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scientific-spectral-signal

Preprocess spectral and biosignal data with baseline correction, filtering, and peak detection.

3|1|Updated Feb 11, 2026
Search Tags:#signal-processing#spectral#ecg#peak-detection#eeg#baseline-correction#band-power
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-spectral-signal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-spectral-signal
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-spectral-signal
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-spectral-signal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a standard pipeline for preprocessing and analyzing spectral data (Raman, IR, UV-Vis) and biosignals (ECG, EEG), addressing noise, baseline drift, peak detection, and spectral power analysis.

Core Features & Use Cases

  • Baseline correction, denoising, and normalization for spectra and time-series signals.
  • Peak detection and quantification in spectra; band power analysis for EEG/ECG signals.
  • Use Case: Researchers can preprocess a Raman spectrum or EEG recording, extract features (peaks, band powers), and compare across samples.

Quick Start

Run a basic spectral preprocessing pipeline on a sample dataset to baseline-correct, smooth, normalize, and detect peaks.

Frequently Asked Questions about scientific-spectral-signal

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

FAQPage Schema
How do I preprocess Raman spectral data for baseline correction and denoising?▼

To preprocess Raman spectral data, apply baseline correction and denoising to smooth and normalize the spectra. This pipeline removes noise and baseline drift for accurate peak detection.

What is the best way to detect peaks in UV-Vis spectral data?▼

The best way to detect peaks in UV-Vis spectral data is using a modular preprocessing pipeline. It normalizes and smooths the signal first, then quantifies spectral peaks reproducibly.

How do I calculate band power for EEG signals using Python?▼

To calculate band power for EEG signals, use a processing pipeline integrating with SciPy and NumPy. It filters time-series data and performs spectral power analysis on the recordings.

Can I use this signal processing pipeline with SciPy and NumPy?▼

Yes, you can use this pipeline with SciPy and NumPy. It integrates with these common scientific Python tools to provide reproducible workflows for end-to-end biosignal analysis.

Does this skill handle baseline drift correction for ECG recordings?▼

Yes, it handles baseline drift correction for ECG recordings. The pipeline addresses baseline drift and noise in time-series signals, enabling accurate feature extraction like band power analysis.

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