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praxis

Convert raw lab data into publication-ready figures and analyses.

7|1|Updated Mar 17, 2026
Search Tags:#workflow#data-analysis#plotting#slash-commands#praxis#journal-styles
One-click install
npx skills add https://github.com/zmtsikriteas/praxis --skill praxis-zmtsikriteas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: praxis
Source: https://github.com/zmtsikriteas/praxis/tree/main
Command: npx skills add https://github.com/zmtsikriteas/praxis --skill praxis-zmtsikriteas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Praxis provides a unified toolkit to transform raw laboratory data into publication-ready figures and analyses, reducing manual plotting and repetitive processing.

Core Features & Use Cases

  • High-level data loading and technique-aware analysis across XRD, DSC/TGA, EIS, FTIR/Raman, XPS, SEM/EDS, AFM, and more.
  • Journal-style plotting and reproducible exports with metadata for traceability.
  • Use Case: A researcher loads a DSC scan, analyzes Tg/Tm/Tc, and exports a Nature-ready figure in seconds.

Quick Start

Load a dataset, run analyses, and export a publication-ready figure in one seamless workflow.

Frequently Asked Questions about praxis

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

FAQPage Schema
How do I turn raw XRD or DSC data into publication-ready figures?▼

Publication-ready figures are generated by loading raw laboratory data into an automated pipeline that applies technique-aware analysis and journal-styled plotting for reproducible exports.

What's the best way to automate lab data analysis for multiple characterization techniques?▼

Automating lab data analysis across multiple techniques is achieved through a unified Python toolkit that handles XRD, DSC/TGA, EIS, FTIR, Raman, XPS, SEM/EDS, and AFM within a single non-destructive workflow.

Can I use Python with numpy and pandas to generate journal-style plots from spectroscopy data?▼

Yes, you can generate journal-style plots from spectroscopy data using a Python 3.10+ environment built on numpy, pandas, matplotlib, and scipy to ensure reproducible, publication-ready figure exports.

Does this data analysis workflow support reproducible metadata exports for academic publishing?▼

Reproducible metadata exports for academic publishing are supported natively, ensuring that every journal-styled figure generated from raw lab data retains full traceability and non-destructive processing records.

Do I need Python 3.10+ to run reproducible plotting workflows for TGA and EIS data?▼

Yes, Python 3.10+ is required to run these reproducible plotting workflows, as the underlying Praxis modules depend on modern numpy, pandas, matplotlib, and scipy environments to process TGA and EIS datasets.

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