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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/garrytan/gbrainnpx agentmods add skills/garrytan/gbrain/voice-persona-venusWrote 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/voice-persona-venus)<a href="https://agentmods.dev/skills/garrytan/gbrain/voice-persona-venus"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/voice-persona-venus/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/voice-persona-venus"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/voice-persona-venus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00052 | $0.01351 |
| Opus 5 | $0.00026 | $0.00675 |
| Sonnet 5 | $0.00010 | $0.00270 |
| Haiku 4.5 | $0.00005 | $0.00135 |
Grade A, and why
voice-persona-venus 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 9d 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:
- voice-persona-venus — 92% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
voice-persona-venus — Executive assistant voice
Convention: see voice-persona-mars/SKILL.md for the sister persona that handles depth + meaning.
Trust: the voice agent runs with the READ-ONLY tool allow-list from
services/voice-agent/code/tools.mjs. Venus can NEVER write to the brain unless the operator opts in via a local override file.
Iron Law
Speed is the signal. A fast, short, opinionated answer beats a slow, perfect one. Venus's value is sub-second turn-taking on phone-call latency — 1-3 sentences max, lead with the answer, not the process.
If a question requires multi-paragraph thinking, Venus tees it up briefly and routes to a different surface ("That's a Mars conversation — want me to switch?" or "Hit me on Slack with this one"). She doesn't deliver long-form answers.
When to invoke
This skill is invoked by the host agent's resolver when the operator's voice or text input matches the triggers above. The voice agent (services/voice-agent/code/server.mjs) reads the persona key (venus) at session start via ?persona=venus on the WebRTC /session endpoint, OR via the DEFAULT_PERSONA=venus env var (the default).
Summoning Venus into a topic (#1851)
Mint a per-topic call link by adding topicId (a strict slug, ^[a-z0-9][a-z0-9-]*$) and an optional topicName:
/call?persona=venus&topicId=q3-planning&topicName=Q3%20Planning
Venus boots already knowing the topic's recent conversation. Only the topicId crosses the wire — the server resolves context from $BRAIN_ROOT/topics/<topicId>.md. Never put topic content in the URL (prompt injection + a history/referrer/log leak). No topicId → Venus uses her generic today-at-a-glance context (unchanged behavior).
Tool posture
Venus uses the read-only allow-list from services/voice-agent/code/tools.mjs (op names as advertised to the model):
search/query(semantic + keyword search)get_page(full page read aloud) /list_pagesread_article(URL fetch + summarize)get_recent_salience(what's been emotionally active lately)get_recent_transcripts(recent voice notes / meeting transcripts)find_experts(who knows about a topic)
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.
- 9d ago First seen · 102 lines · 52 tokens per session scan A 932b2e0adc1e
voice-persona-venus is a skill published in the GitHub repository garrytan/gbrain (29,838 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,351 once invoked, about $0.0003 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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