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-erpgit 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-erp)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-erp"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-erp/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-erp"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-erp.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 | $0.00216 | $0.02001 |
| Opus 5 | $0.00108 | $0.01001 |
| Sonnet 5 | $0.00043 | $0.00400 |
| Haiku 4.5 | $0.00022 | $0.00200 |
Grade A, and why
mne-erp 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE ERP / ERF Analysis (grill → analyze → critic)
Evoked-response analysis of neurophysiology data via the MNE MCP server. This skill is skeptical by design: most ERP mistakes (peak-picking a window after seeing the grand average, a high-pass that shifts latency, calling an occipital 100 ms deflection "N1") run without any error — so the discipline is to grill the component identity and measurement plan before averaging, and critique before believing.
Companion skills:
mne-mcp-guardfor technical execution safety;mne-methodology-criticfor Phase 3. Loaded objects persist in one MNE session.
PHASE 1 — GRILL (before averaging anything)
Do not average 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? (condition × condition, group × group, pre × post)
- Within- or between-subject? Paired or independent? n per cell?
- Confirmatory (component + window + electrodes pre-specified) or exploratory (whole-head, corrected)?
The questions that decide validity
- Which components, and what are their EXPECTED latency, topography, AND stimulus modality? Latency/polarity/topography must match the named component for that modality. ⚠️ auditory N1 is fronto-central (~100 ms); visual N1 is occipito-temporal ~150–200 ms — a ~100 ms occipital deflection in vision is most likely P1, not N1. A topography/modality mismatch means the component is misnamed. (This is the single most common identity error here.)
- Is the measurement window + electrode set PRE-SPECIFIED? A mean/peak amplitude measured in a window/channel chosen after seeing this dataset's grand average is circular (double-dipping). Pin the window and ROI down NOW, from prior literature, not from the data.
Data & parameters
- Baseline window (length + placement)? Baseline noise propagates into every component.
- Filter. ⚠️ high-pass > 0.3 Hz distorts slow components and shifts/inverts apparent latency and polarity (CDA, LPP, P300, CNV especially); low-pass smooths and delays peaks. State the band.
- Epoching:
tmin/tmax, peak-to-peak rejection threshold + its justification. - Reference, montage, channel selection, units.
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 · 136 lines · 216 tokens per session scan A 3115a196f98a
mne-erp is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 216 tokens to every session and 2,001 once invoked, about $0.0011 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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