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 diillson/chatcli --skill jupytergit clone --depth 1 https://github.com/diillson/chatcliWrote 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/diillson/chatcli/jupyter)<a href="https://agentmods.dev/skills/diillson/chatcli/jupyter"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/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/diillson/chatcli/jupyter"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/jupyter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 52 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00065 | $0.00496 |
| Opus 5.5 | $0.00026 | $0.00198 |
| Sonnet 5.5 | $0.00013 | $0.00099 |
| Haiku 4.5 | $0.00006 | $0.00050 |
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 1mo 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 / Data Science
Local, keyless notebook execution. Detect: command -v jupyter python3 /
Get-Command jupyter, python -ErrorAction SilentlyContinue.
Install: pipx install jupyter or pip install jupyterlab nbconvert ipykernel.
Execute a notebook headlessly
jupyter nbconvert --to notebook --execute --inplace analysis.ipynb
jupyter nbconvert --to html --execute report.ipynb # produce a shareable HTML
Read results with @read on the output, or inspect the executed .ipynb cells.
Run ad-hoc Python (no notebook)
For quick analysis, write a .py and run it via @coder exec python3 script.py — manage deps
in a venv: python3 -m venv .venv && . .venv/bin/activate && pip install pandas matplotlib.
Convert
jupyter nbconvert --to script notebook.ipynb # → .py
jupyter nbconvert --to pdf notebook.ipynb # needs LaTeX (see paper-writing skill)
Live kernel (interactive sessions)
For a persistent kernel across steps, start jupyter console or use jupyter run. Keep the
kernel alive only as long as needed; one-shot --execute is simpler for batch work.
Rules
- Use a project venv; don't pollute the system Python.
- For plots, save to a file (
plt.savefig) and report the path — terminals can't show inline plots. - State which packages you installed; never assume pandas/numpy are present without checking.
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.
- 1mo ago First seen · 53 lines · 65 tokens per session scan A f1d00d000429
jupyter is a skill published in the GitHub repository diillson/chatcli (94 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 496 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 2026-09-03.
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