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 agentmods add skills/garrytan/gbrain/briefingnpx skills add garrytan/gbrain --skill briefinggit 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/briefing)<a href="https://agentmods.dev/skills/garrytan/gbrain/briefing"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/briefing.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00016 | $0.01964 |
| Opus 5 | $0.00008 | $0.00982 |
| Sonnet 5 | $0.00003 | $0.00393 |
| Haiku 4.5 | $0.00002 | $0.00196 |
Grade A, and why
briefing 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.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Briefing Skill
Compile a daily briefing from brain context.
Filing rule: When the briefing creates or updates brain pages, follow
skills/_brain-filing-rules.md.
Contract
- Every fact in the briefing includes an inline
[Source: slug, updated DATE]citation. - Meeting participants are resolved against the brain; gaps are explicitly flagged.
- Active deals and action items include deadlines and recency context.
- The briefing is read-only: no brain pages are created or modified unless the user explicitly requests it.
- Stale alerts surface pages relevant to today's context, not just all stale pages.
Pre-Briefing Context Pull
Run these BEFORE composing the briefing sections. All four pulls are read-only.
0a. Salience scan. Surface pages with high emotional or activity salience:
gbrain salience --days 7
Returns pages ranked by emotional weight and recent activity. Fold the top 5-10 into the briefing under a "High-Salience Pages" section — these are the entities and topics that are emotionally or operationally hot right now. Use this to prioritize which meetings/deals/people get the most briefing depth.
0b. Anomaly detection. Surface statistical anomalies in the brain:
gbrain anomalies
Defaults to today against a 30-day baseline; widen with
--lookback-days N or lower the threshold with --sigma 2. Flags cohorts
(by tag, by type) whose activity broke from their normal cadence — sudden
spikes in mentions or pages updating far off their usual rhythm. Add hits to
an "Anomalies" section after the brain pulse.
0c. Personal recall. Check stored personal facts and preferences before composing:
gbrain recall --query "current priorities and preferences" --json
Use recall to pull personal context — dietary preferences, communication preferences, prior commitments or promises made. This prevents the briefing from contradicting things the user has previously stated or decided.
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.
- 6d ago First seen · 209 lines · 16 tokens per session scan A f86ad2238104
briefing is a skill published in the GitHub repository garrytan/gbrain (29,591 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 1,964 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
pinchtab-mcp
Use this skill when a task requires browser automation through PinchTab's MCP server connected to a remote browser instance. Covers navigation, element interaction, data extraction, form filling, multi-step flows, and session management via MCP tools.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
peekaboo
Capture and automate macOS UI with the Peekaboo CLI.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.