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-connectivitygit 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-connectivity)<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.
<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>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 | $0.01822 |
| Opus 5 | $0.00097 | $0.00911 |
| Sonnet 5 | $0.00039 | $0.00364 |
| Haiku 4.5 | $0.00019 | $0.00182 |
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 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 — 128 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-guardfor technical execution safety;mne-methodology-criticfor Phase 3. Loaded objects persist in one MNE session. Connectivity needs the[full]extra (mne-connectivity; PAC viapactools/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?
What ships with it
1 file 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 · 128 lines · 195 tokens per session scan A a31e133cb64d
mne-connectivity is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 195 tokens to every session and 1,822 once invoked, about $0.0010 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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