. dream.synthesize.exclude_patterns
(default ["medical", "therapy"]) filters out transcripts before any LLM call.
Each entry is auto-wrapped as a word-boundary regex (e.g. medical matches
"medical advice" but NOT "comedical"). Power users may pass full regex.
Cooldown: the cycle's spend cap. dream.synthesize.cooldown_hours (default
12) means at most ~2 synthesize runs per day under autopilot. The completion
timestamp is stored in dream.synthesize.last_completion_ts and is written
ONLY on successful runs (not on skipped/failed). Explicit --input /
--date / --from / --to invocations bypass cooldown.
--dry-run semantics: runs the cheap Haiku significance filter (caches
verdicts) but skips the Sonnet synthesis pass. NOT zero LLM calls.
Configure synthesize on a fresh brain:
gbrain config set dream.synthesize.session_corpus_dir /path/to/transcripts
gbrain config set dream.synthesize.enabled true
gbrain dream --phase synthesize --dry-run --json
gbrain dream
Invocation patterns:
gbrain dream
gbrain dream --phase synthesize
gbrain dream --phase patterns
gbrain dream --input ~/transcripts/2026-04-25.txt
gbrain dream --from 2026-04-01 --to 2026-04-25
gbrain dream --json
Auto-commit deferred to v1.1: v1 writes files to brain_dir but does NOT
git add / commit / push. Either commit yourself or let gbrain autopilot
handle it.
Parses - **YYYY-MM-DD** | Source — Summary and ### YYYY-MM-DD — Title formats.
Note: extracted entries improve structured queries (gbrain timeline), not vector search.
Autopilot check
Verify autopilot is running:
gbrain autopilot --status
If not running, install it:
gbrain autopilot --install --repo ~/brain
Autopilot runs sync, extract, and embed in a continuous loop with adaptive scheduling.
In v0.11.1+, autopilot dispatches each cycle as a single autopilot-cycle
Minion job and supervises the worker child — one install step gives you
sync + extract + embed + backlinks + durable job processing.
Fix a half-migrated install
A v0.11.0 install where the migration skill never fired leaves Minions
partially set up: schema is applied, but ~/.gbrain/preferences.json
doesn't exist, autopilot runs inline, host manifests still reference
agentTurn. Repair:
gbrain apply-migrations --list
gbrain apply-migrations --yes
Full troubleshooting guide: docs/guides/minions-fix.md.
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
Graph population (v0.10.3+)
The links and timeline_entries tables are the structured graph layer.
Populate them periodically or after major imports:
gbrain extract links --source db — backfill structured links by walking pages
from the engine. Reads [Name](people/slug) / [Name](companies/slug) references
and infers relationship types (attended, works_at, invested_in, founded,
advises, mentions, source). Idempotent. Use --source fs --dir <brain>
if you have a markdown checkout to walk instead.gbrain extract timeline --source db — backfill structured timeline entries.
Parses - **YYYY-MM-DD** | summary lines from page content. Idempotent (DB
UNIQUE constraint).gbrain extract all --source db — both in one run.gbrain graph-query <slug> --depth 2 — verify connectivity (use any well-known
entity slug as a probe).gbrain stats — verify link_count > 0 and timeline_entry_count > 0 after extraction.gbrain health — review link_coverage and timeline_coverage percentages
on entity pages (person/company). Below 50% means more extraction is needed.
Available link types (use with gbrain graph-query --type):
attended, works_at, invested_in, founded, advises, mentions, source.
Going forward, every gbrain put call auto-creates and reconciles links via the
auto-link post-hook (default on; disable: gbrain config set auto_link false).
So link-extract is mostly a one-time backfill. timeline-extract should be re-run
after bulk imports or content edits that add new dated entries.
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.
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
Anti-Patterns
- Fixing pages without reading them first -- you must understand context before editing
- Silently skipping dimensions -- every dimension must be checked and reported, even if clean
- Deleting orphan pages without checking if they should be linked instead
- Running embedding refresh during peak usage hours
- Batch-fixing back-links without verifying the relationship is real
- Marking a dimension "clean" without actually querying it
- Rewriting compiled truth without reading the full timeline first
- Removing tags without checking if other pages use the same tag consistently
Output Format
The maintenance report follows this structure:
## Brain Health Report — YYYY-MM-DD
| Dimension | Issues Found | Fixed | Remaining |
|----------------------|-------------|-------|-----------|
| Stale pages | N | N | N |
| Orphan pages | N | N | N |
| Dead links | N | N | N |
| Missing cross-refs | N | N | N |
| Back-link violations | N | N | N |
| Citation gaps | N | N | N |
| Filing violations | N | N | N |
| Tag inconsistencies | N | N | N |
| Embedding staleness | N | N | N |
| Security (RLS) | N | N | N |
| Schema health | N | N | N |
| File storage | N | N | N |
| Open threads | N | N | N |
### Details
[Per-dimension breakdown with specific pages and actions taken]
### Benchmark Results (if run)
[Tier 1-4 query results with pass/fail]
### Outstanding Issues
[Items requiring user attention or confirmation]
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)
