Manus에서 모든 스킬 실행
원클릭으로
원클릭으로
원클릭으로 Manus에서 모든 스킬 실행
시작하기파일 탐색기
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관련 직업SOC
SOC 직업 분류 기준
SOC 직업 분류 기준
# Academic Literature Search — 学术文献检索与引用管理
Search arXiv for preprints in physics, math, CS, quantitative biology, quantitative finance, statistics, electrical engineering, economics. Use when: (1) finding preprints by topic, (2) searching by author, (3) browsing arXiv categories, (4) getting paper metadata/abstracts. NOT for: published journal articles (use crossref-search), biomedical (use pubmed-search).
Screen papers for systematic reviews using ASReview active learning. Use when: user has a large set of papers to screen for inclusion/exclusion, wants to prioritize relevant papers, or needs to reduce manual screening workload. NOT for: searching papers (use literature-search) or meta-analysis (use meta-analysis).
Analyzes astronomical observations and cosmological models including telescope data processing, celestial mechanics calculations, stellar evolution, galaxy classification, and cosmological parameter estimation; trigger when users discuss stars, galaxies, exoplanets, dark matter, or the universe's large-scale structure.
"Astronomical computations via Astropy. Use when: user asks about celestial coordinates, FITS files, or cosmological calculations. NOT for: telescope control or real-time observation planning."
Performs bioinformatics analyses including pathway enrichment, gene ontology analysis, protein-protein interaction networks, multi-omics integration, and biological sequence database querying; trigger when users discuss gene sets, biological pathways, functional annotation, or omics data integration.
| name | computational-pathology-agent |
| description | COPYRIGHT NOTICE |
name: computational-pathology-agent description: Analyze Whole Slide Images (WSI) for digital pathology, including tissue segmentation and feature extraction. keywords:
Version: 1.0.0 Author: MD BABU MIA, PhD Date: February 2026
This agent specializes in the analysis of Whole Slide Images (WSIs) for digital pathology. It leverages Deep Learning models (ResNet, ViT, HoverNet) to perform segmentation, classification, and feature extraction from gigapixel histology images.
from Skills.Pathology_AI.Computational_Pathology_Agent.wsi_analyzer import WSIAnalyzer
# Initialize
path_agent = WSIAnalyzer(slide_path="./data/biopsy_001.svs")
# Extract tissue patches
path_agent.extract_patches(patch_size=256, level=1)
# Analyze Nuclei (requires model weights)
# path_agent.segment_nuclei()