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_api_introgit 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_api_intro)<a href="https://agentmods.dev/skills/causify-ai/helpers/notebook.create_api_intro"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.create_api_intro.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.01657 |
| Opus 5 | $0.00005 | $0.00829 |
| Sonnet 5 | $0.00002 | $0.00331 |
| Haiku 4.5 | $0.00001 | $0.00166 |
Grade A, and why
notebook.create_api_intro 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 3d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal
-
Create a self-contained Jupyter notebook that teaches the Python package
<PACKAGE_NAME>by progressively introducing its core primitives, mental model, and API surface -
The notebook should be optimized for learning the library itself, not for solving a large real-world problem
Teaching Philosophy
- Start from the smallest possible working example
- Introduce one new concept at a time
- Use the minimum amount of code necessary to demonstrate each concept
- Prefer toy examples with 2–5 objects instead of realistic datasets
- Every code cell should answer exactly one question
- Avoid helper functions, abstractions, and boilerplate unless they are part of the library's API
- Focus on understanding:
- What are the primitive objects?
- How are they created?
- How do they interact?
- What methods are available?
- What state do they hold?
- How do they compose into larger structures?
Notebook Structure
Name of the Notebook
- The name of the notebook
<FILE>is either specified directly by the user or it is generated as:tutorials/<PACKAGE_NAME>/<PACKAGE_NAME>.<ID>.API.<description>.ipynb - E.g., for the package
pgmpyand for probabilistic inference the name can betutorials/pgmpy/pgmpy.01.API.probabilistic_inference.ipynb
Use Standard Template Structure
-
Use the structure from
.claude/templates/notebook.template.pyfor consistent notebook initialization -
First Cell: Include autoreload, logging, and core dependencies
-
Second Cell: Optionally install packages on-the-fly
-
Third Cell: Notebook-specific imports and logger
Follow General Notebook Conventions
- Follow the notebook conventions documented in
.claude/skills/notebook.rules.md:# Setup and Initialization: Standard template structure and Python code rules# Code Cell Design and Content: Python coding style, showing results, and using pandas dataframes for tables# Text and Markdown Formatting: Markdown bullet points, emdash replacement, and LaTeX notation# Data Processing and Visualization: Data manipulation and plotting conventions## Visualization Cell Triplet Details: Structure for notebook cells with visualizations or interactive widgets
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.
- 3d ago First seen · 224 lines · 10 tokens per session scan A adf034d54967
notebook.create_api_intro is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed yesterday), licensed Apache-2.0. It adds 10 tokens to every session and 1,657 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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