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 skills/stellarshenson/claude-code-plugins/notebooknpx skills add stellarshenson/claude-code-plugins --skill notebookgit clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWhat 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 | $0.00018 | $0.00930 |
| Opus 5 | $0.00009 | $0.00465 |
| Sonnet 5 | $0.00004 | $0.00186 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
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 2d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create New Notebook
Scaffold a properly structured notebook. Uses skills for standards - do NOT duplicate their rules here.
Skills to apply
datascience:notebook-standards- section order, GPU selection, imports, config cell; itsreferences/rich-output.md(colour palette, print patterns) andreferences/equations.md(unicode inline + display math)datascience:progressbars- progress bar style and patterns (if long-running ops)
Read these skills before generating the notebook. They are the source of truth for structure, colors, and patterns.
Steps
-
ASK the user:
- Purpose (what the notebook does - used for header narrative)
- Author initials (default: kj)
- GPU needed? (yes/no) - if yes, GPU selection: pin a specific GPU by UUID (default, reproducible) or auto-pick the freest GPU at runtime? See
notebook-standards/references/gpu-setup.md - Libraries needed (torch, polars, sklearn, transformers, etc.)
- Long-running operations? (yes/no - if yes, ask classic tqdm or modern rich per
progressbarsskill)
-
Determine next notebook number by scanning existing
NN-*.ipynborNN-*.pyfiles. -
Check
pyproject.toml- ensurerichin dependencies. If progress bars needed, ensuretqdmorrichper chosen style. Add if missing. -
Create
<NN>-<initials>-<description>.ipynb(default) or.pyif user prefers. -
Header: brief narrative paragraph (purpose, method, rationale - 2-4 sentences of flowing prose, not bullets). Approach section with numbered phases explaining "what AND why". Output section listing specific artifacts.
-
Structure: follow
notebook-standardsskill exactly - GPU first, imports grouped with autoreload, reproducibility seeds, config cell with sectioned Rich render. Section overview MANDATORY - every## Section Nameheader gets a 1-2 sentence overview (or 3-5 bullets when listy) directly below, BEFORE the first code cell. -
Configuration cell: hyperparameters with inline comments + sectioned Rich render at the END of the same cell. When GPU is enabled, the render MUST include
[bold]Device[/bold]sub-section showingtorch.cuda.get_device_name(0)so the resolved GPU is visible in the output. Seenotebook-standards/SKILL.mdConfiguration template +notebook-standards/references/gpu-setup.mdsection 5.
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.
- 2d ago First seen · 50 lines · 18 tokens per session scan A f7cc50745c24
notebook is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 930 once invoked, about $0.0001 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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