Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lord1Egypt/scientific-agent-toolkit --skill neurokit2git clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/neurokit2)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/neurokit2"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/neurokit2/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/neurokit2"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/neurokit2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.02846 |
| Opus 5 | $0.00048 | $0.01423 |
| Sonnet 5 | $0.00019 | $0.00569 |
| Haiku 4.5 | $0.00010 | $0.00285 |
Grade A, and why
neurokit2 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to neurokit2 — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeuroKit2
Overview
NeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.
When to Use This Skill
Apply this skill when working with:
- Cardiac signals: ECG, PPG, heart rate variability (HRV), pulse analysis
- Brain signals: EEG frequency bands, microstates, complexity, source localization
- Autonomic signals: Electrodermal activity (EDA/GSR), skin conductance responses (SCR)
- Respiratory signals: Breathing rate, respiratory variability (RRV), volume per time
- Muscular signals: EMG amplitude, muscle activation detection
- Eye tracking: EOG, blink detection and analysis
- Multi-modal integration: Processing multiple physiological signals simultaneously
- Complexity analysis: Entropy measures, fractal dimensions, nonlinear dynamics
Core Capabilities
1. Cardiac Signal Processing (ECG/PPG)
Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.
Primary workflows:
- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment
- HRV analysis across time, frequency, and nonlinear domains
- PPG pulse analysis and quality assessment
- ECG-derived respiration extraction
Key functions:
import neurokit2 as nk
# Complete ECG processing pipeline
signals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)
# Analyze ECG data (event-related or interval-related)
analysis = nk.ecg_analyze(signals, sampling_rate=1000)
# Comprehensive HRV analysis
hrv = nk.hrv(peaks, sampling_rate=1000) # Time, frequency, nonlinear domains
2. Heart Rate Variability Analysis
Compute comprehensive HRV metrics from cardiac signals. See references/hrv.md for all indices and domain-specific analysis.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/bio_module.md 13 KB
- references/complexity.md 17 KB
- references/ecg_cardiac.md 11 KB
- references/eda.md 14 KB
- references/eeg.md 14 KB
- references/emg.md 12 KB
- references/eog.md 12 KB
- references/epochs_events.md 14 KB
- references/hrv.md 15 KB
- references/ppg.md 11 KB
- references/rsp.md 14 KB
- references/signal_processing.md 16 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 355 lines · 96 tokens per session scan A 836239d1510a
neurokit2 is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 96 tokens to every session and 2,846 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to neurokit2, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-deepchem
Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use when running end-to-end molecular ML experiments that need…
alterlab-hypogenic
Runs automated LLM-driven hypothesis generation and testing on tabular datasets with HypoGeniC, combining literature insights with data-driven testing. Use when systematically exploring hypotheses about patterns in empirical data (for example deception detection or content analysis). For manual hypothesis formulation…
alterlab-esm
Run ESM protein language models — ESM3 for generative multimodal protein design across sequence, structure, and function, and ESM C for efficient embeddings and representations — locally or via the cloud Forge API. Use when working with protein sequences, structures, or function prediction, designing novel proteins…
alterlab-molfeat
Featurizes molecules for machine learning with molfeat (100+ featurizers) — ECFP/MACCS/MAP4 fingerprints, RDKit and Mordred physicochemical descriptors, and pretrained embeddings (ChemBERTa, ChemGPT, GIN) exposed as scikit-learn transformers that convert SMILES into feature vectors. Use when turning molecules into…