Does it cover complex number operations?

Absolutely, it covers all four precision types, including single (c) and double (z) precision complex operations using lapack_complex_float and lapack_complex_double types.

Does this skill support both row-major and column-major layouts?

Yes, it provides documentation for both CBLAS and LAPACKE layout constants, explaining the leading dimension (lda) requirements for each to ensure memory safety.

Can it help me choose between different LAPACK solvers?

Yes, it includes a solver selection guide that helps you choose the right routine (e.g., Simple vs. Expert drivers) based on your matrix type and accuracy requirements.

How does this help with linking errors in C programs?

It provides specific compile and link commands for GCC, Clang, OpenBLAS, and Intel MKL, helping resolve 'undefined reference' errors for BLAS and LAPACK functions.

CBLAS & LAPACKE API Reference

Name: CBLAS & LAPACKE API Reference
Author: datathings

bydatathings

•

Data Science & ML

Provides a comprehensive C API reference for BLAS and LAPACK numerical linear algebra routines.

This skill equips Claude with the complete API documentation for BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra PACKage) version 3.12.1, specifically for the CBLAS and LAPACKE C interfaces. It enables Claude to assist developers in selecting the correct routines for vector and matrix operations, linear system solvers, eigenvalue computations, and singular value decomposition (SVD). Beyond function lookups, it provides essential guidance on memory layout (row vs. column major), precision prefixes, matrix storage schemes, and optimized linking strategies for high-performance computing (HPC) environments.

Key Features

01Reference for 1,284 functions across CBLAS and LAPACKE v3.12.1

02Comprehensive solver selection guide for linear systems and eigenvalues

037 GitHub stars

04Compilation and linking guidance for OpenBLAS, Intel MKL, and GCC

05Support for all precision types: single/double real and single/double complex

06Detailed documentation on matrix storage (Banded, Symmetric, Packed, RFP)

Use Cases

01Optimizing matrix operations by selecting specialized routines for structured matrices

02Debugging complex linear algebra routine calls and memory alignment issues

03Developing high-performance scientific simulations or engineering applications in C/C++

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add datathings/marketplace blas_lapack

For use in Claude.ai and ChatGPT

Download Skill