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/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_api_intro"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.create_api_intro.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 | $0.00010 | $0.01710 |
| Opus 5.5 | $0.00004 | $0.00684 |
| Sonnet 5.5 | $0.00002 | $0.00342 |
| Haiku 4.5 | $0.00001 | $0.00171 |
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 22d 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 — 220 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
-
Follow this 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- Follow the cell layout in
.claude/skills/notebook.rules.mdunder## Use Standard Template Structure: title cell with scope and pedagogical arc, optional pip cell, autoreload and third-party imports cell, and a last setup cell that callsutils.init_loggers(_LOG) - Do not add an
## Importsheader
- Follow the cell layout in
Follow General Notebook Conventions
- Follow the notebook conventions documented in
.claude/skills/notebook.rules.md:# Design Principles and Setup: Standard template structure and Python code rules# 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# Visualization and Interactivity: 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.
- 22d ago Changed · +2 lines 554235550257
- 1mo ago Changed · -6 lines 4f9a824d614d
- 1mo 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 (152 stars, last pushed yesterday), licensed Apache-2.0. It adds 10 tokens to every session and 1,710 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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