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 agentmods add skills/yuno15-bb/c-brain/recallnpx skills add Yuno15-bb/c-brain --skill recallgit clone --depth 1 https://github.com/Yuno15-bb/c-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/skills/yuno15-bb/c-brain/recall)<a href="https://agentmods.dev/skills/yuno15-bb/c-brain/recall"><img src="https://agentmods.dev/badge/skills/yuno15-bb/c-brain/recall.svg" alt="Measured on agentmods" 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 | $0.00071 | $0.00348 |
| Opus 5 | $0.00036 | $0.00174 |
| Sonnet 5 | $0.00014 | $0.00070 |
| Haiku 4.5 | $0.00007 | $0.00035 |
Grade A, and why
recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Recall from the trunk
Retrieve what is already known before working something out again.
How
Run the recall, top 5, and read what comes back:
brain recall "$ARGUMENTS"
If brain is not on the PATH, the plugin ships it — call it directly:
"${CLAUDE_PLUGIN_ROOT}/bin/brain" recall "$ARGUMENTS".
Each result gives a score, a note name and a path relative to the trunk
(~/.c-brain/trunk). Open the ones above the noise floor and actually read
them — the ranking is lexical, so it tells you where to look, never what is
true.
Then
- Say what the notes establish, and cite each one by its path so the user can check you.
- Say plainly when nothing relevant came back. A confident answer assembled from three weakly-matching notes is worse than "the trunk has nothing on this" — it looks like memory and is not.
- Never rewrite a note as a side effect of reading it.
Scale
Recall holds well to about a thousand notes and degrades past that
(tests/recall_benchmark.py publishes the numbers). On a large trunk, prefer
several narrow queries over one broad one.
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.
- 4d ago First seen · 39 lines · 71 tokens per session scan A 32366e15f75a
recall is a skill published in the GitHub repository Yuno15-bb/c-brain (2 stars, last pushed 10d ago), licensed Apache-2.0. It adds 71 tokens to every session and 348 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 2026-08-31.
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