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A modular collection of professional skills designed for OpenClaw agents, covering neuroanatomy, lesion interpretation, tauopathy staging, and multimodal reasoning, encoding classic neuroscience methodologies into AI-native research workflows.
Section 01
OpenClaw-Neuropath-Skills is a modular professional skill framework designed for OpenClaw agents, covering core skills such as neuroanatomy, lesion interpretation, tauopathy staging, and multimodal reasoning. It encodes classic neuroscience methodologies into AI-native research workflows, enabling whole-slide analysis in neuropathology.
Section 02
Neuropathology is a fundamental pillar of neuroscience, relying on rigorous observation and reasoning. The digitization of whole-slide images (WSI) has led to an explosion in data volume, and traditional manual analysis faces efficiency bottlenecks. The OpenClaw-Neuropath-Skills project emerged to encode classic neuropathological methodologies into AI-native skills, allowing agents to analyze like pathologists.
Section 03
The project adopts a modular skill architecture, where each skill corresponds to a professional link in the neuropathological workflow, embodying the "divide and conquer" idea: breaking complex tasks into manageable, reusable, and combinable units. Skills collaborate through clear interfaces, balancing system flexibility and the integrity of the analysis process.
Section 04
A foundational skill that identifies and locates normal neural tissue structures (such as cortical layering, gray-white matter boundaries, and nucleus positions) to provide spatial cognitive capabilities.
Identifies abnormal pathological changes (such as abnormal cell morphology, structural disorders, and inflammatory responses). It references the diagnostic thinking of human pathologists, confirming details from low to high magnification.
Automates standardized processes like Braak staging, performing systematic staging judgments according to international pathological standards.
Integrates multimodal information such as histological staining, immunohistochemistry, clinical history, and imaging to simulate the comprehensive judgment of experts.
Section 05
Encodes the classic scientific methodologies of pioneers like Cajal and Alzheimer into AI workflows, including:
Section 06
Section 07
Expand support for more neurodegenerative disease assessments, integrate genomic/proteomic data, develop real-time collaboration functions, and establish a cross-institutional skill-sharing ecosystem.
OpenClaw-Neuropath-Skills is a model of deep AI application. It encodes human expert knowledge into reusable digital assets, emphasizing human-machine collaboration rather than replacement, and provides a sustainable path for AI implementation in professional service fields.
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