A tool for analyzing neurophysiology recordings such as EEG, MEG, intracranial EEG, ECoG, and fNIRS through MNE-Python, a scientific software library. It supports cleaning recordings, removing artifacts, finding event-related responses, and plotting results.

Loading recordings, filtering and re-referencing them, marking or interpolating bad channels, removing eye and heart artifacts with ICA, creating epochs, averaging responses, and examining time-frequency data.

It lets an agent guide a data-analysis workflow while keeping a persistent session and inspecting plots before choosing the next processing step.

mne-analyst — Skill for Claude Code and Codex by Exekiel179/MNE-MCP

Install

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.

Any agent

npx skills add Exekiel179/MNE-MCP --skill mne-analyst

Clone the repo

git clone --depth 1 https://github.com/Exekiel179/MNE-MCP

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-analyst/github.svg)](https://agentmods.dev/skills/exekiel179/mne-mcp/mne-analyst)

Your own site

<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.

Your own site · 80×15

<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>

Per session 267 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,792 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01792
Opus 5.5	$0.00107	$0.00717
Sonnet 5.5	$0.00053	$0.00358
Haiku 4.5	$0.00027	$0.00179

Measured 1mo ago against content hash 41bb60ed6003, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

skills/mne-analyst/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 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-MCP is a lightweight interface: skills ship with it, scientific libraries do not. Before a workflow, read environment checks. Use the server's reported Python, not the agent terminal's default interpreter, when diagnosing dependencies.

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_compute_tfr`; legacy quick path: `mne_tfr_morlet`
Decoding (MVPA)	`mne_decode cond_a=… cond_b=…`
Connectivity	`mne_compute_connectivity` for bands/pairs/estimator settings; `mne_connectivity` for one-band quick calls
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)

Read the full file on GitHub · 100 lines

Files

What ships with it

5 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.

Changes

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 Changed · +10 lines 41bb60ed6003
2mo ago First seen · 90 lines · 267 tokens per session scan A 58ddb117b8e1

Subscribe to this mod's changes

mne-analyst is a skill published in the GitHub repository Exekiel179/MNE-MCP (12 stars, last pushed 23d ago), licensed MIT. It adds 267 tokens to every session and 1,792 once invoked, about $0.0011 per session on Opus 5.5. 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.

Data and AI Scientific

mne-analyst next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
mne-analystExekiel179/MNE-MCP	Skill	12	23d ago	MIT	A	267	Claude Code, Codex
arboretoK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	55	Claude Code, Codex
deepspot-mK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	70	Claude Code
torchdrugK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	64	Claude Code
pyhealthK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	84	Claude Code, Codex
nemo-mbridge-perf-expert-parallel-overlapNVIDIA/skills	Skill	3.9k	yesterday	Apache-2.0	A	56	Claude Code
ml-training-recipesOrchestra-Research/AI-Research-SKILLs	Skill	14k	27d ago	MIT	A	88	Claude Code