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.
git clone --depth 1 https://github.com/eliransu/digital-brainWrote 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/commands/eliransu/digital-brain/note)<a href="https://agentmods.dev/commands/eliransu/digital-brain/note"><img src="https://agentmods.dev/badge/commands/eliransu/digital-brain/note/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/commands/eliransu/digital-brain/note"><img src="https://agentmods.dev/badge/commands/eliransu/digital-brain/note.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 | $0.00058 | $0.00257 |
| Opus 5.5 | $0.00023 | $0.00103 |
| Sonnet 5.5 | $0.00012 | $0.00051 |
| Haiku 4.5 | $0.00006 | $0.00026 |
Grade A, and why
note 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 1mo 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.
What it actually says
Invoke the note skill from the digital-brain plugin. Follow its SKILL.md exactly: parse $ARGUMENTS to extract optional kind (task|reminder), due time (by … / tomorrow / in Nh|Nd — default today 18:00 or tomorrow 09:00), and @Person assignee. Create wiki/notes/<YYYY-MM-DD>-<HHMM>-<slug>.md with frontmatter (note_kind, due, expires = created + 7d, status: open, assignee). Update wiki/notes/_index.md, touch wiki/hot.md only if due ≤ 24h, wikilink any @Person mentions (file via ppl flywheel if new), and append to wiki/log.md. Always run the 7-day expiration sweep first.
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.
- 1mo ago First seen · 6 lines · 58 tokens per session scan A 87d7c84e7e00
note is a command published in the GitHub repository eliransu/digital-brain (1 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 257 once invoked, about $0.0002 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 2026-08-31.
Other commands, from other repositories
vault-recap
Generate a period review from vault activity.
rvbc
RvBC — the older spelling of /rnbc, the RuvNet Brain Console (same console as /rnbc, /rnb, /brain-console and /ruvnet-brain:configure — every spelling works). Your whole RuvNet stack on one page. Read-only until you click.
wiki-query
Ask questions against the wiki. Synthesizes answers from wiki pages with cross-reference citations.
wiki-digest
Generate a daily or weekly digest of wiki changes — new sources, pages, insights, and gaps.
start-0-5.en
Auto-run by AI: Run uv run python tools/setupprogress.py show to display the current setup progress.
setup-m365cli.es
Lesson command — Setup de M365 CLI.