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 causify-ai/helpers --skill notebook.create_outlinegit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/causify-ai/helpers/notebook.create_outline)<a href="https://agentmods.dev/skills/causify-ai/helpers/notebook.create_outline"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.create_outline/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/causify-ai/helpers/notebook.create_outline"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.create_outline.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 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00012 | $0.01580 |
| Opus 5.5 | $0.00005 | $0.00632 |
| Sonnet 5.5 | $0.00002 | $0.00316 |
| Haiku 4.5 | $0.00001 | $0.00158 |
Grade A, and why
notebook.create_outline 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 15d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
- Create a comprehensive outline for an interactive Jupyter notebook that teaches a concept (passed from the user) through visualization and hands-on exploration
- The outline is a markdown file that:
- Describes what each cell in the the notebook will contain without
- Writing any code, serving as a blueprint for implementation
Key Principles
- Make sure to follow the section
# Design Principles and Setupfrom the file.claude/skills/notebook.rules.md - The output is a
notebook_outline.<TAG>.mdmarkdown file that describes the notebook units - Save markdown file in the current dir
- Do not write any code
Outline Unit Structure
- Each unit in the outline corresponds to a triplet of cells in the final notebook:
- Markdown cell: Section header, goal, and description of the panels and controls (before viz)
- Code cell: Visualization, widgets, and interactive controls
- Markdown cell: Guided usage, actions plus observations, then the implementation (after viz)
Numbering and Naming
- Number units incrementally:
- E.g.,
Cell 1,Cell 2, etc
- E.g.,
- Use descriptive titles that signal the learning objective
- Not just "Plot" or "Widget"
- Keep titles concise (5-7 words)
Outline Unit Description Template
-
Each outline unit describes the full visualization triplet
- 3 notebook cells: pre-visualization markdown, code, and post-visualization markdown
-
Use this structure for each unit:
## Cell i: <Concise Learning Objective> **Goal** - <Learning objective 1> - <Learning objective 2> **Plots and their descriptions** - `<Plot 1 name>`: <Description of what it shows> - `<Plot 2 name>`: <Description of what it shows> - `Comments`: Current parameter values and state observations **Widgets** (if applicable) - `<widget name>`: <description, range, effect on display> - Each widget description is close to the widget itself **Guided usage** (post-visualization) - <Action on a widget, plus the observation it produces> - <Action on a widget, plus the observation it produces> **Implementation** Libraries and functions used
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.
- 15d ago Changed · +5 lines d50209a4e6f9
- 1mo ago Changed · +10 lines e578fae6e287
- 1mo ago Changed · -6 tokens per session eead90cd0568
- 1mo ago First seen · 176 lines · 18 tokens per session scan A fc3dfbd78d43
notebook.create_outline is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 1,580 once invoked, about $0.0000 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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