Generate comprehensive target profiles including druggability assessment, disease associations, pathway context, and competitive landscape. Use this skill when analyzing drug targets for validation, dossier creation, or competitive intelligence. Supports gene symbols (EGFR), protein names, or UniProt IDs. Keywords: target, dossier, validation, druggability, tractability, target analysis
Generate comprehensive compound profiles including structure, properties, bioactivity, and development status. Use for drug analysis, SAR studies, and competitive profiling. Keywords: compound, drug, molecule, structure, SMILES, bioactivity, IC50
Analyze academic papers, extract claims, methods, evidence, limitations, and propose follow-up experiments or verification steps.
Retrieve and organize evidence for key claims using available tools, with source URLs and confidence tags.
When the user wants to solve complex optimization problems using metaheuristics, apply genetic algorithms, simulated annealing, or other nature-inspired algorithms. Also use when the user mentions "genetic algorithm," "simulated annealing," "tabu search," "ant colony," "particle swarm," "evolutionary algorithms," "nature-inspired optimization," "heuristic search," or when exact optimization methods are too slow for large-scale problems. For exact methods, see optimization-modeling. For multi-objective, see multi-objective-optimization.
Clinical data modeling covering healthcare terminology systems (ICD-10, SNOMED CT, LOINC, RxNorm, CPT, NDC), clinical document architecture, patient data normalization, temporal clinical data patterns, and clinical decision support data models.
Research-focused autonomous agent for evidence collection.
Computational system taxonomy for classifying systems by their computational properties. Load when analyzing or categorizing system architectures.
Run a simulation example and analyze the output
When the user wants to solve the Traveling Salesman Problem (TSP), find the shortest route visiting all cities, or optimize tour sequences. Also use when the user mentions "TSP," "shortest tour," "Hamiltonian cycle," "tour optimization," "route sequencing," "optimal visit order," "traveling salesperson," or "minimum distance tour." For vehicle routing with capacities, see vehicle-routing-problem.
Morph, blend, and transform faces using each::sense AI. Create face morphs, celebrity blends, family resemblance predictions, gender swaps, and animated transitions between faces.
Router for simulation math - ODEs, state-space, stability, control, numerics, chaos, stochastic
Deep research skill for systematic exploration. Auto-triggered for research, analysis, investigation tasks. Ensures data accuracy and research depth.
Deep web research with parallel investigators, multi-wave exploration, and structured synthesis. Spawns multiple web-researcher agents to explore different facets of a topic simultaneously, launches additional waves when gaps are identified, then synthesizes findings. Use when asked to research, investigate, compare options, find best practices, or gather comprehensive information from the web.\n\nThoroughness: quick for factual lookups | medium for focused topics | thorough for comparisons/evaluations (waves continue while critical gaps remain) | very-thorough for comprehensive research (waves continue until satisficed). Auto-selects if not specified.
Multi-specialist collaborative analysis to understand how features, functions, or components work
MDOODZ governing equations and numerical methods — conservation of momentum (Stokes), energy, and mass, constitutive relations, non-dimensionalisation and scaling, marker-in-cell advection, free surface algorithm, pure shear ALE mode, and theoretical background.
Code-to-physics glossary for MDOODZ — variable names, struct fields, function names mapped to physical meaning, equations, SI units, grid naming conventions, creep mechanism abbreviations, and solver process stages.
MDOODZ mechanical anisotropy module — director-based fabric tracking, anisotropy factors (δ_n, δ_s), viscous transverse isotropy, ViscosityConciseAniso, finite strain evolution, anisotropic rifting methodology, and anisotropy scenario examples.
Analyzes datasets to extract insights, identify patterns, and create visualizations. This skill should be used when users want to explore data, perform statistical analysis, identify trends, or visualize patterns in structured data.
Electronic circuit simulation and SPICE analysis via ngspice MCP tool
Diskrete oder kontinuierliche Markov-Ketten erstellen und analysieren, einschliesslich Uebergangsmatrix-Konstruktion, Zustandsklassifikation, Berechnung stationaerer Verteilungen und mittlerer Erstpassagezeiten. Verwenden beim Modellieren eines gedaechtnislosen Systems mit beobachteten Uebergangszaehlungen oder -raten, beim Berechnen von langfristigen stationaeren Wahrscheinlichkeiten, beim Bestimmen erwarteter Treffzeiten oder Absorptionswahrscheinlichkeiten, beim Klassifizieren von Zustaenden als transient oder rekurrent oder beim Aufbau einer Grundlage fuer Hidden Markov Models oder Reinforcement Learning MDPs.
Simulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.
Massenspektren systematisch interpretieren um Summenformel zu bestimmen, Fragmentierungswege zu identifizieren und Molekuelstrukturen vorzuschlagen. Behandelt Ionisierungsmethoden-Bewertung, Molekuelion-Identifikation, Isotopenmuster-Analyse, haeufige Fragmentierungsverluste und Reinheitsbewertung.
