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 ludo-technologies/python-best-practices --skill data-sciencegit clone --depth 1 https://github.com/ludo-technologies/python-best-practicesWrote 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/ludo-technologies/python-best-practices/data-science)<a href="https://agentmods.dev/skills/ludo-technologies/python-best-practices/data-science"><img src="https://agentmods.dev/badge/skills/ludo-technologies/python-best-practices/data-science/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/ludo-technologies/python-best-practices/data-science"><img src="https://agentmods.dev/badge/skills/ludo-technologies/python-best-practices/data-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.01543 |
| Opus 5.5 | $0.00039 | $0.00617 |
| Sonnet 5.5 | $0.00020 | $0.00309 |
| Haiku 4.5 | $0.00010 | $0.00154 |
Grade A, and why
data-science 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Data Science
NumPy and pandas best practices for data science and ML code. Designed for AI agents and LLMs to write fast, correct, and reproducible numeric code.
Numeric code has idioms that differ from general Python: loops are the anti-pattern, data structures are immutable buffers, and dtypes are a design decision. Where a rule here overlaps a coding-standards rule, this skill takes precedence for array and DataFrame code.
Categories
Vectorization [CRITICAL]
Keep computation in compiled NumPy/pandas code instead of the Python interpreter.
| Rule | Description |
|---|---|
| vec-no-python-loops | Vectorize instead of Python loops, iterrows, and row-wise apply (10-1000x faster) |
| vec-no-incremental-growth | Build arrays and DataFrames once, not incrementally (O(n) vs O(n²)) |
Indexing & Mutation [CRITICAL]
Write to DataFrames in ways that are correct under Copy-on-Write and visible in the data flow.
| Rule | Description |
|---|---|
| mut-loc-not-chained | Assign with .loc, never with chained indexing |
| mut-no-inplace | Avoid inplace=True |
Data Types [HIGH]
Make dtypes an explicit decision rather than an inference result.
| Rule | Description |
|---|---|
| dtype-explicit-io | Specify dtypes when loading data |
| dtype-categorical | Use category dtype for low-cardinality strings |
| dtype-numpy-explicit | Choose NumPy dtypes explicitly |
Schema Validation [HIGH]
Validate tabular data where it enters the system.
| Rule | Description |
|---|---|
| schema-pandera | Validate DataFrames at boundaries with pandera |
Reproducibility [HIGH]
Make experiments repeatable and free of hidden global state.
| Rule | Description |
|---|---|
| repro-default-rng | Use an explicit Generator for randomness |
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- metadata.json 1.2 KB
- README.md 3.6 KB
- rules/_sections.md 2.4 KB
- rules/_template.md 447 B
- rules/dtype-categorical.md 2.5 KB
- rules/dtype-explicit-io.md 3.2 KB
- rules/dtype-numpy-explicit.md 2.6 KB
- rules/mut-loc-not-chained.md 2.6 KB
- rules/mut-no-inplace.md 2.9 KB
- rules/repro-default-rng.md 3.1 KB
- rules/schema-pandera.md 3.2 KB
- rules/style-method-chaining.md 2.6 KB
- rules/style-named-aggregation.md 2.5 KB
- rules/test-numeric-assertions.md 3.0 KB
- rules/type-array-annotations.md 2.9 KB
- rules/vec-no-incremental-growth.md 2.6 KB
- rules/vec-no-python-loops.md 3.2 KB
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 · 173 lines · 98 tokens per session scan A 9e411100539b
data-science is a skill published in the GitHub repository ludo-technologies/python-best-practices (30 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 1,543 once invoked, about $0.0004 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 2026-09-04.
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