Agent skill
staff-training-health
Design healthcare staff training programs. TRIGGERS - Use when user needs help with staff-training-health related tasks.
ClaudeCodexOpenClawEducation & Research
lionelsimai36
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.
instructions.md
--- name: neuroscience-011626319cc3 description: 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)--- name: neuroscience-011626319cc3 description: 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)
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.