What is mne-connectivity?

An EEG, MEG, or intracranial-EEG analysis workflow for measuring functional connectivity, meaning how activity in different brain areas is related over time.

Use it to calculate measures such as coherence, phase locking, cross-frequency coupling, or directed influence in sensor or source space, then review the assumptions behind the results.

It makes hidden design risks explicit before analysis, such as signals appearing connected because they spread through tissue or share a reference.

mne-connectivity — Skill for Claude Code and Codex by Exekiel179

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-connectivity

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-connectivity/github.svg)](https://agentmods.dev/skills/exekiel179/mne-mcp/mne-connectivity)

Your own site

<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-connectivity"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-connectivity/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-connectivity"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-connectivity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 195 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,906 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	$0.00195	$0.01906
Opus 5.5	$0.00078	$0.00762
Sonnet 5.5	$0.00039	$0.00381
Haiku 4.5	$0.00019	$0.00191

Measured 1mo ago against content hash 5b3bbda3a421, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mne-connectivity 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-connectivity/SKILL.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MNE Connectivity (grill → analyze → critic)

Functional/effective connectivity of neurophysiology data via the MNE MCP server. This skill is skeptical by design: most connectivity mistakes (volume conduction inflating zero-lag coherence, trial-count/SNR bias, common-reference artifact) run without any error and produce a beautiful heatmap — so the discipline is to grill before computing and critique before believing.

Companion skills: mne-mcp-guard for technical execution safety; mne-methodology-critic for Phase 3. Loaded objects persist in one MNE session. Check mne_connectivity in the server environment before this workflow (mne-connectivity; PAC via pactools/tensorpac).

PHASE 1 — GRILL (before computing anything)

Do not compute connectivity until these are answered. If the user can't answer one, propose a sensible default and explicitly flag the open risk — never silently choose.

Design & claim

What is the hypothesis, and what is the comparison? (group × group, condition × condition, pre × post) Is the claim undirected (coupling) or directed/effective (who drives whom)?
Within- or between-subject? Paired or independent? n per cell?
Confirmatory (seed/edge/band pre-specified) or exploratory (all-to-all, corrected)?

The question that decides validity

Which measure, and WHY? ⚠️ Volume conduction / field spread makes a single source appear at many sensors with zero phase lag, which inflates coherence and PLV — they cannot tell true coupling from one spread-out source. For sensor-space EEG/MEG, prefer measures that discard the zero-lag component: imaginary coherence, wPLI, or PLI. (Using coh/PLV to claim genuine sensor connectivity without this caveat is the single most common fatal error here.)

Data & parameters

Sensor or source space? Anatomical claims ("frontoparietal coupling") need source space; sensor-space edges are between electrodes, not brain regions.
Reference? A common reference (and the average reference) injects a shared signal that inflates apparent connectivity; consider source space, current-source-density / Laplacian, or a reference-robust measure.
Frequency band(s) and width; epoching length (⇒ low-frequency resolution), rejection threshold.
For PAC: which phase band drives which amplitude band, and over what window?

Read the full file on GitHub · 132 lines

Files

What ships with it

2 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 · +4 lines 5b3bbda3a421
2mo ago First seen · 128 lines · 195 tokens per session scan A a31e133cb64d

Subscribe to this mod's changes

mne-connectivity is a skill published in the GitHub repository Exekiel179/MNE-MCP (12 stars, last pushed 23d ago), licensed MIT. It adds 195 tokens to every session and 1,906 once invoked, about $0.0008 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.

Scientific

mne-connectivity next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
mne-connectivityExekiel179/MNE-MCP	Skill	12	23d ago	MIT	A	195	Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	2d ago	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	yesterday	Apache-2.0	A	205	Claude Code