📖 Overview

NeuroClaw is a research assistant for executable and reproducible neuroimaging research. Its core strength is neuroimaging dataset and model adaptation: turning raw scans into usable inputs quickly, and enabling medical practitioners to run deep learning models with minimal setup.

Neuroimaging datasets demand specialized preprocessing, and preprocessing quality directly determines model validity. Many workflows assume curated datasets, while MedicalClaw provides limited automation for open-source model execution (primarily large projects like TimesFM and AlphaFold), leaving users to spend significant time on environment configuration.

NeuroClaw prioritizes data processing and model configuration/execution. It ships with independent GUI and CLI interfaces for day-to-day use, and can also be installed as a reusable skill library inside agent projects such as OpenClaw, Hermes, and Claude Code.

---

🚀 Updates

• [2026.06.20]: NeuroClaw now provides Windows and macOS desktop clients, while Linux remains supported through the repository and command-line/web workflows.
• [2026.05.23]: NeuroBench now covers both data processing and model training/evaluation.
• [2026.05.20]: 7 atoms × 15 canonical tasks + 4 mediation chains in neurooracle.atoms.
• [2026.05.15]: NeuroOracle launched: knowledge-graph explorer plus hypothesis engine with live demo at https://huggingface.co/spaces/zxcvb20001/NeuroOracle.
• [2026.05.06]: Added 19 dataset and modality skills with companion scripts; all 86 skills enforce unified metadata (layer, skill_type, dependencies); skill_loader DAG validation ensures dependency graph correctness.
• [2026.04.28]: Our technical report is now available on arXiv: https://arxiv.org/abs/2604.24696
• [2026.04.22]: v1.0 released. Stable release with improvements and full documentation.
• [2026.04.17]: Our project homepage is now live. Welcome to visit: https://cuhk-aim-group.github.io/NeuroClaw/
• [2026.04.08]: NeuroBench released for multi-agent neuroimaging workflow evaluation.
• [2026.04.02]: v0.1 released with complete NeuroClaw framework and core functionality.

✨ Key Features

🔄 Data-Aware Orchestration

• Dataset-Context Planning: Organize capabilities around dataset structure, metadata, and workflow stage instead of simply "which tool to call"
• Automatic Skill Recommendation: Users specify the target dataset, and NeuroClaw recommends relevant skills and executable workflows
• Preprocessing Constraint Awareness: Dataset-specific modality availability and preprocessing requirements are considered during orchestration

Supported Dataset Overview

Show supported dataset table

Dataset	Supported Modalities	Additional Data	Cohort Scale	Official Link
ABCD Study	T1w; T2w; dMRI; rs-fMRI; task-fMRI	Physical and mental health; substance use; culture/environment; neurocognition; biological data	Target cohort of ~11,500 children; current releases through the NBDC Data Sharing Platform	https://abcdstudy.org/
ABIDE	T1w; rs-fMRI	ASD/control phenotypic data	1,112 datasets from 17 international sites	https://fcon_1000.projects.nitrc.org/indi/abide/
ADHD-200	T1w; rs-fMRI	Diagnostic status; ADHD symptom measures; demographics; medication history; QC measures	776 participants/datasets across 8 imaging sites	https://fcon_1000.projects.nitrc.org/indi/adhd200/
AIBL	T1w; PET (PiB, FDG, tau)	Cognitive assessments; blood biomarkers; lifestyle and demographic data; APOE genotype	~1,100+ participants (healthy controls, MCI, AD)	https://aibl.csiro.au/
AOMIC	T1w; rs-fMRI; task-fMRI	Personality traits (Big Five); fluid intelligence; demographic data	~1,000+ participants	https://nilab-uva.github.io/AOMIC.github.io/
ADNI	T1w; T2w; FLAIR; dMRI; rs-fMRI; PET	Genetics/omics data; clinical and cognitive assessments	~2,000+ participants across ADNI phases	https://adni.loni.usc.edu/
BOLD5000	T1w; task-fMRI	Visual image stimuli; category and image metadata	4 participants with 5,000-image visual fMRI sessions	https://bold5000-dataset.github.io/
Cam-CAN	T1w; T2*w; rs-fMRI; task-fMRI; MEG	Cognitive, sensory, and health measures across the adult lifespan	~700 participants ages 18-88	https://www.cam-can.org/
COBRE	T1w; rs-fMRI	Demographics; handedness; diagnostic information	147 participants: 72 schizophrenia patients and 75 healthy controls	https://fcon_1000.projects.nitrc.org/indi/retro/cobre.html
DMT-HAR-MED	rs-fMRI	Psychedelic intervention conditions; behavioral and physiological measures	40 participants in OpenNeuro ds006644	https://openneuro.org/datasets/ds006644/versions/1.0.1
HBN	T1w; T2w; dMRI; rs-fMRI; task-fMRI; EEG	Psychiatric, behavioral, cognitive, lifestyle, genetics, actigraphy	~3,900+ released participants; target resource of at least 10,000 ages 5-21	https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/
HCP Aging	T1w; T2w; dMRI; rs-fMRI; task-fMRI; ASL	Behavioral, cognitive, health, and demographic measures	AABC Release 2: 1,396 participants and 2,878 sessions	https://www.humanconnectome.org/study/hcp-lifespan-aging
HCP Development	T1w; T2w; dMRI; rs-fMRI; task-fMRI	Behavioral, cognitive, health, and demographic measures	~600+ children and adolescents ages 5-21	https://www.humanconnectome.org/study/hcp-lifespan-development
HCP Early Psychosis	T1w; T2w; dMRI; rs-fMRI; task-fMRI	Diagnostic, clinical, behavioral, and cognitive measures	~250 early psychosis and control participants	https://www.humanconnectome.org/study/hcp-early-psychosis
HCP Young Adult	T1w; T2w; dMRI; rs-fMRI; task-fMRI	Behavioral and cognitive measures	2025 release: unprocessed imaging for 1,113 subjects and processed data for 1,071	https://www.humanconnectome.org/study/hcp-young-adult
IXI	T1w; T2w; MRA	Healthy brain MRI from three London hospitals	~600 subjects	https://brain-development.org/ixi-dataset/
MS Challenge	T1w; T2w; FLAIR; PD	Expert manual lesion segmentations for MS benchmarking	5 MS patients with multiple longitudinal timepoints	https://smart-stats-tools.org/lesion-challenge
MND	rs-fMRI; task-fMRI	Motor neuron disease diagnosis and clinical measures	59 participants in OpenNeuro ds005874	https://openneuro.org/datasets/ds005874/versions/1.1.0
Natural Scenes Dataset	T1w; task-fMRI	Natural image stimuli; behavioral responses; image annotations	8 participants with dense repeated visual fMRI	https://naturalscenesdataset.org/
NIFD	T1w; fMRI; DTI; PET	FTD clinical and cognitive data; UCSF Memory and Aging Center	Frontotemporal dementia and related disorders cohorts	https://ida.loni.usc.edu/
OASIS	T1w; PET (PiB)	Clinical and cognitive assessments; dementia diagnosis; demographic data	Cross-sectional (400+) and longitudinal (150+) participants ages 18-96	https://www.oasis-brains.org/
PNC	T1w; dMRI; ASL; rs-fMRI; task-fMRI	Genotyping; clinical and neuropsychiatric assessment; Computerized Neurocognitive Battery	>9,500 youth cohort; 1,445 participants with neuroimaging	https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html
PPMI	T1w; rs-fMRI; DAT-SPECT; PET	Clinical, genetic, biospecimen, and wearable sensor data for Parkinson's disease	~2,000+ participants across 30+ clinical sites worldwide	https://www.ppmi-info.org/
REST-meta-MDD	rs-fMRI	MDD diagnosis; clinical and demographic measures	2,428 participants across 25 cohorts	http://rfmri.org/REST-meta-MDD
SCAN	T1w; FLAIR; optional dMRI; rs-fMRI; ASL; amyloid/tau/FDG PET	Linked NACC longitudinal clinical/cognitive data; centralized QC and imaging summaries	Growing multi-ADRC resource; requestable data depend on completed defacing and QC	https://scan.naccdata.org/
SEED-IV	EEG	Emotion labels across four affective categories; trial-level session metadata	15 subjects across 3 sessions for emotion decoding benchmarks	https://bcmi.sjtu.edu.cn/home/seed/
SEED-VIG	EEG	Vigilance/fatigue labels; continuous alertness annotations; behavioral metadata	23 subjects in sustained-attention driving-style vigilance recordings	https://bcmi.sjtu.edu.cn/home/seed/
TCP	rs-fMRI	Psychiatric diagnostic interviews; cognitive and clinical assessments	245 transdiagnostic participants	https://openneuro.org/datasets/ds004215
UCLA CNP	T1w; dMRI; rs-fMRI; task-fMRI	Diagnostic groups; neuropsychological and phenotypic assessments	272 participants in OpenNeuro ds000030	https://openneuro.org/datasets/ds000030
UK Biobank	T1w; T2w; FLAIR; dMRI; rs-fMRI; task-fMRI	Genotype/genomic data; questionnaires; hospital records; environmental data; sociodemographic data; physical measures	~50,000 participants with multimodal imaging data	https://www.ukbiobank.ac.uk/

Access is not equivalent to anonymous download. See the verified access matrix for registration, DUA, review, fee, and current availability details.

🎯 Executability and Reproducibility

• Automatic Dependency Management: No manual installation needed; the system detects and resolves dependencies
• True Model Execution: Beyond sharing docs, it guides and executes model reproduction
• Environment Isolation: Virtual environments and containerization avoid system pollution
• Verifiable Processes: Complete logging and result tracking
• Shadow Checkpoints: Git-based filesystem snapshots for rollback and diff comparison without polluting the project repository
• Subagent Orchestration: Spawns specialized subagents (biostatistician, clinical neuroscientist, methodology expert) for multi-perspective task execution
• Reflective Learning: Automatic reflection on tool failures and task completion, with persistent memory for cross-session learning

🧠 End-to-End Research Coverage

• Literature Review: arXiv search, PubMed retrieval, academic resource integration
• Experiment Design: Scientific literature analysis, methodology evaluation, research proposal generation
• Data Processing: Multi-format conversion (DICOM ↔ NIfTI), automated preprocessing pipelines
• Model Execution: Run published research models, deep learning framework integration
• Result Visualization: Scientific data visualization, statistical chart generation
• Paper Writing: Auto-generated drafts, format standardization

🤝 Flexible Integration

• NeuroClaw works as a standalone research assistant with its own GUI and CLI, so researchers can use it directly without depending on another host project.
• skills/, materials/, USER.md, and SOUL.md can also be installed as a reusable skill library in existing agent systems such as OpenClaw, Hermes, and Claude Code.
• The bundled core/ engine provides an integrated agent loop, skill loader, and tool runtime for standalone deployments.
• Non-neuroscience connectors (WhatsApp, Telegram, Slack, calendar, e-commerce, SaaS auth) are disabled by default via core/config/features.json and can be re-enabled if needed.

---

🚀 Quick Start

Option 1. Desktop Client (recommended)

Download the latest Windows or macOS client from the GitHub Releases page.

• Windows: use NeuroClaw Setup 0.2.1.exe for normal installation. The portable .exe is also available, but may take longer to start because it extracts the app first.
• macOS: use the .dmg or .zip build from the release assets.
• Open Settings to configure the model endpoint, runtime mode, Python path, FSL path, proxy, language, and text size.
• Open NeuroOracle from the sidebar. If the graph file is missing, the client can download it from Hugging Face.

Linux remains supported through the source repository, command-line workflow, and web interface.

Option 2. Run from Source

Requirements: Python >= 3.10 and Git. Conda/Mamba, CUDA/GPU tools, FSL, FreeSurfer, and dcm2niix are optional depending on the workflows you want to run.

git clone https://github.com/CUHK-AIM-Group/NeuroClaw.git
cd NeuroClaw
python installer/setup.py
python core/agent/main.py --web

Then open http://localhost:7080 in your browser.

Useful checks:

python installer/setup.py --check
python core/agent/main.py --web --port 8080 --host 0.0.0.0

Settings are saved to neuroclaw_environment.json. API keys can be passed at runtime with --api-key or provided through the configured provider environment variable.

Option 3. Install as a Host-Agent Skill

Use this path if you want Codex, Claude Code, Cursor, or another coding agent to call NeuroClaw as a neuroimaging skill library.

git clone https://github.com/CUHK-AIM-Group/NeuroClaw.git
cd NeuroClaw
python installer/install_agent_integration.py --target codex

Common targets:

Host agent	Install command
Codex	python installer/install_agent_integration.py --target codex
Claude Code	python installer/install_agent_integration.py --target claude-code
Cursor	python installer/install_agent_integration.py --target cursor --scope project
Multiple agents	python installer/install_agent_integration.py --target all

After installation, ask the host agent to use NeuroClaw or enter NeuroClaw mode for neuroimaging, NeuroOracle, NeuroBench, and autoresearch tasks.

Note: We provide benchmark run results and per-model outputs under materials/benchmark_results/. These artifacts can be used as practical references when running NeuroClaw benchmarks or reproducing model outputs.

Benchmark Evaluation

NeuroBench tasks live under neurobench/, and each task directory contains a task.md instruction file.

NeuroBench currently accepts these benchmark configurations:

• with-skills: the agent can use the skills loaded from skills/
• no-skills: the baseline run without skills
• with-skills + no-skills paired comparison: enable --benchmark-compare-skills to run both variants for the same task set

Benchmark scoring is handled separately with --score-benchmark: it reads reports in output/, applies a GPT-5.4 weighted rubric, and generates numeric scores for planning completeness, tool/skill reasonableness, and command/code correctness. For fairness, each task case is scored in one batch across all comparable models to reduce scoring-standard drift. Skill-call counts are recorded separately and used for efficiency analysis.

To score existing benchmark reports:

python core/agent/main.py --score-benchmark

To speed up scoring on larger runs:

python core/agent/main.py --score-benchmark --score-workers 8

Web benchmark mode

python core/agent/main.py --web --benchmark

CLI benchmark batch runner

python core/agent/main.py --benchmark

To run the paired skill comparison in CLI mode:

python core/agent/main.py --benchmark --benchmark-compare-skills

In CLI benchmark mode, NeuroClaw will ask for:

• the benchmark directory path
• the benchmark model name

Then it will:

• read all task.md files recursively from that directory
• sort tasks alphabetically by task folder name
• run tasks one by one without asking for intermediate confirmation
• print progress in the terminal only
• save reports under output/<model_name>/, with one markdown report per case and run

The benchmark reports include the solution thinking, skills used, skill-call counts, and the commands or code that were used or suggested.

---

📁 Project Structure

NeuroClaw/
├── README.md / README_zh.md        # Project documentation
├── USER.md / SOUL.md               # User preferences and agent behavior guidelines
│
├── core/                           # Standalone NeuroClaw engine
│   ├── agent/                      # CLI/Web agent entry points
│   ├── web/                        # FastAPI Web UI
│   ├── skill_loader/               # Reads skills/*/SKILL.md
│   └── config/                     # Feature toggles and runtime settings
│
├── installer/                      # Setup wizard and host-agent integration installer
│   ├── setup.py
│   ├── config_wizard.py
│   └── install_agent_integration.py
│
├── skills/                         # Skill library
│   ├── base skills                 # Environment, search, BIDS, Git, conversion
│   ├── interface skills            # Research idea, method design, experiments, writing
│   └── subagent skills             # Tool, model, dataset, and modality workflows
│
├── models/                         # Brain model adapters and training/evaluation scripts
├── neurooracle/                    # Knowledge graph and autoresearch pipeline
│
├── neurobench/                     # NeuroBench evaluation tasks (T01-T120)
│
├── docs/                           # Project website pages
├── materials/                      # Research materials and benchmark outputs
│
└── LICENSE                         # License

---

🛠️ Skill Quick Reference

Tip: Click the ℹ️ icon on any skill card in the Web UI to view expanded documentation, usage examples, and recent execution logs.

Base Layer

Skill	Function	Status
dcm2nii	DICOM → NIfTI conversion with metadata support	✅
nii2dcm	NIfTI → DICOM conversion for clinical interoperability	✅
git-essentials	Core Git commands for collaboration	✅
git-workflows	Advanced Git workflows (rebase/worktree/bisect)	✅
multi-search-engine	Multi-engine web search without API keys	✅
conda-env-manager	Conda environment lifecycle management	✅
docker-env-manager	Docker environment management	✅
dependency-planner	Dependency planning and safe installation workflow	✅
claw-shell	Safe shell execution gateway via dedicated session	✅
overleaf-skill	Overleaf sync and collaborative manuscript operations	✅
academic-research-hub	Multi-source academic search and paper retrieval	✅
bids-organizer	Base skill for organizing raw data into BIDS structure	✅
beautiful-log	Export clean User/NeuroClaw dialogue into beautiful HTML logs	✅
knowledge-graph-builder	Build domain knowledge graphs from literature and databases	✅
skill-updater	Skill updater and management utilities	✅

Interface Layer (Task Orchestration)

Skill	Function	Status
research-idea	Brainstorms and generates research ideas from literature	✅
method-design	Formalizes network architecture and derives theoretical components	✅
experiment-controller	Finds and executes reproducible research experiments	✅
paper-writing	Generates hierarchical manuscript drafts from IDEA/METHOD/EXPERIMENT	✅

Subagent Layer

Subagent in NeuroClaw includes four categories: tool, model, dataset, and modality.

Tool

Skill	Function	Status
brain-visualization	Publication-ready figures and 3D assets (connectomes, atlas summaries, FreeSurfer PLY)	✅
harmonization-tool	Cross-site / cross-scanner feature harmonization (ComBat, ComBat-GAM, CovBat, site-as-covariate) with site-stratified and leave-site-out splitters; required for honest mega-analysis across multi-site cohorts	✅
harness-core	Core harness SDK: verification, checkpointing, drift detection, audit logging	✅
mne-eeg-tool	Base-layer MNE-Python implementation for EEG	✅
fsl-tool	FSL-based sMRI/fMRI/DWI processing utilities	✅
fmriprep-tool	fMRIPrep pipeline wrapper and execution	✅
qsiprep-tool	qsiPrep pipeline wrapper for diffusion MRI	✅
hcppipeline-tool	HCP-style processing pipeline utilities	✅
dipy-tool	Diffusion MRI processing via DIPY	✅
nibabel-skill	Low-level neuroimaging I/O and geometry handling (NIfTI, affine, FreeSurfer I/O)	✅
nilearn-tool	Fast neuroimaging feature extraction and decoding prep	✅
conn-tool	Functional connectivity computation and analysis	✅
freesurfer-tool	FreeSurfer-based MRI processing and segmentation	✅

Model

Skill	Function	Status
run_models	Model registry and model execution orchestration	✅
wmh-segmentation	White matter hyperintensity segmentation (MARS-WMH nnU-Net)	✅
brain_gnn	BrainGNN: graph neural network for fMRI classification	✅
bnt	BrainNetworkTransformer: dense FC Transformer with DEC pooling for phenotype prediction	✅
brainnetcnn	BrainNetCNN: E2E/E2N/N2G convolutions over dense connectivity matrices	✅
combraintf	Com-BrainTF: community-aware two-level Transformer over dense FC matrices	✅
ibgnn	IBGNN: interpretable PyG-based GNN with MLP message function and edge-mask explainer	✅
lggnn	LG-GNN: PyG-based GNN with Self-Attention Brain Pooling and mutual-information regularization	✅
fm_app	FM-APP: multi-stage phenotype prediction with fMRI+sMRI	✅
neurostorm	NeuroStorm: neuroimaging foundation model	✅
glm	Classical first-level and second-level GLM for task-fMRI activation and group inference	✅
ica	Resting-state network decomposition via independent component analysis	✅
dictlearning	Sparse resting-state network decomposition via dictionary learning	✅
spacenet	Voxel-wise neuroimaging disease classification with sparse coefficient maps	✅
kmeans	Brain parcellation via K-means clustering	✅
hierarchical	Multi-scale brain parcellation via hierarchical clustering	✅
filtering	Temporal filtering for neuroimaging signal denoising	✅
detrending	Temporal drift removal for neuroimaging signal denoising	✅
statistical-ml	Unified tabular OLS/GLM, SVM/SVR, Ridge, Elastic Net, XGBoost, and mixed-effects models	✅
subject-subtyping	Subject-level subtyping with clustering and latent embeddings	✅
survival-models	Censor-aware Cox, RSF, DeepSurv, and XGBoost survival models	✅
causal-treatment-models	Cross-fitted treatment-effect and individualized policy models	✅
temporal-models	LSTM, GRU, TCN, and temporal Transformer sequence models	✅
imaging-genetics-models	Association, LMM, PRS, PLS, and CCA imaging-genetics models	✅
cnn3d	Compact residual 3D CNN for voxel-level prediction	✅
cpm	Connectome Predictive Modeling with fold-local edge selection	✅
kg-link-prediction	ComplEx, R-GCN, GraphSAGE, and GAT knowledge-graph link prediction	✅

Workflow

Skill	Function	Status
neuroimaging-decoding	Coordinates ROI MVPA, ROI GLM, and voxel-wise SearchLight analysis	✅
connectome-discovery	Converts connectome-model outputs into significant maps and ranked targets	✅
brain-age-modeling	Cross-validated brain-age prediction with fold-local bias correction	✅

Dataset

Skill	Function	Status
abide-skill	ABIDE dataset download, BIDS staging, and sMRI/rs-fMRI processing	✅
aibl-skill	AIBL dataset access, BIDS staging, and sMRI/PET processing	✅
abcd-skill	ABCD Study controlled NBDC access, BIDS staging, and multimodal processing	✅
adhd200-skill	ADHD-200 dataset download, BIDS staging, and sMRI/rs-fMRI processing	✅
adni-skill	ADNI and ADNI-DOD controlled access, BIDS staging, and processing workflow	✅
aomic-skill	AOMIC dataset validation, BIDS staging, and sMRI/rs-fMRI/task-fMRI processing	✅
bold5000-skill	BOLD5000 dataset BIDS validation and visual task-fMRI processing	✅
camcan-skill	Cam-CAN dataset BIDS validation, multimodal sMRI/rs-fMRI/task-fMRI/dMRI processing	✅
cobre-skill	COBRE dataset BIDS staging and schizophrenia-control fMRI processing	✅
dmt-har-med-skill	DMT-HAR-MED dataset BIDS validation and psychedelic rs-fMRI processing	✅
hbn-skill	HBN dataset download, BIDS staging, and multimodal sMRI/fMRI/dMRI/EEG processing	✅
hcpa-skill	HCP Aging/AABC access, BIDS staging, and multimodal sMRI/fMRI/dMRI/ASL processing	✅
hcpd-skill	HCP Development dataset download, BIDS staging, and multimodal sMRI/fMRI/dMRI processing	✅
hcpep-skill	HCP Early Psychosis dataset download, BIDS staging, and multimodal sMRI/fMRI/dMRI processing	✅
hcpya-skill	HCP Young Adult 2025/S1200 access, BIDS staging, and multimodal sMRI/fMRI/dMRI processing	✅
ixi-skill	IXI dataset BIDS validation and multimodal sMRI/MRA/dMRI processing	✅
mnd-skill	MND dataset BIDS validation, rs-fMRI/task-fMRI processing, and phenotype extraction	✅
mschallenge-skill	MS Lesion Challenge BIDS validation, lesion analysis, and longitudinal tracking	✅
nsd-skill	Natural Scenes Dataset BIDS validation, task-fMRI processing, and COCO stimulus extraction	✅
nifd-skill	NIFD dataset BIDS validation, multimodal sMRI/rs-fMRI/dMRI processing for frontotemporal dementia	✅
oasis-skill	OASIS dataset BIDS validation, sMRI processing, and phenotype extraction for aging/AD research	✅
pnc-skill	PNC dataset BIDS validation, multimodal sMRI/rs-fMRI/task-fMRI/dMRI processing for developmental studies	✅
ppmi-skill	PPMI dataset BIDS validation, multimodal sMRI/rs-fMRI/dMRI processing for Parkinson's disease	✅
rest-mneta-mdd-skill	REST-meta-MDD multi-site rs-fMRI processing, site harmonization, and depression phenotype extraction	✅
scan-skill	SCAN/NACC access planning, approved-export staging, phenotype linkage, and multimodal MRI/PET processing	✅
seed-iv-skill	SEED-IV EEG emotion recognition (4 emotions), feature extraction, and classification	✅
seed-vig-skill	SEED-VIG EEG vigilance/fatigue detection, feature extraction, and drowsiness classification	✅
tcp-skill	Transdiagnostic Connectome Project BIDS validation, multimodal sMRI/rs-fMRI/dMRI processing	✅
ucla-cnp-skill	UCLA CNP BIDS validation, multimodal sMRI/task-fMRI/dMRI processing, multi-disorder phenotyping	✅
ukb-skill	UKB brain imaging automated processing workflow	✅

Modality

Skill	Function	Status
eeg-skill	EEG preprocessing and feature extraction workflows	✅
fmri-skill	Functional MRI preprocessing and analysis workflows	✅
smri-skill	Structural MRI preprocessing and analysis workflows	✅
dwi-skill	Diffusion MRI preprocessing and analysis workflows	✅
pet-skill	PET imaging workflows (SUVR computation, reference regions, PVC)	✅
asl-skill	ASL perfusion MRI workflows (CBF quantification, Buxton model)	✅
meg-skill	MEG processing workflows (source localization, time-frequency, connectivity)	✅

Legend: ✅ Implemented | 🏗️ In Development | ⏳ Planned

---

🙏 Acknowledgments

Thanks to:

• OpenClaw
• Hermes
• Claude Code
• Karcen/rs-fMRI-Pipeline-Tutorial
• nature-skills
• Open-source neuroscience tools community (MNE-Python, FreeSurfer, FSL, etc.)