Route NeuroClaw run requests to model docs and modality skills via claw-shell.
npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill run-models
Please help me install this Agent Skill. Skill: run_models Source: https://github.com/CUHK-AIM-Group/NeuroClaw/tree/main/skills/run_models Command: npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill run-models
This skill provides a centralized orchestration layer to coordinate NeuroClaw model execution by routing high-level run requests to the appropriate modality-specific skills and model docs, ensuring safe, auditable, and reproducible runs.
Request a numbered run plan to select a model and delegate modality preprocessing via fmri-skill and smri-skill.
High-intent search queries and answers about installing and using this skill.
Orchestrate fMRI model workflows by routing high-level run requests to appropriate modality skills and model docs. The system coordinates preprocessing delegation and execution via claw-shell, generating a numbered plan with dependency planning and auditable run-paths.
Ensure reproducibility for NeuroStorm or GLM workflows by maintaining traceable run artifacts under run_models_output. The orchestration layer supports drift monitoring, dependency planning, and structured result organization for auditable neuroimaging experiment execution.
Route preprocessing to modality-specific skills like fmri-skill and smri-skill before executing SpaceNet or DictLearning. The system generates a numbered plan, requires user confirmation, and delegates preprocessing to corresponding skill stacks before model execution.
Generate a numbered execution plan for SVM or K-means by submitting a high-level run request. The system creates a structured plan with pointers to model docs, delegated preprocessing steps, and dependency planning, requiring user confirmation before execution via claw-shell.
Filtering and detrending are supported as preprocessing workflows within the orchestration layer. The system routes these steps to appropriate modality skills, ensuring safe delegation and execution before producing organized results under run_models_output.
hiyenwongIntegrate fMRI, DTI, and sMRI data into an interpretable graph neural network for cognitive prediction.
FragileTechAnalyze QFT RunHistory results to calibrate parameters and validate against Standard Model constants.
CUHK-AIM-GroupOrchestrate HCP multimodal processing from data download to modality-specific pipelines.
tizeeCreate and run standalone Python scripts with inline dependency metadata using uv.
