garrytan/maintain

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              # preview what would run
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

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

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):

1. Quote the user verbatim. Do not paraphrase memorable phrasings.
2. Cross-reference compulsively: every new page MUST have at least one wikilink.
3. Slug discipline: lowercase alphanumeric and hyphens only. NO underscores, NO file extensions.
4. 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   # preview
gbrain dream                                       # full 8-phase cycle

Invocation patterns:

gbrain dream                                          # full cycle
gbrain dream --phase synthesize                       # just synthesize
gbrain dream --phase patterns                         # just patterns
gbrain dream --input ~/transcripts/2026-04-25.txt     # ad-hoc one transcript
gbrain dream --from 2026-04-01 --to 2026-04-25        # backfill range
gbrain dream --json                                   # CycleReport 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:

# Check migration status
gbrain apply-migrations --list

# Apply pending migrations (idempotent; safe on healthy installs)
gbrain apply-migrations --yes

# If host-specific handlers are flagged in ~/.gbrain/migrations/pending-host-work.jsonl:
# walk them per skills/migrations/v0.11.0.md + docs/guides/plugin-handlers.md,
# ship handler registrations in the host repo, then re-run apply-migrations.

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.

• 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

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)