Dados e Análise

ETE Toolkit (ETE3): Python phylogenetic tree analysis and visualization. Parse Newick/NHX/PhyloXML, traverse/annotate nodes, render figures with TreeStyle/NodeStyle, integrate NCBI taxonomy, run PhyloTree comparative genomics. Use for species trees, gene family evolution, annotated tree figures.

Plataforma de ML baseada em PyTorch para descoberta de medicamentos, cobrindo aprendizado de representação molecular por grafos, previsão de propriedades (ADMET, atividade), retrossíntese e interação droga-alvo. Inclui camadas GNN, modelos pré-treinados e conjuntos de dados de benchmark.

Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments

JASPAR 2024 TF binding profiles via REST API and pyJASPAR. Retrieve PFMs/PWMs by TF name, JASPAR ID, species, or structural class. Scan DNA for TFBS; browse by taxon (human, mouse) or TF family (bHLH, zinc finger). Use for motif enrichment input, TFBS scanning, and regulatory sequence analysis. For ChIP-seq peak motif discovery use homer-motif-analysis; for regulatory variant scoring use regulomed

Queries FUTBIN (EA FC Ultimate Team database) via cli-web-futbin — player search, prices and price history, comparisons, SBCs, evolutions, and market analytics (index, trending, cheapest by rating, movers, fodder, buy/sell signals, undervalue scans, PS/PC arbitrage). Use when the user asks about EA FC player prices, SBCs, evolutions, FUT market data, or trading decisions like "should I buy/sell X"

Use cli-web-futbin to answer questions about EA FC Ultimate Team players, prices, player comparison, SBCs, evolutions, config, market data, popular/trending players, newly released cards, price history, and finding cheap deals. Invoke this skill whenever the user asks about FUTBIN, EA FC player prices, card prices, squad building challenges (SBCs), player evolutions, player comparison, market inde

Use para criar, revisar ou estilizar gráficos, dashboards e outras visualizações de dados, abrangendo ferramentas como Recharts, ECharts, Chart.js, matplotlib, Plotly, seaborn, D3.js e SVG. Aplica princípios de Tufte e padrões modernos de tela, como acessibilidade, responsividade e modo escuro.

Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.

Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.

Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.

Synchronize construction digital twins with real-time data. Connect BIM models with IoT sensors, progress updates, and field data for live project visualization and monitoring.

Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated pipelines without deep programming knowledge.