schema-author

name	schema-author
description	Evolve your brain's schema pack. Add page types, propose new ones from corpus scans, backfill page.type on existing pages, audit pack health. Triggers when an agent notices untyped pages, custom domains needing typed entities (researcher, contract, deposition), or wants to see what types the pack declares. Use when this capability is needed.
metadata	{"author":"beyonai"}

schema-author — evolve your schema pack

Non-goals (use these other skills instead)

This skill AUTHORS the schema pack (adds page types, link verbs, prefixes, flags). For these adjacent jobs, route elsewhere:

• Filing one specific page → references/brain-taxonomist/SKILL.md. Brain- taxonomist routes at WRITE TIME ("where does this note go?"). schema-author changes the rules at AUTHORING TIME ("what types and prefixes exist?").
• Schema-check as part of EIIRP iteration → references/eiirp/SKILL.md already has a schema-check phase. Don't duplicate.
• Just looking up a type's settings → gbrain schema explain <type> directly. This skill is for CHANGING the pack, not READING from it.
• Querying who knows about X → references/expert-routing/SKILL.md (or gbrain whoknows directly). schema-author makes a type expert-routable; it does not run the query.

Convention

Convention: see conventions/brain-first.md for the lookup chain (search → query → get_page → external).

Convention: see conventions/schema-evolution.md for "when to add a type vs alias vs prefix" — the heuristic.

When to invoke

Invoke when the user (or a sibling skill) says any of:

• "Add a researcher type to my schema"
• "I have 4000 untyped pages under meetings/"
• "My brain doesn't know that journal-article is a type"
• "Set paper to be extractable"
• "Propose types from what I've ingested"
• "Sync the new types to backfill existing pages"

DON'T invoke for "where does THIS note go" (use brain-taxonomist) or "who knows about X" (use expert-routing / gbrain whoknows).

Tutorial + vision

• Why this matters: docs/what-schemas-unlock.md — 7 killer use cases (4000 invisible meetings made queryable, founder ops brain, research brain, legal brain, team brain, agent-as-co-curator) plus the structural argument for why types matter at query time. Read this before pitching schema authoring to a user — it's the doc that explains the difference between a pile of notes and a brain with structure.
• 5-minute walkthrough: docs/schema-author-tutorial.md — fork the bundled pack, add a researcher type, sync, prove the T1.5 wiring via gbrain whoknows. Use placeholder pages so it runs against any brain without affecting real content.

Workflow

Phase 1 — Brain (know which pack is active)

gbrain schema active --json

Output gives you pack_name, version, sha8, page_types_count, source_tier. If source_tier === "default", the user is on bundled gbrain-base and any mutation will need a fork first (Phase 4).

Phase 2 — Assess (what does the current pack cover?)

gbrain schema stats --json

Returns per-type page counts, untyped count, and dead_prefixes (pack- declared prefixes with zero matching pages — probable mis-declarations). If coverage < 90%, there's untyped content worth typing.

gbrain schema review-orphans --limit 50 --json

Untyped pages drilldown. Look for shared path prefixes (e.g. "12 of these are under research/papers/") — those are candidates for a new type.

Phase 3 — Propose (what types should the pack add?)

gbrain schema detect --json

Clusters pages by source_path and proposes candidate types. Heuristic only (no LLM call).

gbrain schema suggest --json

LLM-refined candidates with confidence scores. Use the top-3 hit rate as the signal for which to promote.

Phase 4 — Apply (mutate the pack)

If the active pack is bundled (gbrain-base or gbrain-recommended), fork it first:

gbrain schema fork gbrain-base mine
gbrain schema use mine

Then add the types one at a time:

gbrain schema add-type researcher \
  --primitive entity \
  --prefix people/researchers/ \
  --extractable \
  --expert

For complex multi-mutation refactors (e.g. add a type AND the link verb that points to it), agents reaching this surface over MCP can use the batched schema_apply_mutations op:

{"op": "add_type", "name": "researcher", "primitive": "entity", "prefix": "people/researchers/", "extractable": true, "expert_routing": true}
{"op": "add_type", "name": "paper", "primitive": "annotation", "prefix": "research/papers/", "extractable": true}
{"op": "add_link_type", "name": "authored", "inference": {"page_type": "researcher", "target_type": "paper"}}

Validate before sync:

gbrain schema lint --with-db

The --with-db flag opts into the 2 DB-aware rules (extractable_empty_corpus, mutation_count_anomaly) that detect mis-declared types you'd otherwise discover only at runtime.

Phase 5 — Sync (backfill existing pages with the new types)

Dry-run first:

gbrain schema sync --json

Returns per-prefix would_apply counts + sample slugs. If the numbers look right:

gbrain schema sync --apply

Chunked UPDATE in 1000-row batches; never wedges concurrent writers. Idempotent on re-run (second --apply finds nothing to backfill).

Phase 6 — Verify

gbrain schema stats --json

Coverage should be ≥95% now. Spot-check the new type:

gbrain whoknows "machine learning"

If researcher was declared --expert, results should include researcher-typed pages. (The pack-aware wiring at the query path was added in v0.40.6.0 — pre-v0.40.6 brains silently ignored custom expert-routed types.)

Phase 7 — Commit (preserve the change)

If the pack is in source control, commit:

cd ~/.gbrain/schema-packs/mine
git add pack.json
git commit -m "schema: add researcher + paper types + authored link"
git push

If the brain daemon is running (gbrain serve --http), other processes pick up the change within 1 second (stat-mtime TTL gate in loadActivePack — v0.40.6.0 closed the cross-process invalidation gap).

Outputs

• Mutated pack file at ~/.gbrain/schema-packs/<name>/pack.{json,yaml}.
• Audit row in ~/.gbrain/audit/schema-mutations-YYYY-Www.jsonl per mutation.
• pages.type backfilled on matching rows after sync --apply.
• Query paths (whoknows, find_experts) now route through the new expert types.

Contract

• Inputs: a natural-language request that names a type / prefix / link verb / flag change, OR the result of gbrain schema review-orphans showing untyped pages that need a new type.
• Outputs: mutated pack file at ~/.gbrain/schema-packs/<name>/pack.{json,yaml} + an audit row in ~/.gbrain/audit/schema-mutations-YYYY-Www.jsonl + (if sync --apply ran) backfilled pages.type on matching rows.
• Side effects: invalidates the in-process pack cache + the query cache for the source. Other processes pick up the change within 1 second (stat-mtime TTL).
• Idempotency: every primitive is idempotent. add-alias/add-prefix no-op on duplicate; sync --apply finds nothing to update on second run.
• Trust: CLI = local trust (no scope check). MCP = OAuth admin scope (write ops). Audit log captures actor: mcp:<clientId8> per mutation.
• Atomicity: every mutation is wrapped in withMutation's atomic write (.tmp + fsync + rename) + per-pack O_CREAT|O_EXCL lock. Crash mid-write leaves the original file untouched.

Anti-Patterns

• Don't mutate gbrain-base or gbrain-recommended. Fork first (gbrain schema fork gbrain-base mine). These are bundled packs; edits would be lost on upgrade. The mutation primitives refuse with PACK_READONLY.
• Don't add a type for a directory you imported once for triage. Pack types are permanent decisions; one-time imports are not. See references/conventions/schema-evolution.md for the <20-pages-don't-pack-codify heuristic.
• Don't add --expert to a type with no path_prefixes. The expert_routing_without_prefix lint warns about this — expert-routed types with no prefix never match a put_page inference, so whoknows silently never surfaces them.
• Don't promote a schema suggest candidate without verifying the prefix matches real content. Run lint --with-db before add-type to catch prefix collisions pre-write.
• Don't conflate "filing one page" with "evolving the schema." Filing routes via brain-taxonomist; schema-author is for authoring the type taxonomy itself. The Non-goals section above names the boundary.
• Don't skip the dry-run before sync --apply. Always run sync first to see would_apply counts + sample slugs. A pack prefix that matches 50,000 pages is recoverable but slow; verifying first is cheap.
• Don't remove a type without checking references. remove-type refuses with STILL_REFERENCED if another type's aliases / enrichable_types / link_types / frontmatter_links references it. Break the references first; don't add --force.

Output Format

When invoked, this skill produces structured output suitable for both human + JSON consumption:

Per-mutation result (JSON):

{"schema_version": 1, "pack": "mine", "path": "/Users/.../pack.json", "format": "json", "prev_sha8": "a1b2c3d4", "new_sha8": "e5f6g7h8"}

Per-batch result (from schema_apply_mutations MCP op):

{"schema_version": 1, "pack": "mine", "batch_id": "batch-1716491400-abc123", "mutations_applied": 3, "results": [{...}, {...}, {...}]}

Stats JSON (per-source + aggregate + dead-prefix hints):

{"schema_version": 1, "pack_identity": "mine@1.0.0+abc12345", "aggregate": {"total_pages": 4823, "typed_pages": 4710, "untyped_pages": 113, "coverage": 0.9766, "by_type": [{"type": "person", "count": 2104}, ...]}, "per_source": [...], "dead_prefixes": [{"type": "researcher", "prefix": "people/researchers/"

Sync dry-run JSON:

{"schema_version": 1, "apply": false, "pack_identity": "mine@1.0.0+abc12345", "per_prefix": [{"type": "meeting", "prefix": "meetings/", "would_apply": 4000, "sample_slugs": ["meetings/2026-01-01-foo", ...], "dead_prefix": false, "applied": 0}], "total_would_apply": 4000, "total_applied": 0}

Human output (the agent's final summary):

• One line per mutation: Pack: <name> (<format>) and Sha8: <prev> → <new>
• Stats: total pages, typed %, untyped count, per-type breakdown, dead-prefix list
• Sync: per-prefix would_apply/applied count + sample slugs in dry-run mode

On failure, the error envelope follows the standard StructuredAgentError shape from src/core/errors.ts: {error, code, message, details?}. Codes from the mutation primitives: PACK_NOT_FOUND, PACK_READONLY, PACK_CORRUPT, TYPE_EXISTS, TYPE_NOT_FOUND, INVALID_PRIMITIVE, INVALID_RESULT, IO_ERROR, STILL_REFERENCED, LOCK_BUSY.

Failure modes

• PACK_READONLY → you tried to mutate gbrain-base or gbrain-recommended. Fork first.
• INVALID_RESULT → the mutation would create a dangling reference or prefix collision. The pre-write lint gate caught it. Read the error message; the lint rule name names the problem.
• STILL_REFERENCED → you tried to remove a type that another type's aliases / enrichable_types / link_types / frontmatter_links references. The error names every reference. Remove those first.
• LOCK_BUSY → another process is mid-mutation. Wait 30s and retry, or pass --force if you know the holder is wedged.
• permission_denied (MCP only) → your OAuth client doesn't have admin scope. Re-register with gbrain auth register-client --scopes admin.

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Source: beyonai/ByClaw — distributed by TomeVault.