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 reference-packgit 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/reference-pack)<a href="https://agentmods.dev/skills/garrytan/gbrain/reference-pack"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/reference-pack/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/reference-pack"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/reference-pack.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 | $0.00031 | $0.01013 |
| Opus 5.5 | $0.00012 | $0.00405 |
| Sonnet 5.5 | $0.00006 | $0.00203 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
reference-pack 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 1mo 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:
- reference-pack — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reference-pack
This is the canonical reference for a third-party gbrain skillpack. Read its tree once and you know how to author one.
What this skill does
When the user asks how third-party gbrain skillpacks work, this skill points them at the four artifacts every pack ships:
skillpack.json— declares pack metadata + which artifacts the doctor scores (skills, unit_tests, e2e_tests, llm_evals, routing_evals, runbooks, changelog).skills/<name>/SKILL.md— frontmatter (name, description, mutating, triggers) plus markdown body. Agents route ontriggers:; the body is the in-context instruction set.runbooks/bootstrap.md— agent-readable post-scaffold steps. gbrain DISPLAYS this afterscaffoldlands; the agent walks per-step at its own discretion. No auto-executor (codex T1 supply-chain hardening).CHANGELOG.md— Keep-a-Changelog shape. The doctor'schangelog_ present_and_currentdimension fails if there's no## [<version>]entry matching the manifest'sversion.
What the doctor scores
Ten binary dimensions, split into:
Core (5; must all pass to publish at any tier):
manifest_valid— schema-validates skillpack.jsonskills_have_skill_md— every listed skill has SKILL.md with name / description / triggersrouting_evals_present— each skill has routing-eval.jsonl with= 5 intents
skills_have_unique_triggers— MECE at the pack levelchangelog_present_and_current— CHANGELOG entry for current version
Quality badges (5; earn for tier eligibility):
unit_tests_present— manifest.unit_tests matches >= 1 filee2e_tests_present— manifest.e2e_tests matches >= 1 filellm_eval_present—*.judge.jsonwith >= 3 casesbootstrap_runbook_present— non-empty runbooks/bootstrap.mdlicense_present— LICENSE / LICENSE.md / LICENSE.txt non-empty
Tier eligibility:
endorsed— all 10 (gates: Garry'sendorsements.jsonoverlay in the registry)community— all 5 core + >= 3 of 5 badges (default tier on PR merge)experimental— all 5 core + < 3 badgesblocked— any core fails
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.
- 1mo ago First seen · 104 lines · 31 tokens per session scan A e15a5cde2b58
reference-pack is a skill published in the GitHub repository garrytan/gbrain (31,034 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,013 once invoked, about $0.0001 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…