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/hamsurang/kitWrote 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/hamsurang/kit/brain)<a href="https://agentmods.dev/commands/hamsurang/kit/brain"><img src="https://agentmods.dev/badge/commands/hamsurang/kit/brain/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/hamsurang/kit/brain"><img src="https://agentmods.dev/badge/commands/hamsurang/kit/brain.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 | $0.00031 | $0.00375 |
| Opus 5.5 | $0.00012 | $0.00150 |
| Sonnet 5.5 | $0.00006 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
brain 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
/brain — Load Vault Knowledge
Load notes from Obsidian vault as conversational context.
Usage
/brain rust ownership
/brain react hooks
/brain 이전에 정리한 캐싱 전략
Prerequisites
- Load
references/vault-setup.mdand resolve vault path - Verify vault structure exists
Execution
- Parse topic — extract keywords from the argument (or ask if empty)
- MOC scan — Grep
2-maps/*.mdfor topic keywords - Read top 3 MOCs — extract
[[wikilinks]]from matching MOCs - Read up to 5 linked notes — prioritize most relevant by keyword match
- Fallback search — if no MOCs matched, Grep
1-zettelkasten/and0-inbox/directly for keywords to find orphan notes not yet linked to any MOC - Context budget — stay within 15,000 characters total
- Present findings — summarize loaded notes and answer in context
Output Format
vault에서 [N]개 노트를 찾았습니다:
- [[note-1-title]] (from MOC: topic-name)
- [[note-2-title]] (from MOC: topic-name)
[Answer the user's question using the loaded note content]
No Results
"[topic]"에 대한 노트를 찾지 못했습니다.
검색한 키워드: [terms]
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 · 53 lines · 31 tokens per session scan A 7042ce846791
brain is a command published in the GitHub repository hamsurang/kit (19 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 375 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.