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 agentlas-ai/agentlas-desktop --skill neurokit2git clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/neurokit2)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/neurokit2"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/neurokit2"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.00063 | $0.03503 |
| Opus 5.5 | $0.00025 | $0.01401 |
| Sonnet 5.5 | $0.00013 | $0.00701 |
| Haiku 4.5 | $0.00006 | $0.00350 |
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
100% identical to neurokit2 — 30 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeuroKit2
Scope and evidence cutoff
Use this skill for method-aware, reproducible biosignal research with NeuroKit2. The snapshot was checked on 2026-07-23 against:
- stable PyPI 0.2.13, released 2026-03-02;
- Python metadata (
>=3.10; classifiers 3.10–3.14) and wheel dependencies; - GitHub release notes/tags,
NEWS.rst, source at tagv0.2.13; - official API pages/examples (the live site identified itself as
0.2.13.dev214); and - pinned 0.2.13 runtime signatures and synthetic output schemas.
The live documentation can be ahead of the stable wheel. Prefer the pinned runtime for reproducible work and name both versions if consulting development docs.
Boundary
NeuroKit2 is a research and educational toolbox. Do not present its output as:
- a diagnosis, treatment recommendation, patient-monitoring decision, or alarm;
- validation, certification, or regulatory evidence for a medical device; or
- proof that a physiological construct is measured validly in a new sensor, protocol, environment, population, or disease group.
Validate acquisition hardware, electrode/optode placement, units, sampling and clock accuracy, preprocessing, detector/decomposition method, population, task, and outcomes for the intended study. Preserve raw data and an auditable exclusion log. Use deidentified local files only; do not place PHI in prompts, logs, examples, or bundled fixtures.
Reproducible installation
uv pip install "neurokit2==0.2.13"
For optional features, create a uv project, add only the packages actually required at
reviewed exact versions, and commit/review the resulting uv.lock before
uv sync --locked. NeuroKit2 exposes an upstream full extra, but this skill
intentionally does not install that floating transitive set in an automated workflow.
Optional capabilities can require MNE, cvxopt, Plotly, PyEMD, pyRQA, Pillow, OpenCV,
or file readers. Record the resolved environment with the analysis. Provision any MNE
data/template download as an explicit, checksummed study input. Do not install a moving
development branch for a reproducible study.
What ships with it
19 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 7.5 KB
- references/complexity.md 7.3 KB
- references/ecg_cardiac.md 6.7 KB
- references/eda.md 6.8 KB
- references/eeg.md 7.1 KB
- references/emg.md 5.6 KB
- references/eog.md 5.2 KB
- references/epochs_events.md 6.9 KB
- references/hrv.md 8.2 KB
- references/ppg.md 6.7 KB
- references/rsp.md 6.7 KB
- references/signal_processing.md 6.7 KB
- scripts/_common.py 19 KB runs code
- scripts/ecg_hrv_pipeline.py 11 KB runs code
- scripts/eda_pipeline.py 9.8 KB runs code
- scripts/generate_synthetic.py 7.4 KB runs code
- scripts/inspect_signal.py 13 KB runs code
- scripts/plan_epochs.py 9.8 KB runs code
- scripts/validate_multimodal.py 12 KB runs code
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 · 341 lines · 63 tokens per session scan A 0d4307e60864
neurokit2 is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 63 tokens to every session and 3,503 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to neurokit2, differing in 30 lines, and is treated as a copy.
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