Agent skill · research science · k-dense-ai
neurokit2
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Why this skill is useful
Provides specific methods and scripts for physiological time-series preprocessing that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, neurokit2, numpy, pandas, pywavelets, requests, scikit-learn, scipy, setuptools installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
NeuroKit2 Scope and evidence cutoff Use this skill for method-aware, reproducible biosignal research with NeuroKit2. The snapshot was checked on 2026-07-23 against: stable PyPI 0.2.13, released 2026-03-02; Python metadata (>=3.10; classifiers 3.10–3.14) and wheel dependencies; GitHub release notes/tags, NEWS.rst, source at tag v0.2.13; official API pages/examples (the live site identified itself as 0.2.13.dev214); and pinned 0.2.13 runtime signatures and synthetic output schemas. The live documentation can be ahead of the stable wheel. Prefer the pinned runtime for reproducible work and name both versions if consulting development docs. Boundary NeuroKit2 is a research and educational toolbox. Do not present its output as: a diagnosis, treatment recommendation, patient-monitoring decision, or alarm; validation, certification, or regulatory evidence for a medical device; or proof that a physiological construct is measured validly in a new sensor, protocol, environment, population, or disease group. Validate acquisition hardware, electrode/optode placement, units, sampling and clock accuracy, preprocessing, detector/decomposition method, population, task, and outcomes for the intended study. Preserve raw data and an auditable exclusion log. Use deidentified local files only; do not place PHI in prompts, logs, examples, or bundled fixtures. Reproducible installation For optional features, create a uv project, add only the packages actually required at reviewed exact versions, and commit/review the resulting uv.lock before uv sync --locked. NeuroKit2 exposes an upstream full extra, but this skill intentionally does not install that floating transitive set in an automated workflow. Optional capabilities can require MNE, cvxopt, Plotly, PyEMD, pyRQA, Pillow, OpenCV, or file readers. Record the resolved environment with the analysis. Provision any MNE data/template download as an explicit, checksummed study input. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/neurokit2