neurokit2
by K-Dense-AIneurokit2 is a Python biosignal processing skill for analyzing ECG, EEG, EDA, RSP, PPG, EMG, and EOG data. Use it to clean signals, detect peaks and events, extract HRV and complexity features, and support scientific workflows in psychophysiology, clinical analysis, and human-computer interaction.
This skill scores 74/100, which means it is a usable listing candidate for directory users: it has real biosignal workflow coverage and enough detail to reduce guesswork, but it would still benefit from stronger execution guidance and install-oriented support. Users deciding whether to install should view it as a practical NeuroKit2 reference/operational skill for physiological signal analysis, not a fully scaffolded, tool-backed package.
- Broad, explicit trigger coverage for ECG, EEG, EDA, RSP, PPG, EMG, EOG, and multi-modal physiological analysis.
- Substantial SKILL.md content with valid frontmatter, long body, and many headings, which improves scannability and operational understanding.
- Uses concrete workflow-oriented language for common analyses like HRV, ERPs, complexity measures, autonomic assessment, and signal integration.
- No install command and no support files/scripts, so users may need to infer environment setup and execution details.
- No references/resources/rules files, which limits trust signals and makes edge-case behavior or exact method selection less discoverable.
Overview of neurokit2 skill
What neurokit2 is for
neurokit2 is a Python biosignal processing skill for analyzing physiological data such as ECG, EEG, EDA, RSP, PPG, EMG, and EOG. It is most useful when you need to turn raw or lightly cleaned sensor data into interpretable measures like heart rate variability, event markers, autonomic activity, or signal complexity.
Best-fit users and tasks
This neurokit2 skill is a strong fit for researchers, data scientists, and scientific developers working in psychophysiology, clinical signal analysis, or human-computer interaction. Use it when the real job is not just plotting signals, but cleaning them, extracting features, and comparing physiology across conditions, trials, or participants.
Why install it
Install neurokit2 when you want a practical workflow for biosignal analysis rather than a generic Python prompt. The main value is speed to correct preprocessing choices, feature extraction, and modality-specific analysis steps that are easy to get wrong without a guide.
How to Use neurokit2 skill
Install neurokit2
Use the skill install flow in your directory, then load the skill before asking for analysis help. A typical install command is:
npx skills add K-Dense-AI/claude-scientific-skills --skill neurokit2
If your environment uses a different skill manager, install the skill into the same workspace where you will run the analysis so the agent can read the repository context.
Give the skill the right input
The neurokit2 skill works best when you specify:
- signal type:
ECG,EEG,EDA,PPG,EMG, orEOG - sampling rate
- file format or column names
- what you want out: cleaning, peaks, HRV, epochs, connectivity, or summary features
- any constraints: missing samples, artifacts, short recordings, multi-subject data
A weak request is “analyze this physiological data.” A stronger one is “Use neurokit2 to clean 5-minute ECG at 1000 Hz, detect R-peaks, compute HRV time and frequency metrics, and flag segments with motion artifact.”
Read these files first
Start with scientific-skills/neurokit2/SKILL.md to see the intended workflow and supported tasks. If you are adapting the skill into your own analysis process, inspect the repository tree around that file and any linked sections in the skill body before writing code or prompts.
Prompting workflow that works
For best results, ask for a staged output:
- identify the signal type and expected preprocessing
- validate sampling rate and data shape
- run artifact handling and peak/event detection
- compute the requested metrics
- summarize interpretation limits
This helps the neurokit2 skill avoid jumping straight to metrics before the input quality is known, which is a common failure mode in biosignal work.
neurokit2 skill FAQ
Is neurokit2 only for one signal type?
No. The neurokit2 skill supports multiple physiological modalities, but it is especially valuable when you need a consistent workflow across ECG, EEG, EDA, respiration, and related biosignals. If your data is not physiological, this skill is probably the wrong fit.
Do I need neurokit2 install knowledge first?
Basic Python familiarity helps, but you do not need to know every function in advance. The neurokit2 guide is useful when you know the biosignal and the end goal, but not the exact preprocessing or feature-extraction sequence.
Is a plain prompt enough?
Sometimes for toy examples, but not for real scientific work. The neurokit2 skill is better when you need repeatable analysis steps, modality-aware defaults, and guidance on what inputs are required before trusting results.
When should I not use it?
Do not use neurokit2 for non-physiological data, undocumented sensor streams, or tasks where the sampling rate and signal meaning are unclear. If your main problem is statistical modeling after feature extraction, the skill can help with preprocessing, but it is not a substitute for your analysis pipeline.
How to Improve neurokit2 skill
Give cleaner, narrower inputs
The biggest quality gain comes from clearly stating the signal, sampling rate, duration, and target output. For example, “ECG from 12 participants, 500 Hz, want R-peaks and HRV by condition” is far better than “analyze my physiology data.” That extra specificity helps the neurokit2 skill choose the right processing path.
Describe data quality before analysis
Tell the model about missing samples, motion artifacts, baseline drift, or irregular event timing. neurokit2 results are only as good as the preprocessing assumptions, so these details change whether you should filter, interpolate, segment, or exclude data.
Ask for interpretation boundaries
For scientific use, ask the skill to separate computed metrics from claims. A good neurokit2 guide output should say what the numbers mean, what is uncertain, and what cannot be inferred from the signal alone. This is especially important for neurokit2 for Scientific work where overinterpretation is easy.
Iterate with a concrete second pass
After the first result, refine with one specific follow-up: “show the exact preprocessing steps,” “compare HRV metrics across two conditions,” or “adapt this for batch processing across subjects.” That produces more useful output than asking for a broader rewrite, and it helps surface edge cases in the neurokit2 skill workflow.