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-sourcegit 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-source)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-source"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-source/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-source"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-source.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 | $0.00241 | $0.02161 |
| Opus 5 | $0.00120 | $0.01081 |
| Sonnet 5 | $0.00048 | $0.00432 |
| Haiku 4.5 | $0.00024 | $0.00216 |
Grade A, and why
mne-source 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 10d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Source Localization (grill → analyze → critic)
Source (inverse) modeling of neurophysiology data via the MNE MCP server. This skill is skeptical by design: the inverse problem is ill-posed — many source configurations explain the same sensor data — so every estimate depends on choices (head model, covariance, regularization, method) that all run without any error and silently shape "where" the activity is. The single biggest trap is a quantitative source claim resting on a template head with no individual MRI — so the discipline is to grill the model before computing and critique the localization before believing.
Companion skills:
mne-mcp-guardfor technical execution safety;mne-methodology-criticfor Phase 3. Loaded objects persist in one MNE session. Source tools need the[full]extra (nibabelfor the forward model,pyvistafor rendering).
PHASE 1 — GRILL (before computing anything)
Do not build a forward model or apply an inverse until these are answered. If the user can't answer one, propose a sensible default and explicitly flag the open risk — never silently choose.
The question that decides validity
- Template head (fsaverage) or individual MRI? ⚠️ The MCP forward model uses fsaverage — a template head. That makes any source estimate exploratory / qualitative: it cannot support a quantitative anatomical claim ("the generator is in left BA44"). Quantitative localization needs an individual MRI + BEM + co-registration. (This is the single most common fatal overreach here.)
Geometry & co-registration
- Are electrode positions digitized and co-registered to the head, or nominal (idealized montage on a template)? Nominal positions add localization error on top of the template-head error.
- EEG or MEG? EEG source localization is harder (volume conduction, skull-conductivity uncertainty) and is more easily overinterpreted than MEG.
Noise covariance (drives the whitening — get it wrong and the map is wrong)
- Source: pre-stimulus baseline (for evoked) or empty-room (MEG)? Enough samples to estimate it stably (rank!)? Was the data rank-reduced by average reference / interpolation / ICA — and does the covariance reflect that rank?
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.
- 10d ago First seen · 144 lines · 241 tokens per session scan A 2fb75ef5ca3a
mne-source is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 241 tokens to every session and 2,161 once invoked, about $0.0012 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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