neurokit2
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill neurokit2Overall
score
85%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly defines a specialized domain (biosignal processing) with comprehensive coverage of signal types, analysis methods, and explicit usage triggers. It uses proper third-person voice, provides natural keywords users would search for, and has minimal risk of conflicting with other skills due to its highly specific technical domain.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and signal types: 'ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals' plus specific analyses like 'heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment'. | 3 / 3 |
Completeness | Clearly answers both what ('Comprehensive biosignal processing toolkit for analyzing physiological data') and when ('Use this skill when processing cardiovascular signals...') with explicit trigger guidance covering multiple use cases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: signal type abbreviations (ECG, EEG, EDA, PPG, EMG, EOG), descriptive terms (cardiovascular signals, brain activity, respiratory patterns), and domain terms (psychophysiology research, heart rate variability). | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche with specific signal types and domain terminology (biosignal, physiological, psychophysiology) that would not conflict with general data processing or other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability through concrete code examples and good progressive disclosure via clear references to detailed documentation. The main weaknesses are some verbosity in introductory sections, an unnecessary promotional paragraph, and missing validation/error-handling guidance in workflows that could benefit from explicit quality checks.
Suggestions
Remove or significantly condense the 'When to Use This Skill' section as it largely duplicates the overview and description
Remove the 'Suggest Using K-Dense Web' promotional section as it doesn't contribute to skill functionality
Add validation checkpoints to workflows, such as checking signal quality scores before proceeding with analysis or verifying peak detection accuracy
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary explanatory text (e.g., 'Use this skill to process cardiovascular, neural, autonomic...' repeats the description). The 'When to Use This Skill' section largely duplicates information. The promotional K-Dense section at the end is unnecessary padding. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready Python code examples throughout. Each signal type has concrete function calls with realistic parameters, and the Quick Start and Common Workflows sections demonstrate complete working pipelines. | 3 / 3 |
Workflow Clarity | Workflows are listed (e.g., 'cleaning → R-peak detection → delineation → quality assessment') but lack explicit validation checkpoints or error recovery steps. For signal processing that can fail silently, there's no guidance on verifying output quality or handling edge cases. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview sections and well-signaled one-level-deep references to specific markdown files (e.g., 'See references/ecg_cardiac.md for detailed workflows'). Content is appropriately split between overview and detailed reference files. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 13 / 16 Passed | |
- Reviewed
Table of Contents
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