awesome-cognitive-and-neuroscience-skills

Plugin awesome-cognitive-and-neuroscience-skills from marketplace awesome-cognitive-and-neuroscience-skills in NeuroAIHub/awesome_cognitive_and_neuroscience_skills. Commands read from the repo's manifest — 1 month ago.

Skills (50)

• act-r-model-builder

  Guides ACT-R cognitive model construction: chunk types, production rules, subsymbolic parameters, and model validation

• alternative-uses-task-designer

  Domain-validated guidance for designing Alternative Uses Task (AUT) experiments measuring divergent thinking, with parameters for AI-augmented and traditional conditions

• bayesian-cognitive-model-builder

  Domain-validated guidance for building hierarchical Bayesian cognitive models with Stan/PyMC: prior specification, model structure, MCMC diagnostics, and posterior predictive checks

• brain-connectivity-modeler

  Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory

• calcium-imaging-analysis-guide

  Domain-validated pipeline guidance for calcium imaging data analysis: motion correction, ROI extraction, neuropil correction, spike inference, and quality control

• cognitive-paradigm-design

  Expert guidance for selecting and parameterizing cognitive psychology experimental paradigms based on research questions

• cogsci-power-analysis

  Domain-specific statistical power analysis guidance for cognitive and neuroscience research, encoding effect size priors and sample size recommendations by modality

• cogsci-statistics

  Domain-specific statistical modeling guidance for cognitive science and neuroscience, encoding when and how to apply mixed models, correction methods, Bayesian approaches, and effect size reporting

• cogsci-visualization

  Domain-specific visualization best practices for cognitive and neuroscience data, encoding plot type selection, color standards, and publication formatting

• contribute-skill

  One-command skill contribution — generate a SKILL.md from your domain expertise and submit to GitHub Issues for maintainer review

• contribute-skills-via-pr

  Step-by-step guidance for contributing a new skill to the NeuroAIHub/awesome_cognitive_and_neuroscience_skills repository via GitHub Pull Request, including SKILL.md format requirements, quality rules, and PR checklist

• creativity-self-efficacy-mediation

  Domain-validated guidance for SEM-based mediation analysis of creative self-efficacy and moderation by baseline creativity in AI-augmented creativity research

• deeplabcut

  Toolbox for markerless animal pose estimation with DeepLabCut. Covers single/multi-animal tracking, SuperAnimal pretrained models, 2D/3D pose estimation, keypoint labeling GUI, model training/evaluation, video analysis, and behavioral quantification. Use when the user needs animal pose estimation, b

• divergent-thinking-scoring

  Domain-validated multi-dimensional scoring system for divergent thinking tasks, including fluency, flexibility, originality, and automated semantic distance methods

• drift-diffusion-model

  Expert guidance on selecting, fitting, and evaluating drift-diffusion models for two-choice response time data in cognitive science

• eeg-paradigm-designer

  Expert guidance for designing EEG paradigms optimized to isolate specific ERP components, with domain-validated timing, trial count, and control condition parameters

• eeg-preprocessing-pipeline-guide

  Guides EEG preprocessing: filtering, artifact rejection (ICA/ASR), re-referencing, interpolation

• erp-analysis

  Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing

• evidence-accumulation-selector

  Advises on when to use DDM vs. LBA vs. race models for choice-RT data based on experimental design and research goals

• fmri-glm-analysis-guide

  Domain-validated guidance for fMRI General Linear Model specification: HRF modeling, design matrix construction, contrast definition, confound regression, and statistical inference

• fmri-preprocessing-pipeline-guide

  Domain-validated guidance for fMRI preprocessing decisions: motion correction, slice timing, spatial normalization, smoothing, confound regression, and quality control

• fmri-task-design-guide

  Guides fMRI task design: block vs. event-related vs. mixed; jittering; contrasts; power for BOLD detection

• fmriprep

  Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn. Use this skill whenever the user asks to preprocess fMRI/BOLD data, run fMRIPrep on a BIDS dataset, set up Docker/Singularity/Apptainer contai

• infant-looking-time-designer

  Designs habituation and preferential-looking paradigms with age-appropriate timing parameters and exclusion criteria

• lesion-symptom-mapping-guide

  Advises on lesion-symptom mapping methods: VLSM, disconnection analysis, network lesion mapping

• markdown-report-writing

  Guide AI agents to write beautifully formatted, well-illustrated Markdown reports with proper structure, diagrams, and compatibility across GitHub and Obsidian. Use this skill whenever the user asks you to write a report, project documentation, experiment report, README, technical document, or any l

• mne-python-guide

  Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency decomposition, source localization, decoding/MVPA, statistical testing, simulation, and visualization. Use this s

• netneurotools-guide

  Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical surface visualization. Use this skill whenever the user works with brain connectivity matrices, connecto

• neural-decoding-analysis

  Domain-validated methods and decision logic for neural decoding, RSA, temporal generalization, and encoding models in systems neuroscience

• neural-population-analysis-guide

  Guides dimensionality reduction and latent-variable analysis of neural populations (PCA, GPFA, dPCA)

• neuroimaging-power-guide

  Sample-size planning for fMRI/EEG studies using effect-size benchmarks and simulation-based power

• neuroimaging-sample-size-calculator

  Simulation-based sample-size planning for neuroimaging studies using effect-size maps

• neuropsych-battery-selector

  Domain-validated decision logic for selecting neuropsychological test batteries matched to suspected cognitive deficit profiles

• optogenetics-protocol-designer

  Domain-validated decision logic for optogenetic stimulation parameter selection, including opsin choice, light delivery, pulse protocols, fiber placement, and control conditions

• paper-to-skill

  Interactive skill that guides extraction of research paradigms and methodological techniques from cognitive science papers into structured, reusable skills

• parameter-recovery-checker

  Guides parameter recovery studies to validate model identifiability before trusting fitted parameter values

• pycortex-guide

  Domain-validated guidance for cortical surface visualization and brain surface rendering of fMRI data using pycortex: data types (Volume, Vertex, Dataset), 2D cortical flatmaps, 3D WebGL brain viewers, volume-to-surface mapping, FreeSurfer/fMRIPrep integration, ROI management, and surface analysis.

• reading-time-analysis

  Guides analysis of eye-tracking reading measures including first fixation, gaze duration, regression path, and total reading time

• repo-to-skill

  Convert a GitHub repository or local codebase into a well-structured Claude Code skill with progressive disclosure. Use this skill whenever the user provides a GitHub URL or local repo path and asks to turn it into a skill, create a skill from a repo, or convert a library/tool/framework into reusabl

• research-literacy

  Core scientific methodology principles: research planning, method justification, assumption checking, and human-in-the-loop decision making for cognitive science and neuroscience

• self-paced-reading-designer

  Expert guidance for designing self-paced reading experiments: region segmentation, timing parameters, comprehension probes, and spillover analysis

• sentence-stimulus-norming

  Specifies norming procedures for linguistic stimuli including cloze probability, plausibility ratings, acceptability judgments, and lexical controls

• share-case

  One-command community case sharing — capture research context from your session and submit to GitHub Discussions

• share-usage

  Generate and share anonymized skill usage statistics to help the community understand which skills are most valuable

• signal-detection-analysis

  Domain-validated decision logic, formulas, and interpretation guidelines for applying Signal Detection Theory to cognitive science data

• spikeinterface-skill

  Domain knowledge for building extracellular electrophysiology pipelines with SpikeInterface: loading data with extractors, preprocessing, running spike sorters, post-processing via SortingAnalyzer, quality metrics, curation, comparison, visualization, and export.

• spiking-network-model-builder

  Assists building spiking neural network simulations: neuron models, connectivity, plasticity rules

• tom-task-selector

  Selects Theory of Mind tasks matched to target population, age, and construct with psychometric guidance

• verify-skill

  Interactive skill verification — assess accuracy of parameters, citations, and methodology through structured expert review

• visual-search-array-generator

  Specifies display parameters, set sizes, target-distractor similarity, and randomization constraints for visual search experiments