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.

Quality

92%

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Securityby

Passed

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Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality, method-aware skill body with executable patterns, explicit validation checkpoints, and clean one-level-deep reference structure. Only minor conciseness trimming of repeated cautions and version detail would improve it further.

Dimension	Reasoning	Score
Conciseness	The body is dense and information-rich with no padding of concepts Claude already knows, but repeated cautionary themes (units, validation, reproducibility) and pervasive version-pinning detail offer minor trim opportunities, keeping it just below fully lean.	4 / 5
Actionability	It provides numerous copy-paste-ready, executable code blocks (ecg_process, eda_phasic, events_find, complexity, bio_process) and exact bash command lines with flags covering the common per-modality cases.	5 / 5
Workflow Clarity	A clear 10-step preprocessing sequence and 8-item data contract serve as checklists, with explicit validation gates (inspect_signal before filtering, validate_multimodal before bio_process, plan_epochs before epoching); validation is present so the destructive/batch cap does not apply.	5 / 5
Progressive Disclosure	The body is an overview with a well-signaled, one-level-deep references table (12 reference files, all verified present) and a helpers table (6 scripts, all verified present), organized by modality for easy navigation.	5 / 5
	Total	19 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that concretely states capabilities and gives an explicit trigger clause with a useful negative boundary. The only minor gap is coverage of a few natural synonyms in the trigger phrasing.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete actions — 'preprocessing, event/interval analysis, multimodal alignment, variability, and complexity' plus 'method-aware validation' — giving comprehensive coverage of the skill's capabilities rather than vague language.	5 / 5
Completeness	It explicitly answers both 'what' ('build or audit reproducible research workflows for...') and 'when' ('Trigger when code imports neurokit2 or needs its current APIs...') with concrete trigger phrases, plus a negative boundary.	5 / 5
Trigger Term Quality	It includes the natural library name and import phrasing ('code imports neurokit2', 'current APIs, schemas, and method-aware validation') with good coverage, but a few natural synonyms users might say (e.g. 'biosignal', 'physiological signals') are absent from the trigger clause itself.	4 / 5
Distinctiveness Conflict Risk	It carves a clear niche around the NeuroKit2 library with distinct import/API triggers and adds a negative boundary ('not for diagnosis or device validation'), minimizing conflict with adjacent skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/neurokit2/SKILL.md
Commit: 36d8f13

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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