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 CUHK-AIM-Group/NeuroClaw --skill eeg-skillgit clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawWrote 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/cuhk-aim-group/neuroclaw/eeg-skill)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/eeg-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/eeg-skill/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/cuhk-aim-group/neuroclaw/eeg-skill"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/eeg-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00116 | $0.02399 |
| Opus 5 | $0.00058 | $0.01200 |
| Sonnet 5 | $0.00023 | $0.00480 |
| Haiku 4.5 | $0.00012 | $0.00240 |
Grade A, and why
eeg-skill 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EEG Skill (Modality Layer)
Overview
eeg-skill is the NeuroClaw modality-layer interface skill responsible for all EEG data processing tasks.
It strictly follows the NeuroClaw hierarchical design principles:
- This skill only describes WHAT needs to be done and which tool skill to delegate to.
- It contains no full implementation code.
- All concrete execution (MNE-Python calls, torchaudio, scipy, file I/O, etc.) is delegated to the dedicated base/tool skill
mne-eeg-tool. - Waveform-to-spectrogram conversion uses
torchaudio.transforms.MelSpectrogram. - Frequency-band energy extraction uses continuous wavelet transform (
scipy.signal.cwtwithmorlet2wavelet).
Core workflow (never bypassed):
- Identify the user-provided EEG files (BIDS, .set, .edf, .bdf, .fif, etc.).
- Generate a numbered execution plan that clearly states WHAT needs to be done and which tool skill will handle each step.
- Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation (“YES” / “execute” / “proceed”).
- On confirmation, delegate every step to
mne-eeg-toolviaclaw-shell. - After execution, save all outputs in a clean directory structure (
eeg_output/).
Research use only — outputs are for scientific analysis only.
Quick Reference (Common EEG Tasks – Updated 2026-03-25)
| Task | What needs to be done | Delegate to which tool skill | Expected output |
|---|---|---|---|
| Load & basic validation | Read raw EEG + channel locations + events + validation | claw-shell (via mne-eeg-tool) |
Validation report + raw object |
| Bad-channel detection & interpolation | Auto-detect + interpolate noisy channels | claw-shell (via mne-eeg-tool) |
Cleaned raw data |
| Downsampling + filtering | Resample, high-pass, notch, bandpass filtering | claw-shell (via mne-eeg-tool) |
Filtered .fif files |
| Artifact removal | ICA + AutoReject + EOG/ECG regression | claw-shell (via mne-eeg-tool) |
Cleaned data |
| Continuous data cleaning | Resting-state pipeline (no events) | claw-shell (via mne-eeg-tool) |
Cleaned continuous data |
| Re-referencing & epoching | Average reference (CAR) / REST + epoching + baseline correction | claw-shell (via mne-eeg-tool) |
Epoched .fif files |
| Waveform to Mel-Spectrogram | Convert raw waveform to Mel spectrogram using torchaudio | claw-shell (via mne-eeg-tool) |
Mel-spectrogram tensors (.pt) |
| Frequency-band energy extraction | Extract δ/θ/α/β/γ band energy using CWT with morlet2 wavelet | claw-shell (via mne-eeg-tool) |
Per-band power matrices (CSV / .npy) |
| Feature extraction (core) | Band power, CSP, Hjorth, sample entropy | claw-shell (via mne-eeg-tool) |
Feature matrices (CSV / .npy / .npz) |
| Advanced features | Functional connectivity, ERP peaks/latency/AUC, frontal alpha asymmetry, microstates | claw-shell (via mne-eeg-tool) |
Connectivity matrices, ERP CSV, asymmetry .npy, microstates .fif |
| Full end-to-end pipeline | Any combination of the above for BCI, emotion, epilepsy, fatigue, etc. | claw-shell + dependency-planner |
Complete processed dataset + QC report |
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.
- 11d ago First seen · 197 lines · 116 tokens per session scan A b7565ce13fbc
eeg-skill is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 4d ago), licensed MIT. It adds 116 tokens to every session and 2,399 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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