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-cpp-analystgit 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-cpp-analyst)<a href="https://agentmods.dev/skills/exekiel179/mne-mcp/mne-cpp-analyst"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-cpp-analyst/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-cpp-analyst"><img src="https://agentmods.dev/badge/skills/exekiel179/mne-mcp/mne-cpp-analyst.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 | $0.00046 | $0.00785 |
| Opus 5.5 | $0.00018 | $0.00314 |
| Sonnet 5.5 | $0.00009 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
mne-cpp-analyst 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 27d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MNE-CPP Inspection
Installation Requests
Use Python 3.12+; no MNE-Python dependency is needed. For a source checkout,
install the light bridge with python -m pip install ./packages/mne-cpp-mcp.
Do not assume this preview has been published to PyPI.
When the user asks to install/configure it, identify the selected client and an
existing absolute data directory. Run with the selected environment interpreter:
python -m mne_cpp_mcp setup --data-dir "D:/research/data".
Replace the example directory with the user's choice. The console shortcut is
mne-cpp-mcp setup. Exactly one detected client is selected automatically;
otherwise add --clients with the user's choice, never all clients by default.
Setup reuses --bin-dir or MNE_CPP_BIN_DIR if provided; otherwise it reuses or
installs the verified official Windows x86_64 runtime, registers selected clients
and installs this skill. A broken configured runtime is an error, not permission
to reinstall. For existing native tools add --bin-dir "C:/tools/mne-cpp/bin". Other platforms require
user-managed native tools and are untested. --check is a read-only preflight;
exit code 1 for missing tools is expected. Offline --archive accepts the same
official ZIP and enforces the fixed checksum. The old --install-native flag and
python -m mne_cpp_mcp.server entrypoint remain compatible.
Do not bypass a checksum failure or overwrite an unmanaged native directory. Report backup paths and request a client restart. Client registration does not export environment variables into the current terminal. Missing-dependency diagnosis alone is not permission to install or rewrite client settings.
Analysis Requests
Check mne_cpp_check_status first. ready means verified inspection executables
are available, not that a complete scientific pipeline is supported. Use reported
capability limits even if upstream help advertises more.
mne_cpp_inspect_fiff: prefer block-only inspection; full metadata may contain participant information visible to the AI client.mne_cpp_read_metadata: sampling frequency and Nyquist in Hz, not a quality assessment or a safe cutoff without transition-band review.mne_cpp_evoked_summary: descriptive evoked metadata; nave counts averages and must not be treated as independent participants.mne_cpp_get_help: installed CLI help, not proof of scientific correctness.
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.
- 27d ago First seen · 65 lines · 46 tokens per session scan A 8c6f73f12cd9
mne-cpp-analyst is a skill published in the GitHub repository Exekiel179/MNE-MCP (10 stars, last pushed 5d ago), licensed MIT. It adds 46 tokens to every session and 785 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-22.
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…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports 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…
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…
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.
presenting-conference-talks
Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.