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 Zaoqu-Liu/ScienceClaw --skill prismer-jupytergit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote 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/zaoqu-liu/scienceclaw/prismer-jupyter)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/prismer-jupyter"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/prismer-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/zaoqu-liu/scienceclaw/prismer-jupyter"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/prismer-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 | $0.00023 | $0.00451 |
| Opus 5 | $0.00012 | $0.00226 |
| Sonnet 5 | $0.00005 | $0.00090 |
| Haiku 4.5 | $0.00002 | $0.00045 |
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 7d 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 Skill
Description
Create and execute Jupyter notebooks for interactive data analysis and visualization.
Tools Used
jupyter_execute- Execute Python code in Jupyter kernel (auto-switches to Jupyter)jupyter_notebook- Create, read, update, delete, and list notebooksupdate_notebook- Add or update cells in the notebook without executingupdate_gallery- Display generated plots and visualizations in gallery viewupdate_data_grid- Display structured tabular data (DataFrames, query results) in AG Gridupdate_code- Show code examples and scripts in the Code Playgroundsave_artifact- Save generated artifacts (plots, data files) to workspace collection
Capabilities
- Create new notebooks with proper structure
- Add and execute code cells
- Add markdown documentation cells
- Display inline visualizations
- Display tabular data in interactive grid view
- Show code examples with syntax highlighting
- Export to various formats (HTML, PDF)
Usage Patterns
Create Analysis Notebook
When user says: "Create a notebook for [analysis]"
- Create notebook with title and imports
- Add data loading cell
- Add exploration cells
- Structure with markdown headers
- Execute cells sequentially
Execute and Debug
When user says: "Run this code"
- Execute cell
- Capture output and errors
- If error, diagnose and fix
- Show results or visualizations
Document Workflow
When user says: "Add explanation for this step"
- Add markdown cell before code
- Explain methodology
- Note assumptions and limitations
Best Practices
- Cell Independence: Each cell should run independently when possible
- Import First: All imports at notebook start
- Clear Outputs: Clean outputs before sharing
- Markdown Structure: Use headers for navigation
- Save Often: Checkpoint regularly
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.
- 7d ago First seen · 60 lines · 23 tokens per session scan A b4e445c691e2
jupyter is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 451 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 2026-09-03.
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