NeuroKit2 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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionneurokit2Execute the skills CLI command in your project's root directory to begin installation:
Fetches neurokit2 from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate neurokit2. Access via /neurokit2 in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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NeuroKit2 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.
Apply this skill when working with:
Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.
Primary workflows:
Key functions:
import neurokit2 as nk
# Complete ECG processing pipeline
signals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)
# Analyze ECG data (event-related or interval-related)
analysis = nk.ecg_analyze(signals, sampling_rate=1000)
# Comprehensive HRV analysis
hrv = nk.hrv(peaks, sampling_rate=1000) # Time, frequency, nonlinear domains
Compute comprehensive HRV metrics from cardiac signals. See references/hrv.md for all indices and domain-specific analysis.
Supported domains:
Key functions:
# All HRV indices at once
hrv_indices = nk.hrv(peaks, sampling_rate=1000)
# Domain-specific analysis
hrv_time = nk.hrv_time(peaks)
hrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)
hrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)
hrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)
Analyze electroencephalography signals for frequency power, complexity, and microstate patterns. See references/eeg.md for detailed workflows and MNE integration.
Primary capabilities:
Key functions:
# Power analysis across frequency bands
power = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])
# Microstate analysis
microstates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')
static = nk.microstates_static(microstates)
dynamic = nk.microstates_dynamic(microstates)
Process skin conductance signals for autonomic nervous system assessment. See references/eda.md for detailed workflows.
Primary workflows:
Key functions:
# Complete EDA processing
signals, info = nk.eda_process(eda_signal, sampling_rate=100)
# Analyze EDA data
analysis = nk.eda_analyze(signals, sampling_rate=100)
# Sympathetic nervous system activity
sympathetic = nk.eda_sympathetic(signals, sampling_rate=100)
Analyze breathing patterns and respiratory variability. See references/rsp.md for detailed workflows.
Primary capabilities:
Key functions:
# Complete RSP processing
signals, info = nk.rsp_process(rsp_signal, sampling_rate=100)
# Respiratory rate variability
rrv = nk.rsp_rrv(signals, sampling_rate=100)
# Respiratory volume per time
rvt = nk.rsp_rvt(signals, sampling_rate=100)
Process muscle activity signals for activation detection and amplitude analysis. See references/emg.md for workflows.
Key functions:
# Complete EMG processing
signals, info = nk.emg_process(emg_signal, sampling_rate=1000)
# Muscle activation detection
activation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')
Analyze eye movement and blink patterns. See references/eog.md for workflows.
Key functions:
# Complete EOG processing
signals, info = nk.eog_process(eog_signal, sampling_rate=500)
# Extract blink features
features = nk.eog_features(signals, sampling_rate=500)
Apply filtering, decomposition, and transformation operations to any signal. See references/signal_processing.md for comprehensive utilities.
Key operations:
Key functions:
# Filtering
filtered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)
# Peak detection
peaks = nk.signal_findpeaks(signal)
# Power spectral density
psd = nk.signal_psd(signal, sampling_rate=1000)
Compute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See references/complexity.md for all available metrics.
Available measures:
Key functions:
# Multiple complexity metrics at once
complexity_indices = nk.complexity(signal, sampling_rate=1000)
# Specific measures
apen = nk.entropy_approximate(signal)
dfa = nk.fractal_dfa(signal)
lyap = nk.complexity_lyapunov(signal, sampling_rate=1000)
Create epochs around stimulus events and analyze physiological responses. See references/epochs_events.md for workflows.
Primary capabilities:
Key functions:
# Find events in signal
events = nk.events_find(trigger_signal, threshold=0.5)
# Create epochs around events
epochs = nk.epochs_create(signals, events, sampling_rate=1000,
epochs_start=-0.5, epochs_end=2.0)
# Average across epochs
grand_average = nk.epochs_average(epochs)
Process multiple physiological signals simultaneously with unified output. See references/bio_module.md for integration workflows.
Key functions:
# Process multiple signals at once
bio_signals, bio_info = nk.bio_process(
ecg=ecg_signal,
rsp=rsp_signal,
eda=eda_signal,
emg=emg_signal,
sampling_rate=1000
)
# Analyze all processed signals
bio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)
NeuroKit2 automatically selects between two analysis modes based on data duration:
Event-related analysis (< 10 seconds):
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
I recommend neurokit2 for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
neurokit2 is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: neurokit2 is focused, and the summary matches what you get after install.
neurokit2 reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added neurokit2 from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added neurokit2 from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
neurokit2 reduced setup friction for our internal harness; good balance of opinion and flexibility.
neurokit2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: neurokit2 is the kind of skill you can hand to a new teammate without a long onboarding doc.
neurokit2 has been reliable in day-to-day use. Documentation quality is above average for community skills.
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