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Rukkha1024
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data-context

Analyze step vs. non-step recovery strategies with LMM and FDR correction.

Updated Aug 27, 2026
Search Tags:#data analysis#statistical modeling#research context#step vs non-step#perturbation#EMG analysis
One-click install
npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill data-context-rukkha1024
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-context
Source: https://github.com/Rukkha1024/muscle_synergy_analysis/tree/main/.agents/skills/data-context
Command: npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill data-context-rukkha1024

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a standardized context for analyzing data related to the "platform translation perturbation" experiment, ensuring that all analyses align with the specific goals and methodologies outlined in the associated research paper, particularly concerning the comparison of step vs. non-step recovery strategies.

Core Features & Use Cases

  • Consistent Analysis Framework: Ensures that code and analysis adhere to the paper's defined comparison units (subject-velocity-trial), statistical modeling (LMM with specific fixed/random effects), and multiple comparison correction methods (FDR).
  • Data Semantics Preservation: Guides users to reuse established data structures and naming conventions (e.g., onset-aligned-merged-parquet) to avoid reinventing data handling.
  • Use Case: When developing a new analysis script for comparing EMG signals between step and non-step trials under identical perturbation conditions, this Skill ensures the script defaults to using raw trial data, appropriate statistical models, and correct data semantics, preventing deviations from the paper's established methodology.

Quick Start

Use the data-context skill to set up a new analysis script for comparing EMG signals between step and non-step trials.

Frequently Asked Questions about data-context

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

FAQPage Schema
How do I align EMG analysis with statistical modeling for platform translation perturbation experiments?▼

To align EMG analysis with statistical modeling for platform translation perturbation experiments, use a standardized context enforcing Linear Mixed Models (LMM) with REML estimation and FDR correction. This ensures your script adheres to the paper's established statistical methodology and comparison frameworks.

What is the correct data structure for comparing step vs non-step recovery strategies under identical perturbation conditions?▼

The correct data structure for analyzing step vs non-step recovery strategies uses the `onset-aligned-merged-parquet` format. This preserves established data semantics and naming conventions, preventing you from reinventing data handling workflows for raw-trial datasets.

How do I set up a new analysis script for step vs non-step trial comparisons?▼

To set up a new analysis script for step vs non-step trial comparisons, default to using raw-trial data and polars for data manipulation. Ensure your code adheres to the `subject-velocity-trial` comparison units defined by the research paper.

Does the platform translation perturbation analysis require specific fixed and random effects in its LMM?▼

Yes, the platform translation perturbation analysis requires specific fixed and random effects in its Linear Mixed Models. The context enforces these paper-aligned statistical models, along with REML estimation, to maintain consistency with the original research goals.

Can I use polars for data manipulation when analyzing platform translation perturbation data?▼

Yes, you can use polars for data manipulation when analyzing platform translation perturbation data. The analysis context defaults to using polars alongside raw-trial data to maintain consistent data handling and preserve established research semantics.

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