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 querygit 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/query)<a href="https://agentmods.dev/skills/garrytan/gbrain/query"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/query/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/query"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00040 | $0.02702 |
| Opus 5.5 | $0.00016 | $0.01081 |
| Sonnet 5.5 | $0.00008 | $0.00540 |
| Haiku 4.5 | $0.00004 | $0.00270 |
Grade A, and why
query 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query Skill
Answer questions using the brain's knowledge with 3-layer search and synthesis.
Memory verbs (MEMORY_VERBS v1, gbrain ≥ 0.43). When connected to a brain over MCP, prefer the seven frozen memory verbs for memory work — they carry provenance, evidence, and a server-enforced token budget:
recall(query | entity, budget_tokens)— the budget-packed memory read. Use it instead of baresearchfor "what do we know that we SAVED about X".entity(name)— a zero-LLM person/company/project card (aliases, last-touched, open threads, top edges). Use it instead ofget_page+get_backlinkswhen you just need the card.synthesize(question)— the explicitly-expensive cross-page answer; the heavy version ofquery. Reach for it only when the answer must combine evidence across pages. Fall back tosearch/query/get_pagewhen the verbs aren't on the surface (pre-0.43 servers;--surface fullincludes the verbs alongside every other op). Seedocs/protocol/MEMORY_VERBS_v1.md.
Contract
This skill guarantees:
- Every answer is grounded in brain content (no hallucination)
- Every claim has a citation tracing back to a specific page slug
- Gaps are flagged explicitly ("the brain doesn't have information on X")
- Source precedence is respected (user statements > compiled truth > timeline > external)
- Conflicting sources are noted with both citations
Phases
- Decompose the question into search strategies:
- Keyword search for specific names, dates, terms
- Semantic query for conceptual questions
- Structured queries (list by type, backlinks) for relational questions
- Execute searches:
- Cheap-hybrid search gbrain for exact tokens / known names (search)
- Full-hybrid search gbrain for concept questions (query;
--expandadds multi-query expansion) - List pages in gbrain by type or check backlinks for structural queries
- Read top results. Read the top 3-5 pages from gbrain to get full context.
- Synthesize answer with citations. Every claim traces back to a specific page slug.
- Flag gaps. If the brain doesn't have info, say "the brain doesn't have information on X" rather than hallucinating. Read the result's notices first (see "When it fails"): a degraded or truncated result is not a gap.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago Changed d75f8eda6c2d
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- 14d ago Changed · +1 lines 3fea42c9442f
- 15d ago Changed b34310c7cb5f
- 16d ago Changed · +19 lines 22248c190358
- 19d ago Changed · +3 lines c754d882e7c8
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- 25d ago Changed · +36 lines 7a5857b4898f
- 1mo ago First seen · 171 lines · 40 tokens per session scan A b12aae4e86b8
query is a skill published in the GitHub repository garrytan/gbrain (31,034 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 2,702 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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