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 agentmods add skills/stellarshenson/claude-code-plugins/datasciencenpx skills add stellarshenson/claude-code-plugins --skill datasciencegit clone --depth 1 https://github.com/stellarshenson/claude-code-pluginsWhat 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 | $0.00057 | $0.00637 |
| Opus 5 | $0.00028 | $0.00318 |
| Sonnet 5 | $0.00011 | $0.00127 |
| Haiku 4.5 | $0.00006 | $0.00064 |
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 2d 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 — 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:
kjfor 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_dffor DataFrames,purpose_lffor 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_splitover manual - Plots: matplotlib + seaborn; sizes per purpose in the notebook-standards
references/matplotlib.md
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.
- 2d ago First seen · 60 lines · 57 tokens per session scan A 2edc5b2314da
datascience is a skill published in the GitHub repository stellarshenson/claude-code-plugins (3 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 637 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.