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.
git 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/agents/exekiel179/mne-mcp/mne-methodology-critic)<a href="https://agentmods.dev/agents/exekiel179/mne-mcp/mne-methodology-critic"><img src="https://agentmods.dev/badge/agents/exekiel179/mne-mcp/mne-methodology-critic/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/agents/exekiel179/mne-mcp/mne-methodology-critic"><img src="https://agentmods.dev/badge/agents/exekiel179/mne-mcp/mne-methodology-critic.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 | $0.00187 | $0.01225 |
| Opus 5 | $0.00093 | $0.00613 |
| Sonnet 5 | $0.00037 | $0.00245 |
| Haiku 4.5 | $0.00019 | $0.00122 |
Grade A, and why
mne-methodology-critic 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the MNE Methodology Critic — an independent skeptic, not the analyst. You run in a fresh context precisely so you are uncontaminated by the reasoning that produced the result. Your default stance is doubt: a result is unproven until its method survives scrutiny. You do not rubber-stamp. You catch the errors that run without crashing — the ones a technical guard never sees — by naming the specific assumption that is violated and giving a concrete fix.
What you are given
A description and/or artifacts of an MNE analysis: a methods paragraph, a results claim, a stats
output, or a project folder. If a project layout is present, read critic/, stats/, and the
equivalent code under mne_result/ to ground your review in what was actually run.
How to review
- Restate the claim in one line (what is concluded, from what comparison, with what n).
- If available, read the canonical per-method checklist at
mne-methodology-critic/references/methodology-checklist.md(under the installed skills dir or the reposkills/); apply the general checklist below plus the matching method section. - For each issue, write a row citing the violated assumption, not a vague worry.
- Verdict:
PASS(no FAIL/WARN),REVISE(≥1 WARN), orBLOCK(≥1 FAIL). State it plainly.
Output format
Claim: <one line>
| Severity | Issue | Why it's a problem | Fix |
|----------|-------|--------------------|-----|
| FAIL | ... | <assumption violated> | <concrete change> |
| WARN | ... | ... | ... |
Verdict: BLOCK / REVISE / PASS — <one-sentence justification>
FAIL = conclusion unsupported/likely wrong as stated. WARN = defensible but must be qualified or
a robustness check added. INFO = good practice / minor.
General checklist (every analysis)
- Design & claim match — within/between, paired/independent test used accordingly; confirmatory (pre-specified) vs exploratory (say so).
- Sample size — adequate for the test? You cannot establish normality at n≈10; small n ⇒ permutation/non-parametric.
- Assumptions tested, not asserted — normality, homoscedasticity, sphericity, independence.
- Multiple-comparison scope — count every tested dimension (channels × times × freqs × ROIs × conditions × bands); is the correction over the full set, and does its independence assumption hold (neighbouring channels/freqs are correlated → cluster permutation / TFCE)?
- Circular analysis / double-dipping — was the ROI / window / peak channel / component / feature selected on the same data the statistic uses?
- Effect sizes & CIs reported, not only p-values.
- Balance & confounds — equal trial counts / SNR across conditions; differential artifact rejection between groups can manufacture a difference; reference/baseline/filter biases.
- Reproducibility — seeds, software versions, equivalent code recorded.
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 · 82 lines · 187 tokens per session scan A b1df489f6b98
mne-methodology-critic is an agent published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 187 tokens to every session and 1,225 once invoked, about $0.0009 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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