CatalystNeuro Claude Code Skills
July 15, 2026 ยท View on GitHub
Public repository of Claude Code skills for neurophysiology research.
Installation
Prerequisites
- Install Claude Code
- Ensure you have an active Anthropic API key or Claude Pro/Max subscription
Adding Skills
Register this repository as a Claude Code plugin marketplace:
/plugin marketplace add catalystneuro/claude-skills
Then install individual skills:
/plugin install analyzing-dandi-datasets@catalystneuro-skills
/plugin install using-nemos@catalystneuro-skills
/plugin install nwb-convert@catalystneuro-skills
Manual Installation (Alternative)
If you prefer to install skills without the marketplace, you can clone this repo and add a skill directly:
git clone https://github.com/catalystneuro/claude-skills.git ~/claude-skills
Then in Claude Code:
/skill add ~/claude-skills/nwb-convert
Verifying Installation
After installing a skill, you can verify it's available:
/skills
This will list all installed skills. You should see the skill name in the list.
Available Skills
nwb-convert
Convert neurophysiology data to NWB format and publish on DANDI. This skill acts as an expert NWB conversion specialist, guiding you through the entire conversion process:
- Experiment Discovery - Understand your data modalities, recording systems, and file organization
- Data Inspection - Automatically inspect files to identify formats, channels, and structure
- Metadata Collection - Gather required NWB metadata (subject, session, devices, electrodes)
- Synchronization - Analyze and plan temporal alignment across data streams
- Code Generation - Generate a complete, pip-installable conversion repo using NeuroConv
- Testing & Validation - Run conversions, validate with NWB Inspector, fix issues iteratively
- DANDI Upload - Organize and upload validated NWB files to the DANDI Archive
Supported modalities:
- Extracellular electrophysiology (SpikeGLX, OpenEphys, Intan, Blackrock, Neuralynx, Plexon, TDT, Axona)
- Spike sorting (Kilosort, Phy, SpykingCircus, MountainSort, YASS, Combinato)
- Calcium imaging (ScanImage, Scanbox, Bruker, MicroManager, Miniscope, Hamamatsu)
- Segmentation (Suite2p, CaImAn, EXTRACT, CellPose)
- Behavior (DeepLabCut, SLEAP, FicTrac, video, custom formats)
- Intracellular electrophysiology (ABF, WinWCP)
Usage:
/nwb-convert /path/to/your/data
Or simply describe what you want to convert:
- "I have SpikeGLX recordings with Kilosort sorting and behavioral data from a VR task"
- "Convert my two-photon calcium imaging data with Suite2p segmentation to NWB"
- "Help me publish my electrophysiology dataset on DANDI"
Knowledge base includes:
- 68 NeuroConv interface specifications
- Canonical conversion repo structure (cookiecutter template)
- Patterns from ~20 real CatalystNeuro conversion repos
- NWB best practices distilled from NWB Inspector
analyzing-dandi-datasets
Analyze neurophysiology datasets from the DANDI Archive. Load NWB files with streaming access, use Pynapple for data inspection, and create analysis pipelines for neural phenomena like directional tuning, place cells, and population dynamics.
Requires: neurosift-tools MCP
using-nemos
Fit Generalized Linear Models (GLMs) to neuroscience data using the NeMoS Python package. Covers:
- Basis functions (BSpline, RaisedCosineLog, CyclicBSpline, Eval vs Conv)
- Observation models (Poisson, Gaussian, Gamma, Bernoulli)
- Regularization (Ridge, Lasso, GroupLasso)
- Single-neuron and population GLMs
- Functional connectivity and coupling filter analysis
- Cross-validation and model selection with scikit-learn
- Calcium imaging with Gaussian GLMs
using-pynapple
Moved. The using-pynapple skill now lives in pynapple-org/claude-skills and is maintained there. It is no longer distributed from this repository.
Usage
After installing a skill, Claude Code will automatically use it when relevant. You can also invoke skills directly with their slash command:
/nwb-convert- Start an NWB conversion workflow- "Fit a Poisson GLM with spike history basis" - triggers
using-nemos - "Find a DANDI dataset with hippocampal place cells" - triggers
analyzing-dandi-datasets
Skill Architecture
Each skill directory contains:
SKILL.md- Main skill definition with frontmatter (name, description, tools) and instructions- Reference files - Knowledge bases, patterns, and examples that the skill consults
The nwb-convert skill is the most complex, with 7 phase-specific instruction files and 4 knowledge base files covering interfaces, repo structure, conversion patterns, and NWB best practices.
License
MIT