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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add garrytan/gbrain/plugin install 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/frontmatter-guard)<a href="https://agentmods.dev/skills/garrytan/gbrain/frontmatter-guard"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/frontmatter-guard/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/frontmatter-guard"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/frontmatter-guard.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00068 | $0.02474 |
| Opus 5 | $0.00034 | $0.01237 |
| Sonnet 5 | $0.00014 | $0.00495 |
| Haiku 4.5 | $0.00007 | $0.00247 |
Grade A, and why
frontmatter-guard 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 7d 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:
- frontmatter-guard — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontmatter Guard Skill
Convention: see
skills/conventions/quality.mdfor citation rules; this skill is structural validation, not citation auditing.
Contract
This skill guarantees:
- Every brain page is scanned against the eight canonical frontmatter validation classes
- Mechanical errors (nested quotes, missing closing
---, null bytes, slug mismatch) are auto-repairable on demand with.bakbackups - Validation logic is shared with
gbrain doctor'sfrontmatter_integritysubcheck — single source of truth - Reports per source (gbrain is multi-source since v0.18.0); never silently audits the wrong root
Why This Exists
Brain pages pile up over months. Agents write them with malformed frontmatter:
- Missing closing
---(entity detector bugs) - Unstructured YAML in meeting pages (ingestion bugs)
- Slug mismatches (path renames not propagated)
- Null bytes (binary corruption from copy-paste accidents)
- Nested double quotes in titles (
title: "Alice "Ace" Example")
Without a guard, these accumulate silently until gbrain sync chokes or search returns garbage. The guard makes the failure visible at audit time and trivially fixable.
Validation classes
| Code | Meaning | Auto-fixable? |
|---|---|---|
MISSING_OPEN |
File doesn't start with --- |
No (needs human) |
MISSING_CLOSE |
No closing --- before first heading |
Yes |
YAML_PARSE |
YAML failed to parse | Sometimes (depends on cause) |
SLUG_MISMATCH |
Frontmatter slug: differs from path-derived slug |
Yes (removes the field) |
NULL_BYTES |
Binary corruption (\x00) |
Yes |
NESTED_QUOTES |
title: "outer "inner" outer" shape |
Yes |
NON_STRING_FIELD |
title/type/slug is an unquoted non-string scalar (e.g. title: 123, slug: 2024-06-01) |
No (quote the value) |
EMPTY_FRONTMATTER |
Open + close present but nothing between | No (needs human) |
Phases
Phase 1: Audit
Run a read-only scan across all registered sources (or one with --source <id>).
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.
- 7d ago First seen · 233 lines · 68 tokens per session scan A a20fed7da1ae
frontmatter-guard is a skill published in the GitHub repository garrytan/gbrain (29,789 stars, last pushed 2d ago), licensed MIT. It adds 68 tokens to every session and 2,474 once invoked, about $0.0003 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 2026-09-03.
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