analyzing-malware-family-relationships-with-malpedia
Use the Malpedia platform and API to research malware family relationships, track variant evolution, link families to threat actors, and integrate YARA rules for detection across malware lineages.
Use the Malpedia platform and API to research malware family relationships, track variant evolution, link families to threat actors, and integrate YARA rules for detection across malware lineages.
Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.
重大疾病理赔智能评估(支持 28 种病种)。输入住院病历结构化数据,调用内网评估接口,输出原始 JSON 与自然语言结论(结论 + 证据)。
Retrieve Aavegotchi NFT data by gotchi ID or name on Base. Returns traits, wearables, rarity scores, kinship, XP, level, and owner data.
基于艾宾浩斯遗忘曲线和访问频率的衰减模型设计的遗忘和归档机制,完全依赖openclaw原生记忆系统的拟人化流体记忆系统
Humanize murine antibody sequences using CDR grafting and framework optimization to reduce immunogenicity while preserving antigen binding. Predicts optimal human germline frameworks and identifies critical back-mutations for therapeutic antibody development.
Map unstructured biomedical text to standardized ontologies (SNOMED CT.
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease analysis.
Design CRISPR gRNA sequences for specific gene exons with off-target prediction and efficiency scoring. Trigger when user needs gRNA design, CRISPR guide RNA selection, or genome editing target analysis.
Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers: "shared biomarker study for two diseases", "dual-disease transcriptomic ML paper", "identify common DEGs between disease A and B", "cross-disease hub gene discovery", "shared DEG + PPI + ROC design", "immune infiltration shared biomarker", or "I want to study disease X and Y together". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
Recommend optimal flow cytometry gating strategies for specific cell types and fluorophores
Visualize gene structure with exon-intron diagrams, domain annotations, and mutation position markers. Produces SVG, PNG, or PDF figures suitable for publication from a gene symbol input.
Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.
Generate publication-quality sequence logos for DNA or protein motifs.
Calculate breeding timelines and cage requirements for transgenic mouse colonies
Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction. Always use this skill whenever a user wants to design, plan, or build a study combining MR and single-cell data — even if phrased as "help me write a paper on X", "design a bioinformatics study for Y", or "I want to study Z using MR and scRNA". Covers five study patterns (mechanism gene-set, key-cell, candidate-gene reverse validation, exposure-disease-cell triangulation, translational biomarker) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
Design multi-omics integration strategies for transcriptomics, proteomics, and metabolomics data analysis
Use when analyzing biotech patent landscapes, identifying white spaces in pharmaceutical IP, tracking competitor patents, or assessing freedom to operate for drug development. Provides comprehensive patent analysis and strategic insights for life sciences innovation.
Analyze data with `phylogenetic-tree-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Map patient symptoms to Human Phenotype Ontology terms for gene diagnosis.