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-statsgit 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-stats)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-stats"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-stats/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-stats"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-stats.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 | $0.00234 | $0.02188 |
| Opus 5 | $0.00117 | $0.01094 |
| Sonnet 5 | $0.00047 | $0.00438 |
| Haiku 4.5 | $0.00023 | $0.00219 |
Grade A, and why
mne-stats 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Statistics (grill → analyze → critic)
Statistical inference on neurophysiology data via the MNE MCP server. This is the cross-cutting
skill: it consumes the outputs of mne-erp, mne-timefreq, mne-connectivity, and mne-source
and decides what can actually be claimed from them. It is skeptical by design: most statistical
mistakes (conflating cluster-level with point inference, an incomplete correction family, asserting
normality at small n) run without any error — so the discipline is to grill before testing and
critique before believing.
Companion skills:
mne-mcp-guardfor technical execution safety;mne-methodology-criticfor Phase 3. Loaded objects (evoked / TFR / connectivity / stc arrays) persist in one MNE session.
PHASE 1 — GRILL (before testing anything)
Do not run a test 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, vs a baseline / vs zero)
- Within- or between-subject? Paired or independent? n per cell, and is there power for it?
- Confirmatory (hypothesis + ROI/window/band pre-specified) or exploratory (whole grid, corrected)?
The two questions that decide validity
- What is the statistical FAMILY — exactly which dimensions are tested? Enumerate channels × times × freqs × ROIs × conditions. The correction must cover the entire family, including exploratory tests you ran but won't headline. An undercounted family is the single most common fatal error here.
- Cluster-level or point inference? ⚠️ A cluster-based permutation test licenses a claim about a cluster (a contiguous blob in space/time/freq), not about any specific channel, time, or frequency inside it. You may NOT say "Cz at 320 ms is significant" from a cluster test. If you need point/peak inference, you need a different, pre-specified test. Decide which claim you're making before you run.
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 · 153 lines · 234 tokens per session scan A 52489c239437
mne-stats is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 234 tokens to every session and 2,188 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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