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-preprocessgit 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-preprocess)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-preprocess"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-preprocess/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-preprocess"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-preprocess.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 | $0.00276 | $0.02190 |
| Opus 5 | $0.00138 | $0.01095 |
| Sonnet 5 | $0.00055 | $0.00438 |
| Haiku 4.5 | $0.00028 | $0.00219 |
Grade A, and why
mne-preprocess 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 10d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE Preprocessing (grill → analyze → critic)
Preprocessing & data-quality cleanup of neurophysiology data via the MNE MCP server. This skill is skeptical by design: nearly every preprocessing mistake (a too-aggressive high-pass that eats your slow ERP, a reference that fabricates connectivity, filtering before epoching, differential bad-channel handling between groups) runs without any error and silently biases everything downstream — so the discipline is to grill the downstream analysis before cleaning, and critique the pipeline before believing.
Companion skills:
mne-mcp-guardfor technical execution safety;mne-methodology-criticfor Phase 3. Loaded objects persist in one MNE session, and preprocessing edits are in place.
PHASE 1 — GRILL (before processing anything)
Do not filter, reference, or resample until these are answered. If the user can't answer one, propose a sensible default and explicitly flag the open risk — never silently choose.
The question that decides every parameter
- What is the DOWNSTREAM analysis? Filter choices follow from it, and there is no neutral default: 0.1 Hz high-pass for ERP (slow components survive), ~1 Hz high-pass for ICA (drift hurts decomposition), broadband / no/low high-pass for TFR / low-frequency power. Set the pipeline to the analysis, not the other way round.
Filtering
- High-pass edge — and ⚠️ will the filter edge distort the effect of interest? A 0.5–1 Hz HP can attenuate/shift slow ERP components (CNV, P300, readiness potential). Justify the edge.
- Low-pass edge (anti-alias / smoothing) and transition bandwidth; FIR (linear-phase, default) vs IIR (causal, ringing tradeoffs)?
- Line frequency: 50 or 60 Hz? (region-dependent) — notch it (+ harmonics) or rely on a low-pass below it?
Resampling
- Needed at all? If so, before or after epoching? Downsample after epoching where possible — resampling continuous data jitters event sample positions. New sfreq must stay > 2× the low-pass (anti-aliasing) — MNE low-passes on resample, but verify.
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.
- 10d ago First seen · 154 lines · 276 tokens per session scan A 592d16b40ff7
mne-preprocess is a skill published in the GitHub repository Exekiel179/MNE-MCP (7 stars, last pushed 2mo ago), licensed MIT. It adds 276 tokens to every session and 2,190 once invoked, about $0.0014 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…