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.fix_logginggit 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.fix_logging)<a href="https://agentmods.dev/skills/causify-ai/helpers/notebook.fix_logging"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.fix_logging/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.fix_logging"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/notebook.fix_logging.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 | $0.00450 |
| Opus 5.5 | $0.00004 | $0.00180 |
| Sonnet 5.5 | $0.00002 | $0.00090 |
| Haiku 4.5 | $0.00001 | $0.00045 |
Grade A, and why
notebook.fix_logging 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.
What it actually says
Goal
-
Implement the instructions in
.claude/skills/notebook.rules.mdunder## Use Standard Template Structure -
Use the structure from
.claude/templates/notebook.template.pyfor consistent notebook initialization -
Use
display -
In the local utility
*_utils.pythere should be a function likeimport helpers.hnotebook as hnotebo def init_loggers(notebook_log: logging.Logger) -> None: """ Wire the notebook logger into the utils logger. :param notebook_log: logger owned by the notebook """ hnotebo.init_loggers(notebook_log, utils_log=_LOG)hnotebo.init_loggers()already callsconfig_notebook(), initializeshdbgat INFO level, and redirects the notebook and utils loggers to print- If the notebook needs a third-party logger (e.g.,
causalml), redirect it insideinit_loggers()withhnotebo.set_logger_to_print():hnotebo.set_logger_to_print(logging.getLogger("causalml"))
-
In the notebook, the last setup cell calls
utils.init_loggers(_LOG)instead oflogging.basicConfig()orhdbg.init_logger()
Workflow
Sync Notebook
- At the end, sync the paired
.pyfile with Jupytext following the conventions in# Code Architecture and Responsibility->## Utilities vs. Notebook Responsibilitiesin.claude/skills/notebook.rules.md
Conventions
- Always follow the conventions and guidelines in
.claude/skills/notebook.rules.md
Verification
- Confirm the notebook init cell matches
.claude/templates/notebook.template.py - Confirm
utils.init_loggers()is called and log messages print in the notebook - Confirm the
.ipynband paired.pyfile are in sync via Jupytext
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 · +7 lines 6e93582c7b00
- 1mo ago Changed · +7 lines a9004781914f
- 1mo ago First seen · 41 lines · 10 tokens per session scan A ac83baf4ddac
notebook.fix_logging is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 10 tokens to every session and 450 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.
Other skills, from other repositories
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.
python
Python development with ruff, mypy, pytest - TDD and type safety.