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/mxslr/mlcraft/notebook-deliverynpx skills add mxslr/mlcraft --skill notebook-deliverygit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/notebook-delivery)<a href="https://agentmods.dev/skills/mxslr/mlcraft/notebook-delivery"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/notebook-delivery.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 | $0.00135 | $0.00692 |
| Opus 5 | $0.00068 | $0.00346 |
| Sonnet 5 | $0.00027 | $0.00138 |
| Haiku 4.5 | $0.00014 | $0.00069 |
Grade A, and why
notebook-delivery 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 4d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Notebook and Environment Delivery
Before writing a deliverable, decide WHERE it will run, then adapt to it. If the user has not said, ask which of the three targets they want.
Detect the target environment
| Target | Signal from user | What to adapt |
|---|---|---|
| Kaggle | "kaggle", "commit", "save version", dataset already on Kaggle | Paths under /kaggle/input and /kaggle/working; note the weekly GPU quota and 12h commit limit; use Save and Run All (Commit) for long runs; preprocess once and reuse. |
| Google Colab | "colab", "google colab", Drive | Mount Google Drive; read data and write checkpoints to Drive so a disconnect does not lose progress; note free-tier idle timeouts. |
| Local GPU (VSCode) | "local gpu", "my laptop", "vscode", "run offline" | Require an NVIDIA CUDA GPU; size resolution and batch to the local VRAM (usually far less than 16 GB); read data from a local folder path the user sets. |
Make the notebook portable when possible: auto-detect the environment and set the working directory and data path from it, so the same file runs in more than one place.
Environment-specific cautions to state plainly
- Kaggle: quota is total hours per week and resets weekly. A run must fit the budget; measure seconds per epoch on a quick test first. Do not run long jobs in an interactive session that dies when the tab closes; use a Commit.
- Colab: free GPU disconnects on idle; always checkpoint to Drive and support resume.
- Local: CPU-only cannot train these models in reasonable time. If no CUDA GPU, say so and recommend Kaggle or Colab.
Writing style for generated notebooks (required)
- Keep explanations short and simple. One or two sentences per markdown cell is usually enough.
- Write in a professional research tone, like a methods section: state what and why, not chit-chat.
- Do not use em-dash characters. Use a normal hyphen, a comma, or a new sentence instead.
- Do not use emoji anywhere in the notebook, comments, or markdown.
- Prefer plain, precise wording. Avoid exclamation marks and filler.
- Every claim about a result must come from an actual run, not an assumption.
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.
- 4d ago First seen · 35 lines · 135 tokens per session scan A eb5484f7a468
notebook-delivery is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 692 once invoked, about $0.0007 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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