NeuroClaw: Closed-Loop Agentic AI for Executable and Reproducible Neuroimaging Research

Features • Quick Start • Project Structure • Skills • Acknowledgments

📖 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; full cohort releases through the NIMH Data Archive	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	Behavioral, cognitive, health, and demographic measures	~700+ adults ages 36-100	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	~1,200 young adult participants	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
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/

🎯 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	✅
`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	✅
`svm`	Classical neuroimaging disease classification with ROI/tabular features	✅
`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	✅

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 dataset download, BIDS staging, and multimodal processing	✅
`adhd200-skill`	ADHD-200 dataset download, BIDS staging, and sMRI/rs-fMRI processing	✅
`adni-skill`	ADNI dataset automated 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 dataset download, BIDS staging, and multimodal sMRI/fMRI/dMRI 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 (HCP1200) dataset download, 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	✅
`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.)

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Repository files navigation

NeuroClaw: Closed-Loop Agentic AI for Executable and Reproducible Neuroimaging Research

📖 Overview

🚀 Updates

✨ Key Features

🔄 Data-Aware Orchestration

Supported Dataset Overview

🎯 Executability and Reproducibility

🧠 End-to-End Research Coverage

🤝 Flexible Integration

🚀 Quick Start

Option 1. Desktop Client (recommended)

Option 2. Run from Source

Option 3. Install as a Host-Agent Skill

Benchmark Evaluation

📁 Project Structure

🛠️ Skill Quick Reference

Base Layer

Interface Layer (Task Orchestration)

Subagent Layer

Tool

Model

Dataset

Modality

🙏 Acknowledgments

About

Releases

Packages

Contributors

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 64 Commits
.github/workflows		.github/workflows
core		core
desktop		desktop
docs		docs
installer		installer
materials		materials
models		models
neurobench		neurobench
neurooracle		neurooracle
skills		skills
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
README_zh.md		README_zh.md
SOUL.md		SOUL.md
USER.md		USER.md
pyproject.toml		pyproject.toml

Uh oh!

Folders and files

Latest commit

History

Repository files navigation

NeuroClaw: Closed-Loop Agentic AI for Executable and Reproducible Neuroimaging Research

📖 Overview

🚀 Updates

✨ Key Features

🔄 Data-Aware Orchestration

Supported Dataset Overview

🎯 Executability and Reproducibility

🧠 End-to-End Research Coverage

🤝 Flexible Integration

🚀 Quick Start

Option 1. Desktop Client (recommended)

Option 2. Run from Source

Option 3. Install as a Host-Agent Skill

Benchmark Evaluation

📁 Project Structure

🛠️ Skill Quick Reference

Base Layer

Interface Layer (Task Orchestration)

Subagent Layer

Tool

Model

Dataset

Modality

🙏 Acknowledgments

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages