A set of standards for data-science projects, including notebooks, datasets, machine-learning models, and analysis code.

Use it to apply naming rules, folder conventions, notebook output requirements, data-file handling, and reusable code patterns.

It keeps project files organised and makes notebooks and results easier to reproduce, review, and share.

datascience — Skill for Claude Code by stellarshenson

Install

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.

Any agent

npx skills add stellarshenson/claude-code-plugins --skill datascience

Clone the repo

git clone --depth 1 https://github.com/stellarshenson/claude-code-plugins

Made for: Claude Code.

Or install datascience, the plugin that ships this one along with the rest of its 20 skills, 15 commands.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/datascience/github.svg)](https://agentmods.dev/skills/stellarshenson/claude-code-plugins/datascience)

Your own site

<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/datascience"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/datascience/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/stellarshenson/claude-code-plugins/datascience"><img src="https://agentmods.dev/badge/skills/stellarshenson/claude-code-plugins/datascience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 637 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00057	$0.00637
Opus 5.5	$0.00023	$0.00255
Sonnet 5.5	$0.00011	$0.00127
Haiku 4.5	$0.00006	$0.00064

Measured 1mo ago against content hash 2edc5b2314da, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

datascience 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.

plugins/datascience/skills/datascience/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Science Standards

Conventions for data science projects.

Notebook Naming

Pattern: NN-initials-description.ipynb

Two-digit execution order: 01, 02, 03
Author initials: kj for Konrad Jelen
Brief description: data-exploration, train-yolov8m
Examples: 01-kj-data-exploration.ipynb, 04-kj-train-yolov8m.ipynb

Sequential numbering within groupings. Archive obsolete to @archive/. Never delete. temp_ prefix for temporary notebooks excluded from Git.

File Format

.ipynb = source of truth, committed WITH its outputs. The executed notebook - inline figures, rich renders, tables - is the artefact a reader opens, so the outputs are part of what gets reviewed and shared. Do not gitignore .ipynb, and do not keep the source in a paired Jupytext .py.

Project Structure (cookiecutter-data-science)

data/raw/          # Original immutable datasets (never modify)
data/interim/      # Intermediate transformed data
data/processed/    # Final canonical datasets
data/external/     # Third-party data
notebooks/         # Jupyter notebooks
src/               # Reusable Python modules extracted from notebooks
models/            # Trained model artifacts
reports/           # Generated analysis and figures

PyTorch Model Artifacts

models/<model_name>/
  model.pt        # TorchScript (torch.jit.load, no class needed, for inference)
  checkpoint.pt   # State dict (needs class definition, for retraining)

Folder rolling: current → -1 → -2, up to 5 versions.

Code Standards

Imports: never into __init__.py. Always explicit module imports
Docstrings: Google format, type hints for params and returns
DataFrames: purpose_df for DataFrames, purpose_lf for LazyFrames
Rich output: from rich import print as rprint - the form every notebook-standards template uses
Polars: lazy (pl.LazyFrame + collect()) for large datasets
Prefer builtins: sklearn.model_selection.train_test_split over manual
Plots: matplotlib + seaborn; sizes per purpose in the notebook-standards references/matplotlib.md

Read the full file on GitHub · 60 lines

Changes

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 · 60 lines · 57 tokens per session scan A 2edc5b2314da

Subscribe to this mod's changes

datascience is a skill published in the GitHub repository stellarshenson/claude-code-plugins (4 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 637 once invoked, about $0.0002 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 2026-08-31.

Data and AI

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Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

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google-cloud-solution-agentic-analytics-spark-knowledge-catalog

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google/skills · 132 tokens

training-check

Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.

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nemo-automodel-launcher-config

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

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finetuning

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…

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