GBrain is a memory and retrieval layer for AI agents that searches, connects, and synthesizes information from stored sources. It is used to give coding agents and autonomous agents access to knowledge beyond their current code, including shared company information with access controls. The catalogue add-ons help agents operate GBrain and connect it to agent workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add garrytan/gbrain --skill schema-unifygit clone --depth 1 https://github.com/garrytan/gbrainWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/garrytan/gbrain/schema-unify)<a href="https://agentmods.dev/skills/garrytan/gbrain/schema-unify"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/schema-unify/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/garrytan/gbrain/schema-unify"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/schema-unify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.03059 |
| Opus 5 | $0.00049 | $0.01529 |
| Sonnet 5 | $0.00020 | $0.00612 |
| Haiku 4.5 | $0.00010 | $0.00306 |
Grade A, and why
schema-unify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- schema-unify — 86% identical, 42 lines differ
How it starts
The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema Unification (gbrain-base → gbrain-base-v2)
v0.41.22 ships gbrain-base-v2 — a 15-type DRY/MECE taxonomy (14 canonical + note catch-all) — as the install default for new brains. Existing brains on gbrain-base can opt in via the pack_upgrade_available onboard finding + the unify-types PROTECTED Minion handler.
This skill is the playbook for that migration.
brain_first: exempt
This skill is ABOUT the brain's shape — it can't depend on the brain it's reshaping. No gbrain search lookup first; jump straight to onboard.
When this skill fires
- Agent runs
gbrain onboard --checkand seespack_upgrade_availableortype_proliferationwarnings - User asks "what is the canonical taxonomy / how do I clean up my page types / migrate to v2"
- A
dangling_aliasesfinding surfaces (post-unify GC) - An agent ingesting from a custom pack wants to consult the v2 taxonomy as a reference
Mental model (one paragraph)
A production gbrain brain accreted 94 distinct pages.type values over years of ingestion: tweet / tweet-thread / tweet-bundle / tweet-single / media/x-tweet/bundle / tweet-stub all coexisting; 5.5K concept-redirect pages; atom-partner-link pages that should be links; civic / framework / insight / memo / anecdote one-offs. The cure: collapse to 15 canonical types (person, company, media, tweet, social-digest, analysis, atom, concept, source, deal, email, slack, writing, project, note) with subtypes/format/origin pushed to frontmatter, alias-rows for redirects, real link-rows for edge-shaped pages, and a catch-all that bins long-tail unknowns to note with frontmatter.legacy_type = <original> for rollback.
Workflow
Phase 1: Discovery
Confirm the brain is actually on gbrain-base (not already on v2).
gbrain schema active --json | jq -r '.identity'
Expected: [email protected]+<sha>. If you see gbrain-base-v2@..., the brain is already on v2 — skip the migration.
Then run onboard to see what would change:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 272 lines · 99 tokens per session scan A e9ac84018d67
schema-unify is a skill published in the GitHub repository garrytan/gbrain (29,751 stars, last pushed yesterday), licensed MIT. It adds 99 tokens to every session and 3,059 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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