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claude-codescience

Babysitter numerical-linear-algebra-toolkit

High-performance numerical linear algebra operations

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/mathematics/skills/numerical-linear-algebra-toolkit" ~/.claude/skills/a5c-ai-babysitter-numerical-linear-algebra-toolkit && rm -rf "$T"
manifest: library/specializations/domains/science/mathematics/skills/numerical-linear-algebra-toolkit/SKILL.md
tags
#matrix-decomposition#eigen-analysis#sparse-matrix#iterative-solvers#numerical-analysis
source content

Numerical Linear Algebra Toolkit

Purpose

Provides high-performance numerical linear algebra operations for scientific computing and mathematical analysis.

Capabilities

  • Matrix decompositions (LU, QR, SVD, Cholesky, Schur)
  • Eigenvalue/eigenvector computation
  • Sparse matrix operations
  • Iterative solvers (CG, GMRES, BiCGSTAB)
  • Condition number estimation
  • Error analysis and bounds

Usage Guidelines

  1. Decomposition Selection: Choose appropriate factorization for the problem
  2. Sparsity Exploitation: Use sparse formats for large sparse matrices
  3. Iterative Methods: Apply iterative solvers for very large systems
  4. Conditioning: Assess and monitor condition numbers

Tools/Libraries

  • LAPACK
  • BLAS
  • SuiteSparse
  • Eigen