From the author
Organizes, queries, validates, and converts neuroscience and biomedical datasets using the Brain Imaging Data Structure (BIDS) standard, covering MRI, PET, EEG, MEG, iEEG, EMG, NIRS, microscopy, motion capture, and behavioral data. Covers PyBIDS (BIDSLayout), the bids-validator, HeuDiConv, dcm2bids, BIDScoin, JSON sidecars, dataset_description.json, events.tsv, participants.tsv, .bidsignore, and derivatives. Use when arranging raw data into BIDS directories and filenames. Use when converting DICOM scans to BIDS. Use when validating a dataset before OpenNeuro or DANDI submission. Use when querying files or sidecar metadata with PyBIDS. Use when writing BIDS derivatives. For extracellular Neuropixels analysis, use neuropixels-analysis instead.
Install
npx skills add KalarisLabs/research-agent-skills --skill bids -a claude-code
Installs to .claude/skills/bids/ · Each command fetches the skill straight from GitHub with the open skills CLI.
No download: this skill’s licence (Custom or unclear) does not let us redistribute its files. The install commands fetch them from GitHub instead.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 71
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- nwb-conversionK-Dense-AI/scientific-agent-skillsConverts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.Marketing★ 55.8K
- neurokit2foryourhealth111-pixel/Vibe-SkillsComprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.Science★ 3.8K
- neuropixels-analysisK-Dense-AI/scientific-agent-skillsAnalyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.Engineering★ 55.8K
- nsd-skillBioTender-max/awesome-bio-agent-skillsUse this skill whenever the user wants an end-to-end workflow for the Natural Scenes Dataset (NSD), including data access, BIDS validation, multimodal processing of task-fMRI and structural MRI, stimulus metadata extraction, and QC integration. Triggers include: 'NSD', 'Natural Scenes Dataset', 'process NSD data', 'NSD fMRI', 'visual neuroscience', or any request to run the NSD multimodal pipeline.Productivity★ 213
- hbn-skillBioTender-max/awesome-bio-agent-skillsUse this skill whenever the user wants an end-to-end workflow for the Healthy Brain Network (HBN) dataset, including download, BIDS organization, and multimodal processing of sMRI, dMRI, rs-fMRI, task-fMRI, and EEG data. Triggers include: 'HBN', 'Healthy Brain Network', 'process HBN', 'HBN fMRI', 'HBN EEG', or any request to run the HBN multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for HBN.Productivity★ 213
- pyhealthforyourhealth111-pixel/Vibe-SkillsComprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).Testing★ 3.8K
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in