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 functional-area-resolvergit 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/functional-area-resolver)<a href="https://agentmods.dev/skills/garrytan/gbrain/functional-area-resolver"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/functional-area-resolver/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/functional-area-resolver"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/functional-area-resolver.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 302 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00089 | $0.04419 |
| Opus 5.5 | $0.00036 | $0.01768 |
| Sonnet 5.5 | $0.00018 | $0.00884 |
| Haiku 4.5 | $0.00009 | $0.00442 |
Grade A, and why
functional-area-resolver 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- functional-area-resolver — 100% identical, 18 lines differ
- functional-area-resolver — 94% identical, 23 lines differ
- functional-area-resolver — 88% identical, 201 lines differ
How it starts
The opening of the file, as written. The whole thing — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Functional-Area Resolver — Pattern for Compressing Routing Tables
Problem
Routing files (RESOLVER.md, AGENTS.md) grow as skills are added. Each skill gets its own row (trigger -> skill path). At ~200+ skills this hits 25-30KB, eating context budget that should go to actual work.
Solution: Functional-Area Dispatchers
Replace N rows per area with one entry per functional area. Each entry
lists all sub-skills it can dispatch to in a (dispatcher for: ...) clause.
Before (270 rows, 25KB)
- Creating/enriching a person or company page -> `enrich`
- Fix broken citations in brain pages -> `citation-fixer`
- Publish/share a brain page as link -> `brain-publish`
- Generate PDF from brain page -> `brain-pdf`
- Read a book through lens of a problem -> `strategic-reading`
- Personalized book analysis -> `book-mirror`
- Brain integrity -> `brain-librarian`
...
After (13 rows, 13KB)
- **Brain & knowledge**: create/enrich/search/export brain pages, filing,
citations, publishing, book analysis, strategic reading, concept synthesis,
archive mining -> `brain-ops` (dispatcher for: enrich, query, brain-pdf,
brain-publish, brain-export, brain-librarian, citation-fixer, book-mirror,
strategic-reading, concept-synthesis, archive-crawler, ...)
Why It Works
The LLM doesn't need one row per sub-skill. It needs:
- Area recognition — "this is about brain pages" -> Brain & Knowledge
- Sub-skill visibility — the
(dispatcher for: ...)list shows what's available - The skill file itself — once the LLM reads
brain-ops/SKILL.md, it has full routing detail
This is a two-layer dispatch: routing file routes to the area, the area skill routes to the specific sub-skill. Each layer does one job well.
A/B Eval Results
Three resolver architectures tested across three Anthropic frontier models (Opus 4.7, Sonnet 4.6, Haiku 4.5) on real production AGENTS.md content, 20 hand-authored training fixtures + 5 held-out blind fixtures, n=3 seeded repeats per (fixture, variant). Two scoring rules: STRICT (predicted slug exactly equals expected) and LENIENT (predicted is in the same dispatcher area as expected). Both matter:
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.
- 13d ago Changed · +8 lines 65726a6c268e
- 14d ago Changed · +1 lines c84053916210
- 16d ago Changed · +7 lines 1d2dd3826999
- 1mo ago First seen · 354 lines · 89 tokens per session scan A 52df04bc4f8e
functional-area-resolver is a skill published in the GitHub repository garrytan/gbrain (31,034 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 4,419 once invoked, about $0.0004 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…