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data-and-benchmarks

Defines a four-tier data sourcing policy for fluid dynamics benchmarks, datasets, and reference values.

Updated Sep 13, 2026
Search Tags:#validation#data provenance#datasets#fluid dynamics#citation policy#google colab#benchmark data
One-click install
npx skills add https://github.com/shammun/fluidpy --skill data-and-benchmarks-shammun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-and-benchmarks
Source: https://github.com/shammun/fluidpy/tree/main/.claude/skills/data-and-benchmarks
Command: npx skills add https://github.com/shammun/fluidpy --skill data-and-benchmarks-shammun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Fluid dynamics notebooks, tests, and scripts need trustworthy numbers, but it is easy to fabricate benchmark values, cite sources from memory, or embed unverifiable data. This Skill enforces a strict sourcing hierarchy so every reference value is reproducible, cited, and accessible both locally and on Google Colab. ## Core Features & Use Cases - Four-tier data policy: analytic/synthetic generation first, then cited published benchmarks, then free online datasets, then manual downloads staged in Google Drive. - Citation and provenance rules: benchmark tables live in reference/chNN/ with a SOURCES.md recording DOI, retrieval method, and verification date; scalar constants like Blasius f''(0) carry citations in docstrings. - Colab-aware loading: a single loader resolves reference files, cached downloads, manual data folders, and Drive mounts with clear error instructions. - Use Case: When writing a lid-driven cavity validation test, generate the Ghia et al. (1982) centreline profile as a cited CSV in reference/ch10/ rather than typing numbers from memory, and record the source in SOURCES.md. ## Quick Start Apply the data-and-benchmarks policy to decide where the reference values for my chapter 10 cavity-flow test should come from and how to cite them.

Frequently Asked Questions about data-and-benchmarks

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get benchmark data for fluid dynamics validation tests?▼

Follow a tier order: generate analytic or synthetic data first, then use digitised published benchmarks stored as CSV with a SOURCES.md citation table. Only fall back to online datasets or manual downloads when earlier tiers cannot satisfy the need.

How do I cite a published benchmark like Ghia et al. lid-driven cavity data?▼

Store the values as a plain CSV in reference/chNN/ and add one row per value to SOURCES.md with the source, DOI or URL, how it was obtained, and the verification date. Never quote benchmark values from memory.

Can I access datasets in Google Colab without committing them to git?▼

Yes. Mount Google Drive in the first Colab cell and place manually downloaded files under MyDrive/fluidpy/data/manual/chNN/. The loader resolves reference files, cached downloads, manual folders, and Drive paths with a clear error if nothing is found.

Why does my implementation miss a textbook number by 1-2 percent?▼

A fluid property mismatch is a common cause. Use one consistent property source, such as a small table in thermo.py or CoolProp, and state which property set was used in the report so discrepancies are traceable.

Can I include tables copied from the textbook in my repository?▼

No. Book-derived values are private evidence: keep them in git-ignored tests/book_values_chNN.json, guard tests with skipif, and report only derived statements. Never commit book tables to reference folders, notebooks, or published HTML.

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