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-advancedgit 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-advanced)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-advanced"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-advanced/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-advanced"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-advanced.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 | $0.00294 | $0.02218 |
| Opus 5 | $0.00147 | $0.01109 |
| Sonnet 5 | $0.00059 | $0.00444 |
| Haiku 4.5 | $0.00029 | $0.00222 |
Grade A, and why
mne-advanced 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Advanced / Rare Methods (grill → analyze → critic)
A hub for the non-standard analyses — microstates, entropy/complexity, graph metrics, the 1/f
deep-dive, intracranial specifics, fNIRS GLM, real-time. Each runs mostly via mne_run_code with an
external library that is an optional dependency (named per method below). This skill is extra
skeptical: a smaller literature means more researcher degrees of freedom and easier
over-interpretation, so the discipline is to grill appropriateness and parameter-sensitivity before
computing, frame as exploratory, and critique before believing.
Companion skills:
mne-mcp-guardfor technical execution safety;mne-methodology-criticfor Phase 3;mne-spectralfor the canonical PSD / aperiodic workflow. Loaded objects persist in one MNE session. Install the named library first (e.g.pip install pycrostates); flag it as an optional dependency to the user before running.
PHASE 1 — GRILL (before computing anything)
These methods are non-standard, so the intake is harsher. Do not compute until these are answered. If the user can't answer one, propose a sensible default and explicitly flag the open risk — never silently choose.
Appropriateness (the gate question)
- Is this method validated for this data type and question, or would a standard method answer it better? (e.g. "network hubs" via graph theory when a simple ROI contrast suffices; entropy as a vague "complexity" proxy when band power is the real hypothesis.) Justify choosing the rare tool.
- Confirmatory (metric + ROI/band pre-specified) or exploratory (then say so, and frame every number as hypothesis-generating)? With a small literature, default to exploratory.
Parameters & sensitivity (these methods are parameter-hungry)
- What are the key parameters, and how sensitive are results to them? Plan a sensitivity
sweep, not a single setting. Examples: microstates
n_maps(4–7) + GFP-peak selection; sample entropym,r, length; multiscale entropy scales; graph threshold / density; HFO detector thresholds; specparampeak_width_limits/aperiodic_mode.
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 · 148 lines · 294 tokens per session scan A 60c5355d144c
mne-advanced is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 294 tokens to every session and 2,218 once invoked, about $0.0015 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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