Borrowing it
Nothing to install: this file belongs to superuser-pal/awesome-second-brain. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/superuser-pal/awesome-second-brain/main/.claude/commands/capture/brain-dump.mdgit clone --depth 1 https://github.com/superuser-pal/awesome-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/superuser-pal/awesome-second-brain/brain-dump)<a href="https://agentmods.dev/commands/superuser-pal/awesome-second-brain/brain-dump"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/brain-dump/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/superuser-pal/awesome-second-brain/brain-dump"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/brain-dump.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 | $0.00032 | $0.00784 |
| Opus 5.5 | $0.00013 | $0.00314 |
| Sonnet 5.5 | $0.00006 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00078 |
Grade A, and why
brain-dump 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain Dump
"What's on your mind? Share your thoughts freely."
Usage
/brain-dump <content>
This command is designed for deeper reflection and stream-of-consciousness capture. It extracts atomic observations and stages them in inbox/raw/.
Workflow
1. Collect Raw Input
Accept any format:
- Stream of consciousness
- Voice-to-text output
- Bullet points
- Rambling paragraphs
No filtering, no judgment — capture everything exactly as provided.
2. Content Analysis
Internal analysis (not written to file):
- Main Themes: [3-5 primary topics]
- Supporting Ideas: [related concepts]
- Action Items: [tasks identified if any]
3. Observation Extraction
Extract atomic observations from the raw content, categorizing each.
Process:
- Identify distinct statements in the raw input.
- For each statement, determine the most appropriate category:
fact,idea,decision,technique,requirement,question,insight,problem,solution,action. - Format as structured observations:
- [category] content #tag1 #tag2. - Suggest 2-3 relevant tags per observation (AI-suggested based on context).
Rules:
- Each observation must be atomic (single subject-verb-object).
- Present extractions to the user for confirmation if they are complex.
4. Domain and Theme Detection
Analyze content to detect target domain and themes.
- Segment content by topic shifts (paragraph breaks, explicit markers like "Also...", domain changes).
- Determine if the content should be split into multiple raw notes.
Multi-note detection: if content contains 3 or more distinct topics that are each independently note-worthy (self-contained, useful on their own), offer to split before writing any files.
Present a split preview:
Detected 3 distinct topics — each could be its own raw note:
1. [Topic A] (domain: X, suggested type: concept) → inbox/raw/topic-a-YYYY-MM-DD.md
2. [Topic B] (domain: Y, suggested type: decision) → inbox/raw/topic-b-YYYY-MM-DD.md
3. [Topic C] (domain: X, suggested type: idea) → inbox/raw/topic-c-YYYY-MM-DD.md
Options:
a) Create 3 separate raw notes (recommended)
b) Capture as a single note
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.
- 2mo ago First seen · 90 lines · 32 tokens per session scan A 556581d1bc28
brain-dump is a command published in the GitHub repository superuser-pal/awesome-second-brain (16 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 784 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
onboard
An interview-based setup command for an LLM Wiki, a notes system designed for use with language models. It asks about your wiki, files, working mode, and personal context, then fills in the setup details.
refresh-context
Re-snapshot Core Context.md from (optional) mothership system files + key personal essays. Run when mothership files change, when new essays signal philosophy shift, or when snapshot is older than 30 days.
session-learn
Extract knowledge from the current session. Invoke with /session-learn or "extract this session".
status
Show memex statistics and status including projects, memos, and pending items.
curate
A command for maintaining a file-based knowledge wiki. It can review pages, shorten overlong material, manage page lifecycles, find possible contradictions, and rebuild weak connections between notes.
ingest
A command for adding information to a file-based knowledge wiki from web pages, files, or text notes. It classifies the material, updates or creates wiki pages, and adds links between related pages.