Why does this skill discourage default values in functions?

Default values act as hidden assumptions. By requiring explicit parameters, the skill ensures all experimental variables are visible, configurable, and recorded for the experiment.

Does this skill help with reproducibility?

Yes. It mandates recording random seeds, pinning exact dependency versions, and ensuring the code used to produce a result is saved alongside the output.

When does the scientific-coding skill activate?

The skill activates automatically when Claude detects tasks involving data analysis, simulations, numerical methods, or computations intended for scientific conclusions.

How does 'fail loudly' benefit scientific research?

In science, a wrong result that looks right is catastrophic. Failing loudly ensures that any numerical instability or data error causes an immediate crash rather than silently poisoning results.

Scientific Coding Expert

Name: Scientific Coding Expert
Author: cemde

bycemde

•

Data Science & ML

Implements rigorous principles for research software, data science, and numerical simulations to ensure scientific accuracy and reproducibility.

The Scientific Coding skill transforms Claude into a specialized co-researcher focused on the absolute integrity of experimental results. Unlike general software development which prioritizes user experience, this skill prioritizes scientific truth by enforcing strict standards for data analysis, numerical methods, and simulations. It prevents the subtle, silent bugs that lead to retracted publications by mandating 'loud' failures, explicit parameterization, and immutable data practices. It is ideal for researchers who need their code to serve as a transparent, reproducible record of their methodology.

Key Features

01Mandatory explicit parameterization of all experimental variables

02Data integrity protection via immutable raw data patterns and transformation logging

03Validation of units, coordinate systems, and array shapes using dimensional analysis

04Guaranteed reproducibility through seed management and dependency pinning

05Strict enforcement of 'fail loudly' principles to prevent silent numerical errors

063 GitHub stars

Use Cases

01Implementing statistical models that require precise validation against mathematical reality

02Building data processing pipelines for peer-reviewed academic research

03Developing complex numerical simulations where accuracy is more critical than speed

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add cemde/scientific-coding-skill scientific-coding

For use in Claude.ai and ChatGPT

Key Features

01Mandatory explicit parameterization of all experimental variables

02Data integrity protection via immutable raw data patterns and transformation logging

03Validation of units, coordinate systems, and array shapes using dimensional analysis

04Guaranteed reproducibility through seed management and dependency pinning

05Strict enforcement of 'fail loudly' principles to prevent silent numerical errors

063 GitHub stars

Use Cases

01Implementing statistical models that require precise validation against mathematical reality

02Building data processing pipelines for peer-reviewed academic research

03Developing complex numerical simulations where accuracy is more critical than speed