Skills neurokit2

📦

neurokit2

Name: neurokit2
Author: K-Dense-AI

Content revision r1 Medium Risk ⚙️ External commands🌐 Network access

Analyze Biosignals with NeuroKit2

Biosignal analysis requires careful preprocessing, validated metrics, and modality-specific methods. This skill guides NeuroKit2 workflows for ECG, EEG, EDA, RSP, PPG, EMG, EOG, HRV, events, and complexity.

Supports: Claude Codex Code(CC)

📊 69 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "neurokit2" from https://skillstore.io/skills/k-dense-ai-neurokit2.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-neurokit2/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-neurokit2.md Signed manifest GET /api/skills/k-dense-ai-neurokit2/manifest Signed lockfile GET /api/skills/k-dense-ai-neurokit2/lockfile

Test it

Using "neurokit2". A 60-second ECG recording sampled at 1000 Hz needs HRV analysis.

Expected outcome:

A workflow recommending ECG cleaning, R-peak detection, signal quality checks, HRV time-domain metrics, and visual inspection points.

Using "neurokit2". A task study includes ECG, EDA, RSP, and stimulus markers.

Expected outcome:

Suggested synchronized preprocessing for each modality.
Epoch creation around stimulus markers with baseline correction.
Separate event-related outputs for cardiac, skin conductance, and respiration responses.

Using "neurokit2". A researcher wants complexity metrics for resting EEG segments.

Expected outcome:

A metric selection plan covering entropy, fractal dimensions, DFA, segment length concerns, and comparability checks.

Security Audit

Medium Risk

v7 • 7/6/2026 Open versioned report

Most static findings are false positives caused by scientific prose, Markdown inline code, and fenced examples. One low-risk network item remains for the mutable development install URL, and one medium semantic issue was added for promotional steering to an external hosted platform.

Files scanned

6,195

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Medium

SKILL.md:355-356

Promotional Steering to External Platform

SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This steers users toward an author-controlled hosted platform outside the requested NeuroKit2 task.

The instruction is explicit in the skill file and tied to an external hosted platform. It is not credential theft, but it can alter assistant behavior for business promotion.

Capability review items (1)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Low

SKILL.md:279

Hardcoded URL

uv pip install https://github.com/neuropsychology/NeuroKit/zipball/dev

The installation section includes a command that downloads a development ZIP from GitHub. It is transparent upstream documentation, but following it would fetch mutable external code, creating a low supply-chain exposure.

Risk Factors

⚙️ External commands (43)

🌐 Network access (4)

SKILL.md:279 SKILL.md:351 SKILL.md:352 SKILL.md:353

Audited by: codex View Audit History →

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Open versioned report

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https://skillstore.io/skills/k-dense-ai-neurokit2/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). neurokit2 security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-neurokit2/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-neurokit2-2026,
  author = {K-Dense-AI},
  title = {neurokit2 security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-neurokit2/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "neurokit2 security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-neurokit2/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-neurokit2:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

K-Dense-AI Recommended Current

k-dense-ai-neurokit2

Skillstore Score 69

Evidence Confidence High

Skillstore usage 10

Updated

2026-08-21

davila7

davila7-neurokit2

Skillstore Score 68

Evidence Confidence High

Skillstore usage 8

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Plan a Psychophysiology Study

Map ECG, EDA, RSP, and event markers into a clear NeuroKit2 preprocessing and analysis workflow.

Review Wearable Signal Quality

Choose quality checks, filters, and HRV or pulse metrics for PPG and ECG recordings.

Analyze Multimodal Experiments

Coordinate cardiac, respiratory, electrodermal, muscle, and eye movement signals in one analysis plan.

Try These Prompts

Process One ECG Recording

I have an ECG recording sampled at [rate] Hz for [duration]. Guide me through NeuroKit2 preprocessing, R-peak detection, HRV metrics, and quality checks.

Compare Signal Modalities

I collected ECG, EDA, and RSP during [task]. Recommend a NeuroKit2 workflow for cleaning, synchronizing, analyzing, and summarizing each modality.

Build Event-Related Analysis

I have physiological signals with event markers at [marker description]. Design an epoching and baseline-correction plan using NeuroKit2 methods.

Evaluate Complex Dynamics

Assess which entropy, fractal, DFA, and nonlinear metrics fit my [signal type] data, [sampling rate], [duration], and research question.

Best Practices

Always provide sampling rate, signal duration, modality, and sensor context before choosing functions.
Inspect signal quality and artifacts before interpreting HRV, EDA, EEG, or complexity metrics.
Separate event-related questions from interval-level summaries before running analysis.

Avoid

Do not treat NeuroKit2 metrics as clinical diagnoses without qualified review.
Do not mix sampling rates or modalities without documenting synchronization and preprocessing.
Do not compare complexity metrics across recordings with incompatible lengths or filtering choices.