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 ask-usergit 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/ask-user)<a href="https://agentmods.dev/skills/garrytan/gbrain/ask-user"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/ask-user/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/ask-user"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/ask-user.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 | $0.00060 | $0.02227 |
| Opus 5.5 | $0.00024 | $0.00891 |
| Sonnet 5.5 | $0.00012 | $0.00445 |
| Haiku 4.5 | $0.00006 | $0.00223 |
Grade A, and why
ask-user 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 13d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ask User — Choice Gate Pattern
Contract
- Present 2-4 options (no more — decision paralysis kicks in past 4).
- Always include an escape hatch (Skip, Cancel, or "none of these").
- Stop the turn immediately after presenting choices. No follow-up tool calls, no preemptive action, no default-and-proceed.
- The user's response triggers the next turn. Acknowledge briefly, then branch.
- One question per message — never stack multiple choice gates.
- Self-explanatory option labels: action verb plus brief qualifier, not "Option 1".
What This Is
A formalized pattern for presenting users with 2-4 options and stopping execution until they respond. This is the canonical way to gate on user input in any GBrain-powered agent.
This is NOT a traditional async/await. In an LLM agent, "gating" means:
- Present the choices (buttons or numbered options)
- Explicitly stop the current turn (do not proceed)
- The user's response triggers the next turn
- Read the response and branch accordingly
When To Use
- Ambiguous requests with multiple valid interpretations
- Destructive operations (bulk deletes, overwrites)
- Filing/routing decisions ("where should this go?")
- Priority triage ("which should I do first?")
- Cold-start phase gates ("ready for the next import source?")
- Any fork where the wrong default wastes significant work
When NOT To Use
- Clear, unambiguous instructions → just do it
- Low-stakes decisions → pick the best option and mention it
- Time-critical operations where delay costs more than a wrong choice
- When the user has already expressed a preference
How To Present Choices
Platform-agnostic format (works everywhere)
Present choices as a clear question with numbered or labeled options:
🔀 **How should I handle this?**
[context about the decision — 1-3 lines max]
1. **Option A** — short description
2. **Option B** — short description
3. **Option C** — short description
4. **Skip** — do nothing for now
With inline buttons (Telegram, Discord, Slack)
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.
- 13d ago Changed 63f535500e5e
- 14d ago Changed · +1 lines f0bfe573fc3a
- 16d ago Changed · +7 lines bbf43870cb9f
- 1mo ago First seen · 253 lines · 60 tokens per session scan A 3eb82b0aeb87
ask-user is a skill published in the GitHub repository garrytan/gbrain (31,034 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 2,227 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…