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 media-ingestgit 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/media-ingest)<a href="https://agentmods.dev/skills/garrytan/gbrain/media-ingest"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/media-ingest/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/media-ingest"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/media-ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00052 | $0.01355 |
| Opus 5 | $0.00026 | $0.00678 |
| Sonnet 5 | $0.00010 | $0.00271 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
media-ingest 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.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Media Ingest Skill
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain.
Filing rule: Read
skills/_brain-filing-rules.mdbefore creating any new page.
Input
| Parameter | Required | Description |
|---|---|---|
| source | yes | URL, file path, or uploaded file reference |
| title | no | Override title (auto-detected if omitted) |
| target_slug | no | Override page slug (auto-generated if omitted) |
Contract
This skill guarantees:
- Every ingested media item has a brain page with analysis (not just a transcript dump)
- Transcripts (video/audio) saved in raw and human-readable formats
- Entity extraction: every person and company mentioned gets back-linked
- Raw source files preserved via
gbrain files upload-raw - Filing by primary subject, not by media format
Convention: See
skills/conventions/quality.mdfor Iron Law back-linking.
Every mention of a person or company with a brain page MUST create a back-link.
Phases
Phase 1: Identify format and fetch
| Format | Action |
|---|---|
| YouTube/video URL | Fetch transcript (Whisper, transcription service, or captions) |
| Audio file | Transcribe with available STT service |
| Extract text (OCR if needed) | |
| Book PDF | Extract text, identify chapters/sections |
| Screenshot/image | OCR via vision model, extract text and entities |
| GitHub repo | Clone, read README + key files, summarize architecture |
Phase 2: Upload raw source
Save the original file for provenance: gbrain files upload-raw <file> --page <slug>
Phase 3: Create brain page
File by primary subject (not format). Use this template:
# {Title}
**Source:** {URL or file path}
**Format:** {video/audio/PDF/book/screenshot/repo}
**Created:** {date}
## Summary
{Key points, not a transcript dump}
## Key Segments / Highlights
{For video/audio: timestamped highlights. For books: chapter summaries.}
## People Mentioned
{List with links to brain pages}
## Companies Mentioned
{List with links to brain pages}
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 · 147 lines · 52 tokens per session scan A 4cb0dee1011d
media-ingest is a skill published in the GitHub repository garrytan/gbrain (29,751 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,355 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 2026-08-30.
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