How do I install the Neuroscience skill?

Run npx skillmds@latest add beita6969/neuroscience in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Neuroscience safe to use?

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Which AI agents work with Neuroscience?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Neuroscience free to use?

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Who published Neuroscience?

beita6969 (@beita6969) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Neuroscience by beita6969 — Agent Skill

Name: Neuroscience
Rating: 4.2 (5 reviews)
Author: beita6969

Neuroscience

Supports neuroscience research including brain imaging analysis (fMRI, EEG), neural circuit modeling, cognitive experiment design, and neurological disorder investigation; trigger when users discuss brain regions, neural signals, cognitive tasks, or neuroimaging data.

beita6969 Updated Sep 9, 2026

File contents

When to Trigger

Activate this skill when the user mentions:

fMRI, EEG, MEG, PET, MRI brain imaging
Neural circuits, synaptic transmission, neurotransmitters
Cognitive experiments, reaction time, psychophysics
Brain regions, Brodmann areas, connectome
Neurological disorders (Alzheimer's, Parkinson's, epilepsy)
Computational neuroscience, spiking neural networks, Hodgkin-Huxley
Brain-computer interfaces (BCI), neural decoding

Step-by-Step Methodology

Define the neuroscience question - Specify level of analysis (molecular, cellular, circuit, systems, cognitive, behavioral). Identify target brain regions or networks.
Experimental design - For imaging studies: specify modality (fMRI for spatial resolution, EEG for temporal resolution, PET for neurochemistry). Design task paradigm with proper controls, counterbalancing, and trial timing (ISI, ITI).
Data acquisition guidance - Recommend acquisition parameters: fMRI (TR, voxel size, field strength), EEG (sampling rate, electrode montage, impedance thresholds). Specify preprocessing steps.
Preprocessing - fMRI: slice timing, motion correction, normalization (MNI/Talairach), smoothing. EEG: filtering (bandpass), artifact rejection (ICA for eye blinks/muscle), re-referencing. Always report each step and parameters.
Analysis - fMRI: GLM for activation, seed-based or ICA for connectivity, MVPA for decoding. EEG: ERP analysis, time-frequency decomposition, source localization. Computational models: implement and fit biophysical or phenomenological models.
Statistical inference - Apply appropriate correction for multiple comparisons: cluster-level FWE for fMRI, permutation-based corrections for EEG. Report effect sizes. Use Bayesian approaches when frequentist results are ambiguous.
Interpretation - Map results to known neuroanatomy (use atlases: AAL, Desikan-Killiany, Schaefer). Discuss findings in context of established theoretical frameworks. Avoid reverse inference pitfalls.

Key Databases and Tools

NeuroSynth / Neuroquery - Meta-analytic functional maps
Allen Brain Atlas - Gene expression and connectivity
OpenNeuro - Open neuroimaging datasets
BrainMap - Functional neuroimaging database
SPM / FSL / AFNI / FreeSurfer - Neuroimaging analysis software
MNE-Python / EEGLAB - EEG/MEG analysis tools
NEURON / Brian2 - Neural simulation environments

Output Format

Brain activation maps with MNI coordinates (x, y, z), cluster size, peak t/z-value.
ERP waveforms with component labels (N1, P3, N400), latency, and amplitude.
Time-frequency plots with frequency bands labeled (delta, theta, alpha, beta, gamma).
Computational model parameters with biological interpretation.

Quality Checklist

Brain coordinates in standard space (MNI or Talairach) with atlas labels
Multiple comparison correction method specified and justified
Sample size adequate for imaging modality (power analysis cited)
Preprocessing pipeline fully documented (software version, parameters)
Task design includes appropriate controls and counterbalancing
Effect sizes reported alongside statistical significance
Reverse inference explicitly avoided or qualified
Raw data sharing or availability discussed (OpenNeuro, BIDS format)

---name: neurosciencedescription: Supports neuroscience research including brain imaging analysis (fMRI, EEG), neural circuit modeling, cognitive experiment design, and neurological disorder investigation; trigger when users discuss brain regions, neural signals, cognitive tasks, or neuroimaging data.--- ## When to Trigger Activate this skill when the user mentions: - fMRI, EEG, MEG, PET, MRI brain imaging - Neural circuits, synaptic transmission, neurotransmitters - Cognitive experiments, reaction time, psychophysics - Brain regions, Brodmann areas, connectome - Neurological disorders (Alzheimer's, Parkinson's, epilepsy) - Computational neuroscience, spiking neural networks, Hodgkin-Huxley - Brain-computer interfaces (BCI), neural decoding ## Step-by-Step Methodology 1. **Define the neuroscience question** - Specify level of analysis (molecular, cellular, circuit, systems, cognitive, behavioral). Identify target brain regions or networks. 2. **Experimental design** - For imaging studies: specify modality (fMRI for spatial resolution, EEG for temporal resolution, PET for neurochemistry). Design task paradigm with proper controls, counterbalancing, and trial timing (ISI, ITI). 3. **Data acquisition guidance** - Recommend acquisition parameters: fMRI (TR, voxel size, field strength), EEG (sampling rate, electrode montage, impedance thresholds). Specify preprocessing steps. 4. **Preprocessing** - fMRI: slice timing, motion correction, normalization (MNI/Talairach), smoothing. EEG: filtering (bandpass), artifact rejection (ICA for eye blinks/muscle), re-referencing. Always report each step and parameters. 5. **Analysis** - fMRI: GLM for activation, seed-based or ICA for connectivity, MVPA for decoding. EEG: ERP analysis, time-frequency decomposition, source localization. Computational models: implement and fit biophysical or phenomenological models. 6. **Statistical inference** - Apply appropriate correction for multiple comparisons: cluster-level FWE for fMRI, permutation-based corrections for EEG. Report effect sizes. Use Bayesian approaches when frequentist results are ambiguous. 7. **Interpretation** - Map results to known neuroanatomy (use atlases: AAL, Desikan-Killiany, Schaefer). Discuss findings in context of established theoretical frameworks. Avoid reverse inference pitfalls. ## Key Databases and Tools - **NeuroSynth / Neuroquery** - Meta-analytic functional maps - **Allen Brain Atlas** - Gene expression and connectivity - **OpenNeuro** - Open neuroimaging datasets - **BrainMap** - Functional neuroimaging database - **SPM / FSL / AFNI / FreeSurfer** - Neuroimaging analysis software - **MNE-Python / EEGLAB** - EEG/MEG analysis tools - **NEURON / Brian2** - Neural simulation environments ## Output Format - Brain activation maps with MNI coordinates (x, y, z), cluster size, peak t/z-value. - ERP waveforms with component labels (N1, P3, N400), latency, and amplitude. - Time-frequency plots with frequency bands labeled (delta, theta, alpha, beta, gamma). - Computational model parameters with biological interpretation. ## Quality Checklist - [ ] Brain coordinates in standard space (MNI or Talairach) with atlas labels - [ ] Multiple comparison correction method specified and justified - [ ] Sample size adequate for imaging modality (power analysis cited) - [ ] Preprocessing pipeline fully documented (software version, parameters) - [ ] Task design includes appropriate controls and counterbalancing - [ ] Effect sizes reported alongside statistical significance - [ ] Reverse inference explicitly avoided or qualified - [ ] Raw data sharing or availability discussed (OpenNeuro, BIDS format)

Frequently asked questions