motion-capture-analyzer
Motion capture data processing and analysis skill for gait analysis and biomechanical studies
Motion capture data processing and analysis skill for gait analysis and biomechanical studies
Supply chain discrete-event simulation for scenario testing and optimization
Aspen HYSYS integration skill for dynamic simulation, pressure-flow networks, and process dynamics
Process engineering skill for analyzing and optimizing nanomaterial synthesis scale-up from lab to production
GROMACS molecular dynamics skill for nanoparticle-biomolecule interaction simulations
Design of Experiments skill for systematic optimization of nanomaterial synthesis and processing
Numerical methods for ordinary differential equations
Numerical methods for partial differential equations
Open-source computer algebra system for symbolic computation
Synthesis parameter optimization skill for metal, semiconductor, and oxide nanoparticle production with automated protocol generation and reproducibility validation
Skill for thermal characterization workflows including DSC, TGA, DTA, TMA, and DMA for phase transitions, decomposition, and viscoelastic property analysis
Digital twin representation of supply chain for real-time monitoring and simulation
Discrete event simulation skill for modeling and analyzing complex systems with stochastic processes.
Paper X-ray. Extracts what the paper says (problem-perspective-result) and what it means for lijigang (cognitive delta cards in ASCII art). Use when user shares an arxiv link, paper URL, PDF, or asks to analyze a research paper. Usually called via ljg-xray router. Do NOT use for blog posts or non-academic articles (use ljg-xray-article instead).
Look up current research information using Perplexity's Sonar Pro or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
Creates sashimi plots showing RNA-seq read coverage and splice junction counts using ggsashimi or rmats2sashimiplot. Visualizes differential splicing events with grouped samples and junction read support. Use when visualizing specific splicing events or validating differential splicing results.
Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways.
Calculates statistical power and minimum sample sizes for RNA-seq, ATAC-seq, and other sequencing experiments. Use when planning experiments, determining how many replicates are needed, or assessing whether a study is adequately powered to detect expected effect sizes.
Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.
Estimates required sample sizes for differential expression, ChIP-seq, methylation, and proteomics studies. Use when budgeting experiments, writing grant proposals, or determining minimum replicates needed to achieve statistical significance for expected effect sizes.
Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions.
Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.