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 skills add uchicago-dsi/ai-sci-skills --skill lab-notebookgit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-skillsWrote 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/skills/uchicago-dsi/ai-sci-skills/lab-notebook)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/lab-notebook"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/lab-notebook/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/skills/uchicago-dsi/ai-sci-skills/lab-notebook"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/lab-notebook.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 | $0.00051 | $0.00665 |
| Opus 5 | $0.00026 | $0.00332 |
| Sonnet 5 | $0.00010 | $0.00133 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
lab-notebook 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 9d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lab Notebook
Optimize For Re-entry And Auditability
- A notebook should answer both:
- what exactly happened?
- what do we currently believe?
- Prefer one low-friction chronological record plus a sparse current-summary layer.
- Reuse the project's existing notebook pattern instead of inventing a new structure.
Choose The Notebook Shape
- If the project already uses one file with summary plus chronology, keep that shape.
- If the project already uses a detailed campaign notebook plus a sparse top-level lab notebook, keep that split.
- Do not create two equally detailed notebooks.
- Default rule:
- detailed layer: append literal work record
- summary layer: update only when the decision or current understanding changes
Use This Entry Contract
For each meaningful experiment, debugging step, or job intervention, capture:
- Question or goal.
- Action taken.
- Evidence:
- commands, scripts, configs, paths, job IDs, artifacts, metrics.
- Result:
- what actually happened.
- Conclusion:
- the inference you are drawing, if any.
- Inference confidence:
low,medium, orhighwhen the entry is making a real inference.
- Decision impact:
- what changed in current understanding, or what the next step is.
Update The Summary Sparingly
- Update the summary layer only when one of these changes:
- current best explanation
- current best branch or baseline
- settled negative results
- next discriminative check
- success criterion or decision rule
- Do not rewrite the summary after every routine action.
- The summary should be short enough that a fresh agent can re-enter from it.
- If multiple live explanations matter, keep a tiny hypothesis registry in the summary layer:
- hypothesis
- strongest support
- strongest weakening evidence
- next falsifier
Make Entries Useful
- Include exact evidence paths and identifiers.
- Include representative visual artifact paths when a plot, overlay, slice, curve, or diff view materially affected the decision.
- Record negative results explicitly when they rule out a family of ideas.
- If the entry draws an inference, state it explicitly instead of leaving it implicit in the next step.
- Keep inference confidence coarse:
low,medium, orhigh. - Do not add confidence to entries that are only reporting raw observations or routine actions.
- Preserve chronology so later readers can reconstruct what happened.
- End each entry with "so what?" in concrete form.
- If nothing changed in understanding, say that explicitly.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 80 lines · 51 tokens per session scan A 1d087746b921
lab-notebook is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (17 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 665 once invoked, about $0.0003 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.
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