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 commands/slowww-ai/second-brain/briefgit clone --depth 1 https://github.com/slowww-ai/second-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/slowww-ai/second-brain/brief)<a href="https://agentmods.dev/commands/slowww-ai/second-brain/brief"><img src="https://agentmods.dev/badge/commands/slowww-ai/second-brain/brief.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 | $0.00011 | $0.00319 |
| Opus 5 | $0.00005 | $0.00160 |
| Sonnet 5 | $0.00002 | $0.00064 |
| Haiku 4.5 | $0.00001 | $0.00032 |
Grade A, and why
brief 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
Topic: $ARGUMENTS
Your job is to produce a brief on the given topic using only information from wiki/notes/ (and wiki/raw/ for citations when needed).
Steps:
- Run
python scripts/search.py "<topic keywords>"with several query variations to find relevant notes. - Also try
python scripts/search.py --tag <likely-tag>if a tag seems obvious. - Read the top ~10 hits in full. Follow
[[wikilinks]]one hop when useful. - Before writing, first ask the user (using AskUserQuestion) to confirm:
- the intended audience and tone
- desired length (one-pager, deep dive, etc.)
- output format (markdown, docx, pdf) — default markdown unless these are already clear from the arguments.
- Draft the brief in
wiki/outputs/<date>-<slug>.mdwith sections: TL;DR, Key points, Details, Open questions, Sources. - In "Sources", list the note IDs you used as a bulleted list.
- If the user asked for
.docxor.pdf, use the appropriate skill to render the final file alongside the markdown. - Share the output file with a
computer://link and a one-sentence summary. Do not restate the whole brief in chat.
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 · 24 lines · 11 tokens per session scan A 42bd7fd2610a
brief is a command published in the GitHub repository slowww-ai/second-brain (10 stars, last pushed 21d ago), licensed MIT. It adds 11 tokens to every session and 319 once invoked, about $0.0001 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.
Other commands, from other repositories
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checklist
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.