Borrowing it
Nothing to install: this file belongs to causify-ai/helpers. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/causify-ai/helpers/master/.claude/skills/notebook.brainstorm_outline/SKILL.mdgit 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.brainstorm_outline)<a href="https://agentmods.dev/skills/causify-ai/helpers/notebook.brainstorm_outline"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.brainstorm_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.brainstorm_outline"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.brainstorm_outline.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 | $0.00014 | $0.00646 |
| Opus 5.5 | $0.00006 | $0.00258 |
| Sonnet 5.5 | $0.00003 | $0.00129 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
notebook.brainstorm_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 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.
How it starts
The opening of the file, as written. The whole thing — 97 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: not, and, or, implies, iff) - 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 and Cold) or Sunny` - Generate and display truth tables for arbitrary formulas - Test entailment between two formulas: does KB entail alpha? - 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.
- 1mo ago First seen · 97 lines · 14 tokens per session scan A f7a8978c8858
notebook.brainstorm_outline is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 646 once invoked, about $0.0001 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 2026-09-13.
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