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 Exekiel179/MNE-MCP --skill mne-analystgit clone --depth 1 https://github.com/Exekiel179/MNE-MCPWrote 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/exekiel179/mne-mcp/mne-analyst)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-analyst"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-analyst/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/exekiel179/mne-mcp/mne-analyst"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-analyst.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 | $0.00267 | $0.01627 |
| Opus 5 | $0.00133 | $0.00813 |
| Sonnet 5 | $0.00053 | $0.00325 |
| Haiku 4.5 | $0.00027 | $0.00163 |
Grade A, and why
mne-analyst 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Analyst
Drive MNE-Python analysis conversationally through the MNE MCP server. The server keeps one
persistent session, so load a recording once and build the pipeline step by step. Every plotting
tool returns > Figure: <path> — read that PNG and interpret it before deciding the next step.
Quick start
mne_check_status # 1. confirm MNE (+ sklearn for ICA) is available
mne_load_raw path=... name=raw # 2. load
mne_set_montage name=raw # 3. positions (needed for topomaps/ICA/interpolation)
mne_plot_psd name=raw # 4. LOOK (read the PNG) → pick filter cutoffs / spot bad channels
Request → tools (routing)
| User wants | Call (in order) |
|---|---|
| Look at the data | mne_describe / mne_get_info, mne_plot_psd, mne_plot_raw |
| Clean / preprocess | mne_filter (+notch), mne_mark_bad_channels → mne_interpolate_bads, mne_set_reference |
| Remove eye/heart artifacts | mne_fit_ica (on ~1 Hz HP data) → mne_plot_ica_components → mne_apply_ica exclude=... |
| ERP / evoked | get events (mne_find_events or mne_events_from_annotations) → mne_make_epochs → mne_average_evoked → mne_plot_evoked / mne_plot_topomap |
| Time-frequency | mne_make_epochs (wide window) → mne_tfr_morlet |
| Decoding (MVPA) | mne_decode cond_a=… cond_b=… |
| Connectivity | mne_connectivity method=coh fmin=8 fmax=13 |
| Source localization (EEG) | mne_compute_noise_cov → mne_make_forward → mne_apply_inverse → mne_plot_source_estimate |
| Save | mne_save |
| BIDS / stats / anything else | mne_run_code (see references/mne-pipelines.md) |
Golden rules (prevent the common failures)
- ✅ Inspect first (
mne_get_info) — never guess channel names, montage, or event codes. - ✅ Set a montage before topomaps, ICA components, or interpolation.
- ✅ High-pass ~1 Hz before ICA; apply the resulting ICA to your ERP-filtered data.
- ✅ SI units: 100 µV =
reject_eeg=100e-6, not100. (The #1 silent error.) - ✅ Read the figure each plot returns; interpret it in plain language.
- ❌ Don't jump to a heavy step (ICA, TFR, source) before a quick sanity check.
- ❌ Don't use a short ERP window for TFR — Morlet needs a wider epoch (e.g.
tmin=-0.5 tmax=1.5).
What ships with it
3 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.
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 · 90 lines · 267 tokens per session scan A 58ddb117b8e1
mne-analyst is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 267 tokens to every session and 1,627 once invoked, about $0.0013 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 2026-08-31.
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