[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72EBPPTDCMY034Y2EKBF03K":3,"skill-same:01M72EBPPTDCMY034Y2EKBF03K":99},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":6,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M72EBPPTDCMY034Y2EKBF03K","neurokit2",null,"Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.","Minions-Land\u002FScience-Discovery","Minions-Land","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F272697820?v=4","01M729FA9HA1NB3692B59MRBJC","Minions Land","01M729FAA6GEV2ZR1CNDZ8WZPJ",5,"MIT","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002FSKILL.md",[],[],"2026-11-10 18:39:20",false,"---\nname: neurokit2\ndescription: Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\n---\n\n# NeuroKit2\n\n## Overview\n\nNeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.\n\n## When to Use This Skill\n\nApply this skill when working with:\n- **Cardiac signals**: ECG, PPG, heart rate variability (HRV), pulse analysis\n- **Brain signals**: EEG frequency bands, microstates, complexity, source localization\n- **Autonomic signals**: Electrodermal activity (EDA\u002FGSR), skin conductance responses (SCR)\n- **Respiratory signals**: Breathing rate, respiratory variability (RRV), volume per time\n- **Muscular signals**: EMG amplitude, muscle activation detection\n- **Eye tracking**: EOG, blink detection and analysis\n- **Multi-modal integration**: Processing multiple physiological signals simultaneously\n- **Complexity analysis**: Entropy measures, fractal dimensions, nonlinear dynamics\n\n## Core Capabilities\n\n### 1. Cardiac Signal Processing (ECG\u002FPPG)\n\nProcess electrocardiogram and photoplethysmography signals for cardiovascular analysis. See `references\u002Fecg_cardiac.md` for detailed workflows.\n\n**Primary workflows:**\n- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment\n- HRV analysis across time, frequency, and nonlinear domains\n- PPG pulse analysis and quality assessment\n- ECG-derived respiration extraction\n\n**Key functions:**\n```python\nimport neurokit2 as nk\n\n# Complete ECG processing pipeline\nsignals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)\n\n# Analyze ECG data (event-related or interval-related)\nanalysis = nk.ecg_analyze(signals, sampling_rate=1000)\n\n# Comprehensive HRV analysis\nhrv = nk.hrv(peaks, sampling_rate=1000)  # Time, frequency, nonlinear domains\n```\n\n### 2. Heart Rate Variability Analysis\n\nCompute comprehensive HRV metrics from cardiac signals. See `references\u002Fhrv.md` for all indices and domain-specific analysis.\n\n**Supported domains:**\n- **Time domain**: SDNN, RMSSD, pNN50, SDSD, and derived metrics\n- **Frequency domain**: ULF, VLF, LF, HF, VHF power and ratios\n- **Nonlinear domain**: Poincaré plot (SD1\u002FSD2), entropy measures, fractal dimensions\n- **Specialized**: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)\n\n**Key functions:**\n```python\n# All HRV indices at once\nhrv_indices = nk.hrv(peaks, sampling_rate=1000)\n\n# Domain-specific analysis\nhrv_time = nk.hrv_time(peaks)\nhrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)\nhrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)\nhrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)\n```\n\n### 3. Brain Signal Analysis (EEG)\n\nAnalyze electroencephalography signals for frequency power, complexity, and microstate patterns. See `references\u002Feeg.md` for detailed workflows and MNE integration.\n\n**Primary capabilities:**\n- Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)\n- Channel quality assessment and re-referencing\n- Source localization (sLORETA, MNE)\n- Microstate segmentation and transition dynamics\n- Global field power and dissimilarity measures\n\n**Key functions:**\n```python\n# Power analysis across frequency bands\npower = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])\n\n# Microstate analysis\nmicrostates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')\nstatic = nk.microstates_static(microstates)\ndynamic = nk.microstates_dynamic(microstates)\n```\n\n### 4. Electrodermal Activity (EDA)\n\nProcess skin conductance signals for autonomic nervous system assessment. See `references\u002Feda.md` for detailed workflows.\n\n**Primary workflows:**\n- Signal decomposition into tonic and phasic components\n- Skin conductance response (SCR) detection and analysis\n- Sympathetic nervous system index calculation\n- Autocorrelation and changepoint detection\n\n**Key functions:**\n```python\n# Complete EDA processing\nsignals, info = nk.eda_process(eda_signal, sampling_rate=100)\n\n# Analyze EDA data\nanalysis = nk.eda_analyze(signals, sampling_rate=100)\n\n# Sympathetic nervous system activity\nsympathetic = nk.eda_sympathetic(signals, sampling_rate=100)\n```\n\n### 5. Respiratory Signal Processing (RSP)\n\nAnalyze breathing patterns and respiratory variability. See `references\u002Frsp.md` for detailed workflows.\n\n**Primary capabilities:**\n- Respiratory rate calculation and variability analysis\n- Breathing amplitude and symmetry assessment\n- Respiratory volume per time (fMRI applications)\n- Respiratory amplitude variability (RAV)\n\n**Key functions:**\n```python\n# Complete RSP processing\nsignals, info = nk.rsp_process(rsp_signal, sampling_rate=100)\n\n# Respiratory rate variability\nrrv = nk.rsp_rrv(signals, sampling_rate=100)\n\n# Respiratory volume per time\nrvt = nk.rsp_rvt(signals, sampling_rate=100)\n```\n\n### 6. Electromyography (EMG)\n\nProcess muscle activity signals for activation detection and amplitude analysis. See `references\u002Femg.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EMG processing\nsignals, info = nk.emg_process(emg_signal, sampling_rate=1000)\n\n# Muscle activation detection\nactivation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')\n```\n\n### 7. Electrooculography (EOG)\n\nAnalyze eye movement and blink patterns. See `references\u002Feog.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EOG processing\nsignals, info = nk.eog_process(eog_signal, sampling_rate=500)\n\n# Extract blink features\nfeatures = nk.eog_features(signals, sampling_rate=500)\n```\n\n### 8. General Signal Processing\n\nApply filtering, decomposition, and transformation operations to any signal. See `references\u002Fsignal_processing.md` for comprehensive utilities.\n\n**Key operations:**\n- Filtering (lowpass, highpass, bandpass, bandstop)\n- Decomposition (EMD, SSA, wavelet)\n- Peak detection and correction\n- Power spectral density estimation\n- Signal interpolation and resampling\n- Autocorrelation and synchrony analysis\n\n**Key functions:**\n```python\n# Filtering\nfiltered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)\n\n# Peak detection\npeaks = nk.signal_findpeaks(signal)\n\n# Power spectral density\npsd = nk.signal_psd(signal, sampling_rate=1000)\n```\n\n### 9. Complexity and Entropy Analysis\n\nCompute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See `references\u002Fcomplexity.md` for all available metrics.\n\n**Available measures:**\n- **Entropy**: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale\n- **Fractal dimensions**: Katz, Higuchi, Petrosian, Sevcik, correlation dimension\n- **Nonlinear dynamics**: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification\n- **DFA**: Detrended fluctuation analysis, multifractal DFA\n- **Information theory**: Fisher information, mutual information\n\n**Key functions:**\n```python\n# Multiple complexity metrics at once\ncomplexity_indices = nk.complexity(signal, sampling_rate=1000)\n\n# Specific measures\napen = nk.entropy_approximate(signal)\ndfa = nk.fractal_dfa(signal)\nlyap = nk.complexity_lyapunov(signal, sampling_rate=1000)\n```\n\n### 10. Event-Related Analysis\n\nCreate epochs around stimulus events and analyze physiological responses. See `references\u002Fepochs_events.md` for workflows.\n\n**Primary capabilities:**\n- Epoch creation from event markers\n- Event-related averaging and visualization\n- Baseline correction options\n- Grand average computation with confidence intervals\n\n**Key functions:**\n```python\n# Find events in signal\nevents = nk.events_find(trigger_signal, threshold=0.5)\n\n# Create epochs around events\nepochs = nk.epochs_create(signals, events, sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Average across epochs\ngrand_average = nk.epochs_average(epochs)\n```\n\n### 11. Multi-Signal Integration\n\nProcess multiple physiological signals simultaneously with unified output. See `references\u002Fbio_module.md` for integration workflows.\n\n**Key functions:**\n```python\n# Process multiple signals at once\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    emg=emg_signal,\n    sampling_rate=1000\n)\n\n# Analyze all processed signals\nbio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n## Analysis Modes\n\nNeuroKit2 automatically selects between two analysis modes based on data duration:\n\n**Event-related analysis** (\u003C 10 seconds):\n- Analyzes stimulus-locked responses\n- Epoch-based segmentation\n- Suitable for experimental paradigms with discrete trials\n\n**Interval-related analysis** (≥ 10 seconds):\n- Characterizes physiological patterns over extended periods\n- Resting state or continuous activities\n- Suitable for baseline measurements and long-term monitoring\n\nMost `*_analyze()` functions automatically choose the appropriate mode.\n\n## Installation\n\n```bash\nuv pip install neurokit2\n```\n\nFor development version:\n```bash\nuv pip install https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\u002Fzipball\u002Fdev\n```\n\n## Common Workflows\n\n### Quick Start: ECG Analysis\n```python\nimport neurokit2 as nk\n\n# Load example data\necg = nk.ecg_simulate(duration=60, sampling_rate=1000)\n\n# Process ECG\nsignals, info = nk.ecg_process(ecg, sampling_rate=1000)\n\n# Analyze HRV\nhrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)\n\n# Visualize\nnk.ecg_plot(signals, info)\n```\n\n### Multi-Modal Analysis\n```python\n# Process multiple signals\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    sampling_rate=1000\n)\n\n# Analyze all signals\nresults = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n### Event-Related Potential\n```python\n# Find events\nevents = nk.events_find(trigger_channel, threshold=0.5)\n\n# Create epochs\nepochs = nk.epochs_create(processed_signals, events,\n                          sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Event-related analysis for each signal type\necg_epochs = nk.ecg_eventrelated(epochs)\neda_epochs = nk.eda_eventrelated(epochs)\n```\n\n## References\n\nThis skill includes comprehensive reference documentation organized by signal type and analysis method:\n\n- **ecg_cardiac.md**: ECG\u002FPPG processing, R-peak detection, delineation, quality assessment\n- **hrv.md**: Heart rate variability indices across all domains\n- **eeg.md**: EEG analysis, frequency bands, microstates, source localization\n- **eda.md**: Electrodermal activity processing and SCR analysis\n- **rsp.md**: Respiratory signal processing and variability\n- **ppg.md**: Photoplethysmography signal analysis\n- **emg.md**: Electromyography processing and activation detection\n- **eog.md**: Electrooculography and blink analysis\n- **signal_processing.md**: General signal utilities and transformations\n- **complexity.md**: Entropy, fractal, and nonlinear measures\n- **epochs_events.md**: Event-related analysis and epoch creation\n- **bio_module.md**: Multi-signal integration workflows\n\nLoad specific reference files as needed using the Read tool to access detailed function documentation and parameters.\n\n## Additional Resources\n\n- Official Documentation: https:\u002F\u002Fneuropsychology.github.io\u002FNeuroKit\u002F\n- GitHub Repository: https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\n- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-020-01516-y\n\n","# NeuroKit2\n\n## Overview\n\nNeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.\n\n## When to Use This Skill\n\nApply this skill when working with:\n- **Cardiac signals**: ECG, PPG, heart rate variability (HRV), pulse analysis\n- **Brain signals**: EEG frequency bands, microstates, complexity, source localization\n- **Autonomic signals**: Electrodermal activity (EDA\u002FGSR), skin conductance responses (SCR)\n- **Respiratory signals**: Breathing rate, respiratory variability (RRV), volume per time\n- **Muscular signals**: EMG amplitude, muscle activation detection\n- **Eye tracking**: EOG, blink detection and analysis\n- **Multi-modal integration**: Processing multiple physiological signals simultaneously\n- **Complexity analysis**: Entropy measures, fractal dimensions, nonlinear dynamics\n\n## Core Capabilities\n\n### 1. Cardiac Signal Processing (ECG\u002FPPG)\n\nProcess electrocardiogram and photoplethysmography signals for cardiovascular analysis. See `references\u002Fecg_cardiac.md` for detailed workflows.\n\n**Primary workflows:**\n- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment\n- HRV analysis across time, frequency, and nonlinear domains\n- PPG pulse analysis and quality assessment\n- ECG-derived respiration extraction\n\n**Key functions:**\n```python\nimport neurokit2 as nk\n\n# Complete ECG processing pipeline\nsignals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)\n\n# Analyze ECG data (event-related or interval-related)\nanalysis = nk.ecg_analyze(signals, sampling_rate=1000)\n\n# Comprehensive HRV analysis\nhrv = nk.hrv(peaks, sampling_rate=1000)  # Time, frequency, nonlinear domains\n```\n\n### 2. Heart Rate Variability Analysis\n\nCompute comprehensive HRV metrics from cardiac signals. See `references\u002Fhrv.md` for all indices and domain-specific analysis.\n\n**Supported domains:**\n- **Time domain**: SDNN, RMSSD, pNN50, SDSD, and derived metrics\n- **Frequency domain**: ULF, VLF, LF, HF, VHF power and ratios\n- **Nonlinear domain**: Poincaré plot (SD1\u002FSD2), entropy measures, fractal dimensions\n- **Specialized**: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)\n\n**Key functions:**\n```python\n# All HRV indices at once\nhrv_indices = nk.hrv(peaks, sampling_rate=1000)\n\n# Domain-specific analysis\nhrv_time = nk.hrv_time(peaks)\nhrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)\nhrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)\nhrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)\n```\n\n### 3. Brain Signal Analysis (EEG)\n\nAnalyze electroencephalography signals for frequency power, complexity, and microstate patterns. See `references\u002Feeg.md` for detailed workflows and MNE integration.\n\n**Primary capabilities:**\n- Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)\n- Channel quality assessment and re-referencing\n- Source localization (sLORETA, MNE)\n- Microstate segmentation and transition dynamics\n- Global field power and dissimilarity measures\n\n**Key functions:**\n```python\n# Power analysis across frequency bands\npower = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])\n\n# Microstate analysis\nmicrostates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')\nstatic = nk.microstates_static(microstates)\ndynamic = nk.microstates_dynamic(microstates)\n```\n\n### 4. Electrodermal Activity (EDA)\n\nProcess skin conductance signals for autonomic nervous system assessment. See `references\u002Feda.md` for detailed workflows.\n\n**Primary workflows:**\n- Signal decomposition into tonic and phasic components\n- Skin conductance response (SCR) detection and analysis\n- Sympathetic nervous system index calculation\n- Autocorrelation and changepoint detection\n\n**Key functions:**\n```python\n# Complete EDA processing\nsignals, info = nk.eda_process(eda_signal, sampling_rate=100)\n\n# Analyze EDA data\nanalysis = nk.eda_analyze(signals, sampling_rate=100)\n\n# Sympathetic nervous system activity\nsympathetic = nk.eda_sympathetic(signals, sampling_rate=100)\n```\n\n### 5. Respiratory Signal Processing (RSP)\n\nAnalyze breathing patterns and respiratory variability. See `references\u002Frsp.md` for detailed workflows.\n\n**Primary capabilities:**\n- Respiratory rate calculation and variability analysis\n- Breathing amplitude and symmetry assessment\n- Respiratory volume per time (fMRI applications)\n- Respiratory amplitude variability (RAV)\n\n**Key functions:**\n```python\n# Complete RSP processing\nsignals, info = nk.rsp_process(rsp_signal, sampling_rate=100)\n\n# Respiratory rate variability\nrrv = nk.rsp_rrv(signals, sampling_rate=100)\n\n# Respiratory volume per time\nrvt = nk.rsp_rvt(signals, sampling_rate=100)\n```\n\n### 6. Electromyography (EMG)\n\nProcess muscle activity signals for activation detection and amplitude analysis. See `references\u002Femg.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EMG processing\nsignals, info = nk.emg_process(emg_signal, sampling_rate=1000)\n\n# Muscle activation detection\nactivation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')\n```\n\n### 7. Electrooculography (EOG)\n\nAnalyze eye movement and blink patterns. See `references\u002Feog.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EOG processing\nsignals, info = nk.eog_process(eog_signal, sampling_rate=500)\n\n# Extract blink features\nfeatures = nk.eog_features(signals, sampling_rate=500)\n```\n\n### 8. General Signal Processing\n\nApply filtering, decomposition, and transformation operations to any signal. See `references\u002Fsignal_processing.md` for comprehensive utilities.\n\n**Key operations:**\n- Filtering (lowpass, highpass, bandpass, bandstop)\n- Decomposition (EMD, SSA, wavelet)\n- Peak detection and correction\n- Power spectral density estimation\n- Signal interpolation and resampling\n- Autocorrelation and synchrony analysis\n\n**Key functions:**\n```python\n# Filtering\nfiltered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)\n\n# Peak detection\npeaks = nk.signal_findpeaks(signal)\n\n# Power spectral density\npsd = nk.signal_psd(signal, sampling_rate=1000)\n```\n\n### 9. Complexity and Entropy Analysis\n\nCompute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See `references\u002Fcomplexity.md` for all available metrics.\n\n**Available measures:**\n- **Entropy**: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale\n- **Fractal dimensions**: Katz, Higuchi, Petrosian, Sevcik, correlation dimension\n- **Nonlinear dynamics**: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification\n- **DFA**: Detrended fluctuation analysis, multifractal DFA\n- **Information theory**: Fisher information, mutual information\n\n**Key functions:**\n```python\n# Multiple complexity metrics at once\ncomplexity_indices = nk.complexity(signal, sampling_rate=1000)\n\n# Specific measures\napen = nk.entropy_approximate(signal)\ndfa = nk.fractal_dfa(signal)\nlyap = nk.complexity_lyapunov(signal, sampling_rate=1000)\n```\n\n### 10. Event-Related Analysis\n\nCreate epochs around stimulus events and analyze physiological responses. See `references\u002Fepochs_events.md` for workflows.\n\n**Primary capabilities:**\n- Epoch creation from event markers\n- Event-related averaging and visualization\n- Baseline correction options\n- Grand average computation with confidence intervals\n\n**Key functions:**\n```python\n# Find events in signal\nevents = nk.events_find(trigger_signal, threshold=0.5)\n\n# Create epochs around events\nepochs = nk.epochs_create(signals, events, sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Average across epochs\ngrand_average = nk.epochs_average(epochs)\n```\n\n### 11. Multi-Signal Integration\n\nProcess multiple physiological signals simultaneously with unified output. See `references\u002Fbio_module.md` for integration workflows.\n\n**Key functions:**\n```python\n# Process multiple signals at once\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    emg=emg_signal,\n    sampling_rate=1000\n)\n\n# Analyze all processed signals\nbio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n## Analysis Modes\n\nNeuroKit2 automatically selects between two analysis modes based on data duration:\n\n**Event-related analysis** (\u003C 10 seconds):\n- Analyzes stimulus-locked responses\n- Epoch-based segmentation\n- Suitable for experimental paradigms with discrete trials\n\n**Interval-related analysis** (≥ 10 seconds):\n- Characterizes physiological patterns over extended periods\n- Resting state or continuous activities\n- Suitable for baseline measurements and long-term monitoring\n\nMost `*_analyze()` functions automatically choose the appropriate mode.\n\n## Installation\n\n```bash\nuv pip install neurokit2\n```\n\nFor development version:\n```bash\nuv pip install https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\u002Fzipball\u002Fdev\n```\n\n## Common Workflows\n\n### Quick Start: ECG Analysis\n```python\nimport neurokit2 as nk\n\n# Load example data\necg = nk.ecg_simulate(duration=60, sampling_rate=1000)\n\n# Process ECG\nsignals, info = nk.ecg_process(ecg, sampling_rate=1000)\n\n# Analyze HRV\nhrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)\n\n# Visualize\nnk.ecg_plot(signals, info)\n```\n\n### Multi-Modal Analysis\n```python\n# Process multiple signals\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    sampling_rate=1000\n)\n\n# Analyze all signals\nresults = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n### Event-Related Potential\n```python\n# Find events\nevents = nk.events_find(trigger_channel, threshold=0.5)\n\n# Create epochs\nepochs = nk.epochs_create(processed_signals, events,\n                          sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Event-related analysis for each signal type\necg_epochs = nk.ecg_eventrelated(epochs)\neda_epochs = nk.eda_eventrelated(epochs)\n```\n\n## References\n\nThis skill includes comprehensive reference documentation organized by signal type and analysis method:\n\n- **ecg_cardiac.md**: ECG\u002FPPG processing, R-peak detection, delineation, quality assessment\n- **hrv.md**: Heart rate variability indices across all domains\n- **eeg.md**: EEG analysis, frequency bands, microstates, source localization\n- **eda.md**: Electrodermal activity processing and SCR analysis\n- **rsp.md**: Respiratory signal processing and variability\n- **ppg.md**: Photoplethysmography signal analysis\n- **emg.md**: Electromyography processing and activation detection\n- **eog.md**: Electrooculography and blink analysis\n- **signal_processing.md**: General signal utilities and transformations\n- **complexity.md**: Entropy, fractal, and nonlinear measures\n- **epochs_events.md**: Event-related analysis and epoch creation\n- **bio_module.md**: Multi-signal integration workflows\n\nLoad specific reference files as needed using the Read tool to access detailed function documentation and parameters.\n\n## Additional Resources\n\n- Official Documentation: https:\u002F\u002Fneuropsychology.github.io\u002FNeuroKit\u002F\n- GitHub Repository: https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\n- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-020-01516-y\n\n",{"name":5,"license":24,"metadata":25,"description":7},"MIT license",{"skill-author":26},"K-Dense Inc.","main","aa7a019e00fa39031d9b30bf5b1becb094af4d0a","scientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2",0,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery","https:\u002F\u002Fgithub.com\u002FMinions-Land","Ming‘s Artificial Intelligence Group","2026-11-01 00:00:09",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add Minions-Land\u002FScience-Discovery --skill neurokit2",{"tool":41,"command":42},"bunx","bunx skills add Minions-Land\u002FScience-Discovery --skill neurokit2",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Minions-Land\u002FScience-Discovery --skill neurokit2","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002FSKILL.md",true,[49,55,59,63,67,71,75,79,83,87,91,95],{"path":50,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":53,"sourceHtmlUrl":54},"references\u002Fbio_module.md","REFERENCE","参考文档",13099,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fbio_module.md",{"path":56,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":57,"sourceHtmlUrl":58},"references\u002Fcomplexity.md",17352,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fcomplexity.md",{"path":60,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Fecg_cardiac.md",10810,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fecg_cardiac.md",{"path":64,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":65,"sourceHtmlUrl":66},"references\u002Feda.md",14733,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Feda.md",{"path":68,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":69,"sourceHtmlUrl":70},"references\u002Feeg.md",14306,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Feeg.md",{"path":72,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Femg.md",12390,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Femg.md",{"path":76,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Feog.md",12271,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Feog.md",{"path":80,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fepochs_events.md",14793,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fepochs_events.md",{"path":84,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fhrv.md",15493,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fhrv.md",{"path":88,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fppg.md",11168,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fppg.md",{"path":92,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Frsp.md",13946,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Frsp.md",{"path":96,"resourceType":51,"resourceTypeDesc":52,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fsignal_processing.md",16652,"https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fneurokit2\u002Freferences\u002Fsignal_processing.md",[100,107,114,121,128,135],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":19},"01M72EN1WZSQM1NTDKSR1EH1JT","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fzarr-python\u002FSKILL.md",[],[],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":19},"01M72EMPMQPWWZKEWCK808J840","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":19},"01M72EMHDRH8EV0X6Q69S1TVNT","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fvenue-templates\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":19},"01M72EMDR9SMKTSBKSG3NEERKG","vaex","Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fvaex\u002FSKILL.md",[],[],{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":19},"01M72EMAH9QAPF3V3XK0M0QHFS","usfiscaldata","Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Fusfiscaldata\u002FSKILL.md",[],[],{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":19},"01M72EM1HYA6GN3K7K29TK2J63","treatment-plans","Generate concise (3-4 page), focused medical treatment plans in LaTeX\u002FPDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.","https:\u002F\u002Fgithub.com\u002FMinions-Land\u002FScience-Discovery\u002Fblob\u002Fmain\u002Fscientific-agent-skills-main\u002Fscientific-skills\u002Ftreatment-plans\u002FSKILL.md",[],[]]