lab-notebook

name	lab-notebook
description	Use after every NSS scan, investigation, or triage. Write lab notebook entry comparing this run to prior runs.

Lab Notebook

Hermes is the lab notebook. The Python pipeline is the instrument; you record what was tried, what differed, and what it means.

When to write

Mandatory after:

• nss-hipif-chain (primary daily cron) / nss-hipif-chain-run.py deterministic chain
• Any nss-bounty-loop tick inside HIPIF depth/hunt subgoals
• Immunefi scan (even if no investigate follows)
• Manual bounty loop, investigate, or run sessions worth keeping

Two locations (both)

1. Hermes memory (cross-session recall)

Use the memory tool to append to MEMORY.md in this profile. Update sections: Active campaigns, Lessons/Gotchas, Open questions.

2. Repo file (versioned, diffable)

Write a dated entry:

data/security_results/lab_notebook/YYYY-MM-DD-<slug-or-scan>.md

Entry template

# Lab entry — YYYY-MM-DD

## Trigger
cron: nss-hipif-chain | nss-bounty-loop | manual | ...

## Scan queue (dry-run top 3)
- slug: grade, scan_grade3_plus, submittable_candidate, analogue

## Investigated
- [slug]: config path, proposals file, campaign_id

## Delegate proposals vs last run
- New templates/params: ...
- Repeated (same as last): ...
- Rejected by validate_hypothesis: ...

## Engine outcome
- Findings: N | max grade: G | novel vs catalogue: ...
- findings_store campaign stats (if available): ...

## Same vs different
Explicit: did we probe differently, or rerun the same assay?

## Night Shift handoff (Day Shift sessions)
- Cron OK to run: ...
- Cron skip / deprioritize: ... (avoid duplicate assays Day Shift already completed)
- Open questions for Kate: ...

## Next action
One concrete step.

## Skill/recon updates
- Gotcha to add: ...
- recon.json change: ...

Compare prior runs

Before writing, read when available:

• Previous lab_notebook/*.md for same slug
• data/security_results/hermes_proposals/ (last 2 JSON files)
• knowledge --campaign <id> --stats
• bounty_scan/latest.json or immunefi_scan/latest.json (scan-only entries)
• loop/state.json (saturated slugs, RSI: cooldown, refinement queue)
• knowledge/improvement_ledger.jsonl (RSI actions)

Gotchas

• Do not skip the notebook because pytest passed with zero findings — "null result" is valuable.
• "Same vs different" must cite evidence (proposal diff or hypothesis_id), not vibes.
• Keep entries under ~80 lines; link to findings.json paths for detail.