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 CHENyiru3/AI-Skills-Collections --skill jupytergit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/jupyter)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/jupyter"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/jupyter/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/chenyiru3/ai-skills-collections/jupyter"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/jupyter.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 | $0.00070 | $0.00407 |
| Opus 5.5 | $0.00028 | $0.00163 |
| Sonnet 5.5 | $0.00014 | $0.00081 |
| Haiku 4.5 | $0.00007 | $0.00041 |
Grade A, and why
jupyter 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 6d 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
Jupyter Notebook Guide
Notebooks are JSON files. Cells are in nb['cells'], and each cell has source (a list of strings) and cell_type (code, markdown, or raw).
Modifying Notebooks
import json
with open("notebook.ipynb") as f:
nb = json.load(f)
# Modify nb["cells"][i]["source"], then:
with open("notebook.ipynb", "w") as f:
json.dump(nb, f, indent=1)
Executing and Converting
jupyter nbconvert --to notebook --execute --inplace notebook.ipynb
jupyter nbconvert --to html notebook.ipynb
jupyter nbconvert --to script notebook.ipynb
jupyter nbconvert --to markdown notebook.ipynb
Finding Code
grep -n "search_term" notebook.ipynb
Cell Structure
# Code cell
{"cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": ["code\n"]}
# Markdown cell
{"cell_type": "markdown", "metadata": {}, "source": ["# Title\n"]}
Clear Outputs
for cell in nb["cells"]:
if cell["cell_type"] == "code":
cell["outputs"] = []
cell["execution_count"] = None
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.
- 6d ago First seen · 60 lines · 70 tokens per session scan A 5e17ca97b2e1
jupyter is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 70 tokens to every session and 407 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-02.
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