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-mcp-guardgit 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-mcp-guard)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-mcp-guard"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-mcp-guard/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-mcp-guard"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-mcp-guard.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 | $0.00118 | $0.00858 |
| Opus 5 | $0.00059 | $0.00429 |
| Sonnet 5 | $0.00024 | $0.00172 |
| Haiku 4.5 | $0.00012 | $0.00086 |
Grade A, and why
mne-mcp-guard 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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE MCP Guard
Use this skill to make MNE MCP execution reliable, especially before expensive steps (ICA, time-frequency, source localization) and on unfamiliar data.
Workflow
- Check capability first —
mne_check_status. If MNE is missing, stop and say so; if scikit-learn is missing, ICA is unavailable. - Inspect before processing —
mne_get_info/mne_describe. Never guess channel names, sampling rate, montage, or event codes. - Look before parameterizing —
mne_plot_psd(read the PNG) before choosing filter cutoffs;mne_plot_rawbefore choosing rejection thresholds. - Smoke-test heavy steps
- Before ICA: confirm data is high-pass filtered (~1 Hz) and montage is set.
- Before epoching: verify real event codes exist (
mne_find_events/mne_events_from_annotations) and that the epoch window fits the recording. - Before TFR: confirm the epoch window is long enough for the lowest frequency.
- Escalate gradually — get a minimal version working (one condition, default params), then add options. Don't jump to a 60-component ICA or full source pipeline on the first try.
- Read warnings, not just success — dropped epochs, rank deficiency, montage subset warnings, and annotation notes change the validity of results.
Guardrails
- Units are SI (volts/tesla). A
rejectof100is 100 volts. 100 µV is100e-6. This is the most common silent error — verify any threshold's order of magnitude. - Set a montage before topomaps, ICA component plots, or interpolation.
- High-pass before ICA (~1 Hz) or components will be unstable.
- Don't assume a file loads in one call — BrainVision/EEGLAB need sidecar files; point at the header.
- Don't conclude "timeout = broken." ICA/TFR/large files are genuinely slow; raise
MNE_MCP_TIMEOUTand retry, or crop/decimate first.
Decision tree
- "no session object named ..."? →
mne_session_info; load the data or fix the name. - Plot/interpolation error mentioning positions/montage? →
mne_set_montage; check channel names match the montage. - ICA error or weird components? → confirm sklearn present, high-pass applied, and
n_components≤ data rank (lower it or use a0.99variance fraction). - TFR "wavelet longer than signal"? → wider epochs, higher
fmin, or smallern_cyclesviamne_run_code. - All epochs dropped / empty evoked? → loosen
reject_eeg, verify event codes, check the epoch window against recording length. - Step times out? → increase
MNE_MCP_TIMEOUT; for large raw usepreload=false+mne_crop.
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.
- 12d ago First seen · 63 lines · 118 tokens per session scan A 03507c43f6ea
mne-mcp-guard is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 118 tokens to every session and 858 once invoked, about $0.0006 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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