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/superuser-pal/awesome-second-brain/incidentgit 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/incident)<a href="https://agentmods.dev/commands/superuser-pal/awesome-second-brain/incident"><img src="https://agentmods.dev/badge/commands/superuser-pal/awesome-second-brain/incident.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 | $0.00000 | $0.00455 |
| Opus 5 | $0.00000 | $0.00228 |
| Sonnet 5 | $0.00000 | $0.00091 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
incident 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 5d 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
Incident Capture
Capture an incident from raw text transcripts, meeting notes, emails, or Slack URLs into structured vault notes. Produces a complete incident work note with timeline, people, analysis, and wins doc entry.
Usage
/incident <slack-urls> OR <raw-text-transcript>
Provide Slack URLs (channels, threads, DMs) or paste a raw transcript/notes summary.
Workflow
1. Gather Raw Data
- If Input is URL: Use
slack-archaeologistandcontact-importersubagents to read channels/threads, fetch profiles, and build the initial timeline. - If Input is Text: AI parses the provided content directly to extract the timeline, events, and personnel involved.
2. Identify People
For every person involved:
- Check if they have a section in
work/06_ORG/PEOPLE.md. - Note their role, team, and contribution to the incident.
- If not present and input was Slack, fetch profile; otherwise, use context from text.
3. Create the Work Note
Create work/03_INCIDENTS/<Incident Name>.md using the templates/Incident Note.md structure. Ensure high-fidelity extraction of:
- Root Cause: What went wrong and why.
- Resolution: How it was fixed.
- Timeline: A precise table of events.
- Impact: Business and user impact.
4. Update References & People
- Update key personnel sections in
work/06_ORG/PEOPLE.mdwith incident context. - Update
brain/MEMORIES.md,brain/RULES.md, andbrain/CAVEATS.mdif new lessons emerged. - Add to
work/05_REVIEW/WINS.mdif applicable.
Important
- Timeline Precision: If timestamps are missing in raw text, order events logically based on the narrative.
- Blamelessness: Keep public-facing logic blameless; use private sections for honest analysis if needed.
- Wikilinks: Ensure all people and projects are correctly wikilinked.
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.
- 5d ago First seen · 46 lines · 0 tokens per session scan A a538e8479f02
incident is a command published in the GitHub repository superuser-pal/awesome-second-brain (14 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 455 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
canvas
AI-orchestrated visual canvas production — create, populate, layout, present, generate, and export Obsidian canvases.
ingest
Ingest a source (URL/file/text) into Raw Sources + compile 1015 Wiki pages, with mandatory user-purpose gate and mothership cross-linking.
audit
Audit the whole Wiki vault against 3 knowledge-integrity criteria — eligibility coverage, MOC-cluster consistency, confidence calibration. Produces a vault health report and queues high-priority pages for /verify.
verify
Verify a single Wiki page against 3 knowledge-integrity criteria — eligibility, consistency, confirmability. Writes verificationStatus back to the page; flags conflicts as disputed rather than deleting them.
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).
onboard
Interview-based first-run setup for this LLM Wiki kit. Asks the essential questions (vault location/name, Mode A/B, mothership path, Core Context identity + reuse axes), then fills every placeholder and writes Core Context so the wiki knows you from day one. Activate when the user says "온보딩해줘", "처음 시작할게", "처음 시작"…