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Tutorial: Build your first schema pack

You'll fork the bundled gbrain-base pack, add a custom researcher page type, import a handful of placeholder researcher pages, backfill their page.type column with one command, then prove the wiring works by running gbrain whoknows and seeing your new type surface in results. End state: a forked-and-active pack on disk, ~5 pages typed as researcher, and a query that proves the pack-aware routing fires end-to-end.

Want the WHY before the HOW? Read what-schemas-unlock.md first — 7 concrete use cases (4000 invisible meetings, the founder ops brain, the research brain, the legal brain, the team brain, agent-as-co-curator) plus the structural argument for why types matter at query time. Then come back here for the 5-minute walkthrough.

The whole walkthrough takes about 5 minutes. You'll see something working by step 3.

What you'll need

  • gbrain v0.40.7.0 or later (gbrain --version to check)
  • A brain that's been initialized (gbrain init already run; either PGLite or Postgres is fine)
  • A terminal you can paste commands into

That's it. No API keys required for this tutorial — every step works against the bundled pack and local-only commands.

Step 1: See what pack is active today

gbrain schema active --json

You'll see something like:

{
  "pack_name": "gbrain-base",
  "version": "1.0.0",
  "sha8": "...",
  "page_types_count": 22,
  "source_tier": "default"
}

source_tier: "default" means you haven't customized anything — you're on the bundled pack. page_types_count: 22 is the universal starter (person, company, meeting, note, etc.).

You can't mutate bundled packs directly. Step 2 forks it so you have something writable.

Step 2: Fork the bundled pack

gbrain schema fork gbrain-base mine

Output: Forked 'gbrain-base' → 'mine' at ~/.gbrain/schema-packs/mine/pack.json.

The fork is a byte-for-byte copy of gbrain-base living at ~/.gbrain/schema-packs/mine/pack.json. Now you have a writable pack you can mutate.

Step 3: Activate the fork

gbrain schema use mine

Output: Pack: mine (json) ... Active.

Run gbrain schema active --json again to confirm pack_name is now mine and source_tier is home-config (read from ~/.gbrain/config.json).

You've already accomplished something visible — the active pack changed, and any future query will route through your fork. The next four steps add a custom type and prove it works.

Step 4: Add a researcher type

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

Output: Pack: mine (json) + Sha8: <prev> → <new>.

What just happened:

  • The mutation went through withMutation's 8-step skeleton: bundled-guard → per-pack lock → read → mutate → file-plane lint validation → atomic write → audit log → cache invalidation.
  • The pack now declares researcher as an entity primitive bound to people/researchers/, marked extractable: true (eligible for facts extraction) and expert_routing: true (surfaces in whoknows queries).
  • An audit row landed in ~/.gbrain/audit/schema-mutations-YYYY-Www.jsonl with your type name SHA-8-redacted and the prefix's first segment only (people) for privacy.

Verify the type is in the pack:

gbrain schema explain researcher

You'll see the resolved settings printed back.

Step 5: Import some placeholder researcher pages

You need pages under people/researchers/ for the next step to do anything. If your brain repo already has them, skip ahead. If not, drop 3-5 placeholder markdown files into <your-brain-repo>/people/researchers/ and import:

mkdir -p people/researchers
cat > people/researchers/alice-example.md <<'EOF'
---
title: Alice Example
---

ML researcher at Example Lab. Works on contrastive embeddings.
EOF

cat > people/researchers/bob-example.md <<'EOF'
---
title: Bob Example
---

Vision researcher at Widget University. Recent paper on diffusion models.
EOF

cat > people/researchers/charlie-example.md <<'EOF'
---
title: Charlie Example
---

RL researcher at Acme Research. Focus on inverse reinforcement learning.
EOF

gbrain sync

The sync imports the new files. They'll be stored in the database but their type column will still be empty — the new type was added to the pack AFTER these pages already existed (the typical real-world scenario for an agent walking into an existing brain).

Step 6: See the gap with stats

gbrain schema stats --json | jq '.aggregate, .dead_prefixes'

You'll see untyped_pages: 3 (or however many you just imported) and dead_prefixes: [] — your new prefix has 3 matching pages, so it's not dead.

The 3 researcher pages are "orphaned" by type even though they live in the right directory. The next step backfills them.

Step 7: Backfill with sync --apply

First dry-run to see what would happen:

gbrain schema sync --json

You'll see something like:

{
  "schema_version": 1,
  "apply": false,
  "per_prefix": [
    {
      "type": "researcher",
      "prefix": "people/researchers/",
      "would_apply": 3,
      "sample_slugs": ["people/researchers/alice-example", "people/researchers/bob-example", "people/researchers/charlie-example"],
      "applied": 0
    }
  ],
  "total_would_apply": 3,
  "total_applied": 0
}

would_apply: 3 is what you'd touch. sample_slugs is the agent's drilldown signal — if those slugs look wrong, abort. They look right, so apply:

gbrain schema sync --apply

You'll see per-batch progress lines on stderr and a final total_applied: 3. The UPDATE ran in chunks of 1000 (yours fit in one chunk) and never wedged any concurrent writer.

Step 8: Prove the wiring works

gbrain whoknows "machine learning"

If your researcher pages contain ML-related content, they'll surface in the ranked results — even though they're typed researcher, not person or company.

This is the load-bearing demonstration of T1.5 wiring. Pre-v0.40.7.0, whoknows hardcoded ['person', 'company'] as the eligible types and would have ignored your researcher pages entirely. The v0.40.7.0 wiring consults the active pack's expert_routing: true types via expertTypesFromPack(pack.manifest), so your custom type now routes through expert search.

What you built

You now have:

  • A fork of gbrain-base named mine at ~/.gbrain/schema-packs/mine/pack.json, active in your brain via ~/.gbrain/config.json.
  • A researcher page type registered in the pack with entity primitive, people/researchers/ prefix, extractable: true, expert_routing: true.
  • 3 pages typed as researcher (backfilled from disk via gbrain schema sync --apply).
  • A query path that routes through the new type: gbrain whoknows reads the pack and includes researcher in its type filter.

You also exercised the full mutation skeleton: bundled-pack guard, per-pack lock, validation gate, atomic write, audit log, cache invalidation. Every step was idempotent — re-running any of them is a no-op.

Next steps

Add a link verb. A researcher can author a paper. To model that:

gbrain schema add-type paper --primitive annotation --prefix research/papers/ --extractable
gbrain schema add-link-type authored --page-type researcher --target-type paper
gbrain schema graph

The graph now shows researcher --(authored)--> paper.

Add aliases for query closure. If you want gbrain query researcher to also surface person rows (because researchers ARE people):

gbrain schema add-alias researcher person

Read skills/conventions/schema-evolution.md for the decision tree on when to add types vs aliases vs prefixes. The short version: <20 pages → don't pack-codify; 20-100 → alias on existing type; 100+ → first-class type.

Lint your pack before shipping. The 11-rule lint surface (with the optional --with-db flag for DB-aware checks) catches dangling references, prefix collisions, and dead-corpus warnings:

gbrain schema lint --with-db

Commit your pack to source control. If ~/.gbrain/schema-packs/mine/ is a git repo, commit pack.json and push. Your pack survives across machines, and the mutation_count_anomaly lint rule will nudge you when you hit >50 mutations in a week (the "you should be committing this" signal).

For agents (MCP): the same operations are reachable over HTTPS MCP via 9 new ops. Register an admin-scope OAuth client and schema_apply_mutations lets a remote agent compose multi-step refactors as one atomic batch. The batched MCP op + per-pack lock + audit log are the load-bearing primitives that make remote schema authoring safe. See skills/schema-author/SKILL.md for the agent dispatcher.

Undo a mistake. Every mutation primitive has an inverse (remove-type, remove-alias, remove-prefix, remove-link-type, set-extractable false, etc.). If you fork twice and want to revert, gbrain schema downgrade restores the previous active pack from ~/.gbrain/schema-pack-history.jsonl.

Related docs

  • Reference: gbrain schema --help for the full 22-verb CLI surface; CLAUDE.md's "Schema Cathedral v3 (v0.40.7.0)" section for the module-by-module architecture.
  • How-to: skills/schema-author/SKILL.md — the agent dispatcher with the 7-phase workflow (brain → assess → propose → apply → sync → verify → commit).
  • Explanation: skills/conventions/schema-evolution.md — when to add a type vs alias vs prefix.
  • Plan + decisions: the original design captured 21 decisions including the bundled-pack guard rationale (D6), the empty-filter fallback contract (D4), and the MCP non-localOnly trust posture (D2). Lives in ~/.claude/plans/system-instruction-you-are-working-recursive-thacker.md (private).