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.outline_ideasgit 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.outline_ideas)<a href="https://agentmods.dev/skills/causify-ai/helpers/notebook.outline_ideas"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.outline_ideas/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.outline_ideas"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.outline_ideas.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 | $0.00014 | $0.00659 |
| Opus 5 | $0.00007 | $0.00329 |
| Sonnet 5 | $0.00003 | $0.00132 |
| Haiku 4.5 | $0.00001 | $0.00066 |
Grade A, and why
notebook.outline_ideas 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 5d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
-
Given some technical material provided from the user, come up with 5 ideas of interactive Jupyter notebooks that teaches the concepts in the materials using
- Visualization
- Interaction
- Exploration
-
When possible suggest and use "famous" examples, data, experiments, and problems related to the provided material
-
The output is a file
notebook_ideas.<tag>.mdmarkdown file that describe the ideas
Template
- For each ideas use a template like
## 1. <Title> ### Goal Students gain intuitive understanding of ... by building and analyzing ... and exploring the relationship between ... ### Learning Objectives - Understand ... - Visualize ... ### Core Concepts - ... ### Key Packages - **package**: ... (Do not cite the standard packages like pandas, scipy, numpy, matplotlib) ### Learning Activities - Build ... - Generate ... - Test ... - Explore ... - Measure ... - Interactive ...
Example
- For a query like "Explain propositional logic":
## 1. Interactive Logic Explorer ### Goal - Students will: - Gain intuitive understanding of propositional logic by building and analyzing logical formulas, truth tables, and inference rules - Explore the relationship between syntax, semantics, and computation. ### Learning Objectives - Construct propositional formulas and evaluate truth values - Enumerate all models and check entailment - Understand SAT solving and computational complexity - Visualize how expressiveness and tractability trade off ### Core Concepts - Propositional logic syntax (operators: ¬, ∧, ∨, ⟹, ⟺) - Semantics via truth tables and model interpretation - Inference rules (Modus Ponens, Modus Tollens, Resolution) - Model checking algorithm (sound and complete) - Satisfiability and NP-completeness ### Key Packages - **sympy** — symbolic logic, propositional formula manipulation - **python-sat** — SAT solver backends ### Learning Activities - Build formulas interactively: `(Rain ∧ Cold) ∨ Sunny` - Generate and display truth tables for arbitrary formulas - Test entailment between two formulas: does KB ⊨ α? - Explore inference rules (modus ponens, resolution) - Measure SAT solver complexity as # variables increases - Interactive "Wumpus World" knowledge base reasoning
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.
- 5d ago First seen · 99 lines · 14 tokens per session scan A 98675fb26518
notebook.outline_ideas is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 659 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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