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-artifactsgit 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-artifacts)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-artifacts"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-artifacts/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-artifacts"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-artifacts.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 | $0.00213 | $0.01683 |
| Opus 5 | $0.00106 | $0.00842 |
| Sonnet 5 | $0.00043 | $0.00337 |
| Haiku 4.5 | $0.00021 | $0.00168 |
Grade A, and why
mne-artifacts 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Artifact Correction (grill → analyze → critic)
Artifact correction of neurophysiology data via the MNE MCP server. This skill is skeptical by design: a bad decontamination runs without any error — ICA happily over-fits, removes neural signal, or is fit on the wrong data — so the discipline is to grill the artifact inventory before cleaning and critique the cleaned data before believing.
Companion skills:
mne-mcp-guardfor technical execution safety (ICA convergence, units); andmne-methodology-criticfor Phase 3. Loaded objects persist in one MNE session.
PHASE 1 — GRILL (before cleaning anything)
Do not fit ICA or reject anything until these are answered. If the user can't answer one, propose a sensible default and explicitly flag the open risk — never silently choose.
Artifact inventory & claim
- Which artifacts are actually present — blink, saccade, ECG, muscle, line noise, electrode pop, drift? (Look first; don't assume.) Do EOG/ECG reference channels exist?
- What downstream analysis is this for, and could cleaning bias the comparison? (e.g. removing an ECG component differently across groups)
The two questions that decide validity
- Is ICA fit on a ~1 Hz high-passed COPY? ICA assumes stationarity; slow drifts make components unstable. Fit on a 1 Hz high-passed copy, then apply the unmixing to the 0.1 Hz data you actually analyze. (This is the single most common fatal error here.)
- Is
n_components≤ the data rank? Asking for more components than the rank yields unstable, uninterpretable components. ⚠️ An average reference and each interpolated channel REDUCE rank by ≥1 — count them.
Identification & selection
- How are artifact components identified — objectively (EOG/ECG correlation, ICLabel) or by eye? Subjective selection is not reproducible.
- How many components removed, and is the rule fixed across subjects (same threshold/labeller), or hand-picked per subject?
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 · 116 lines · 213 tokens per session scan A 4d94e278ba15
mne-artifacts is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 213 tokens to every session and 1,683 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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