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 dralkh/seerai --skill neurokit2git clone --depth 1 https://github.com/dralkh/seeraiWrote 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/dralkh/seerai/neurokit2)<a href="https://agentmods.dev/skills/dralkh/seerai/neurokit2"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/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/dralkh/seerai/neurokit2"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/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 | $0.02857 |
| Opus 5.5 | $0.00038 | $0.01143 |
| Sonnet 5.5 | $0.00019 | $0.00571 |
| Haiku 4.5 | $0.00010 | $0.00286 |
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 1mo 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 — 10 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 — 354 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.
- 1mo ago First seen · 354 lines · 96 tokens per session scan A 7ee34a1dcc21
neurokit2 is a skill published in the GitHub repository dralkh/seerai (94 stars, last pushed 20d ago), licensed MIT. It adds 96 tokens to every session and 2,857 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to neurokit2, differing in 10 lines, and is treated as a copy.
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