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/1-1.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/1-1)<a href="https://agentmods.dev/commands/superuser-pal/awesome-second-brain/1-1"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/1-1/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/1-1"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/1-1.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 | $0.00000 | $0.00575 |
| Opus 5.5 | $0.00000 | $0.00230 |
| Sonnet 5.5 | $0.00000 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00057 |
Grade A, and why
1-1 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture 1:1 Meeting
Take a meeting transcript, notes, or Gemini summary and create a structured vault note with key takeaways, quotes, action items, and DM context.
Usage
/1-1 <participant>
User will paste the transcript/notes. Example: /1-1 <name>
Workflow
-
Parse the input — handle Gemini transcripts, raw notes, or meeting summaries.
-
Manage person-specific note at
work/02_1-1/<Participant>.md:- If the file does not exist, create it using this structure:
--- date: "<current date>" description: "<one-line summary from most recent meeting>" tags: - 1on1 - work-note status: active --- # 1:1 Meetings — <Participant> ## Latest Summary <One-line summary of key topics discussed in the most recent meeting> ## Meetings ### <YYYY-MM-DD> [New Meeting Content] --- - If the file already exists:
- Update the
descriptionin the YAML frontmatter and the## Latest Summarysection with the summary of this new meeting. - Prepend the new meeting content as a heading
### <YYYY-MM-DD>at the top of the## Meetingssection (reverse-chronological order).
- Update the
- If the file does not exist, create it using this structure:
-
Structure the meeting entry with these sections:
- Key Takeaways — bullet points of important items
- Decisions Made — agreed items
- Action Items — checkboxes with owners
- Quotes Worth Noting — direct quotes (blockquotes)
- What Went Well — resonators
- What to Watch — concerns, ambiguous signals
- Context From DMs — check relevant DMs for color
- Related — wikilinks to relevant notes
-
Update related notes:
- Person's section in
work/06_ORG/PEOPLE.md(add or update context) work/INDEX.md(add to Review Prep or Projects)brain/MEMORIES.mdif any context changed
- Person's section in
-
Check for stale context — if the meeting reveals something that contradicts existing vault notes, flag and update.
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 · 69 lines · 0 tokens per session scan A fe8a7e5fb8f1
1-1 is a command published in the GitHub repository superuser-pal/awesome-second-brain (16 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 575 tokens. 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
ingest
Ingest a source (URL/file/text) into Raw Sources + compile 1015 Wiki pages, with mandatory user-purpose gate and mothership cross-linking.
lint
Run comprehensive wiki health check — orphans, broken links, contradictions, stale pages, index sync, MOC coverage, v2/v4/v5 frontmatter coverage, Core Context freshness, and cross-vault link integrity (mainVaultRelated/mainVaultCmds).
capture-tabs
Capture a Chrome research tab group or AI-chat research session into LLM Wiki Inbox markdown, then optionally route it through /inbox or /ingest.
wiki-lint
Run a health check on the wiki. Invoke with /wiki-lint or "lint the wiki".
okf
A command that exports a private knowledge wiki into an OKF-compatible bundle. OKF is a format for packaging knowledge, with a separate guarded mode for preparing material to share externally.
pdf-to-wiki
A workflow for sending a large PDF from Google Drive to NotebookLM, Google's document-analysis tool, and saving the resulting notes as Obsidian markdown files. It passes the file link rather than reading the PDF directly.