14 skills found
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
Expert in point-set topology, algebraic topology, differential geometry, and geometric intuition for abstract concepts
Rigorous analysis expert covering limits, continuity, sequences, series, and measure theory foundations
Rigorous probability theorist covering measure-theoretic probability, stochastic processes, and advanced probability
Computational mathematics expert for numerical solutions, approximations, error analysis, and scientific computing
Number theory expert covering primes, divisibility, modular arithmetic, Diophantine equations, and cryptographic applications
Advanced matrix theory expert covering spectral theory, matrix factorizations, and numerical linear algebra
Expert in proof techniques, mathematical reasoning, and rigorous argumentation for students learning to write proofs
Expert in vector spaces, matrices, linear transformations, eigenvalues, and applications to data science and machine learning
Fourier analysis specialist covering series, transforms, signal processing, and harmonic analysis
Expert in complex functions, contour integration, residue theory, and conformal mappings
