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/cross-modal-reviewWrote 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/cross-modal-review)<a href="https://agentmods.dev/skills/garrytan/gbrain/cross-modal-review"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/cross-modal-review/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/cross-modal-review"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/cross-modal-review.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 190 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 | $0.00076 | $0.01547 |
| Opus 5 | $0.00038 | $0.00773 |
| Sonnet 5 | $0.00015 | $0.00309 |
| Haiku 4.5 | $0.00008 | $0.00155 |
Grade A, and why
cross-modal-review 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 10d 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:
- cross-modal-review — 100% identical, 0 lines differ
- cross-modal-review — 100% identical, 0 lines differ
- cross-modal-review — 86% identical, 76 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Modal Review
Convention: see conventions/cross-modal.yaml for the review pairs and refusal routing chain.
Relationship to
gbrain eval cross-modal: This skill is the manual mid-flow gate (one model reviews work product before commit, with refusal routing). Thegbrain eval cross-modalcommand (v0.27.x) is a sibling surface: 3 different-provider frontier models score-and-iterate on a documented dimension list before tests cement behavior. Use this skill for ad-hoc second opinions; usegbrain eval cross-modalfor the skillify Phase 3 quality gate. The two are complementary, not redundant.
Contract
This skill guarantees:
- Work product is reviewed by a different model before finalizing.
- The review is graded against the originating skill's Contract section (what was promised), not vibes.
- Agreement and disagreement are reported transparently.
- Refusal from one model triggers a silent switch to the next in chain.
- The user always makes the final decision (user sovereignty).
When to invoke (v0.25.1 gating)
Invoke this skill when:
- Significant code changes — any commit touching 5+ files or 100+ lines. Architecture decisions, refactors, API changes.
- Security-sensitive changes — auth flows, brain-write trust boundaries, webhook transforms, cross-skill data passing.
- Stuck or churning — 2+ iterations on the same problem without progress.
- Pre-bulk-operation — before running batch enrichment, migrations, or bulk writes (see conventions/test-before-bulk.md).
- Skill creation / modification — new or rewritten skills that affect operational behavior.
- Brain-page quality concerns — when brain writes need validation against the originating skill's Contract.
Do NOT invoke for:
- Simple memory writes or brain-page updates
- Single-file typo fixes
- Routine cron output or heartbeat operations
- Git commit / push of already-reviewed work
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.
- 10d ago First seen · 198 lines · 76 tokens per session scan A 685233b1afd4
cross-modal-review is a skill published in the GitHub repository garrytan/gbrain (29,751 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 1,547 once invoked, about $0.0004 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
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.