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 laozhong86/gbrain --skill enrichgit clone --depth 1 https://github.com/laozhong86/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/laozhong86/gbrain/enrich)<a href="https://agentmods.dev/skills/laozhong86/gbrain/enrich"><img src="https://agentmods.dev/badge/skills/laozhong86/gbrain/enrich/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/laozhong86/gbrain/enrich"><img src="https://agentmods.dev/badge/skills/laozhong86/gbrain/enrich.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 | $0.00019 | $0.00212 |
| Opus 5 | $0.00010 | $0.00106 |
| Sonnet 5 | $0.00004 | $0.00042 |
| Haiku 4.5 | $0.00002 | $0.00021 |
Grade A, and why
gbrain-enrich 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 9d 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.
This is a copy
100% identical to gbrain-enrich — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Enrich Skill
Workflow
- Read the current page first with
gbrain get <slug>. - Store the full external payload with
gbrain call brain_raw '{"slug":"...","source":"...","data":{...}}'. - Distill only durable facts into compiled truth: role, company, status, major relationships, meaningful background.
- If the payload adds dated evidence, add it to timeline with
gbrain timeline-add. - Link newly confirmed entities with
gbrain link.
Rules
- Raw payload goes into
raw_data, not the page body. - Do not dump long skill lists, full resumes, or complete API responses into compiled truth.
- If enrichment conflicts with newer local knowledge, flag the conflict instead of silently overwriting it.
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.
- 9d ago First seen · 26 lines · 19 tokens per session scan A 062ade7dbaae
gbrain-enrich is a skill published in the GitHub repository laozhong86/gbrain (8 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 212 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gbrain-enrich, differing in 0 lines, and is treated as a copy.
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