BrainPilot

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.

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 containers for fMRIPrep, choose output spaces (MNI152NLin2009cAsym, fsaverage, fsLR/CIFTI), configure susceptibility distortion correction (SDC), extract or interpret the confounds table, resample to surface/grayordinates, cite fMRIPrep and its dependencies, or debug fMRIPrep crashes and hangs.

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, connectomes, graph theory on brain networks, parcellated brain data (Schaefer, Cammoun, Desikan-Killiany), cortical surface templates (fsaverage, fsLR, CIVET, Conte69), network communication metrics, null model generation, spatial autocorrelation on brain maps, community detection in connectomes, or surface-based visualization with PyVista/PySurfer. Also trigger when the user mentions netneurotools, netneurolab, structure-function coupling, network neuroscience, brain graph analysis, or needs to fetch neuroimaging atlases and templates.

Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.

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, behavior tracking, keypoint detection in videos, or mentions DeepLabCut/DLC/SuperAnimal.

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

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

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

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 reusable skill documentation. Also trigger when users say things like 'make a skill from this repo', 'turn this codebase into a skill', or 'I want a skill for [library name]'.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 skill whenever the user works with EEG/MEG/sEEG/ECoG/NIRS/eye-tracking data in Python, mentions MNE, or needs neurophysiological analysis guidance.

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

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

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

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

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

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. Use this skill whenever the user mentions pycortex, `import cortex`, cortical surfaces, brain flatmaps, WebGL brain viewers, cortical surface mapping, or wants to visualize neuroimaging data on the cortex, even if they don't explicitly name pycortex.

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

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