numerical-analysis
Numerical approximation methods
Source facts
- Repository
- ffsshhttiikk/opencode-agents-skills
- Last source activity
- February 28, 2026 at 22:53
- Detected SKILL.md language
- English
- Stars
- 3
- Forks
- 2
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- name
- numerical-analysis
- description
- Numerical approximation methods
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"programmers","category":"mathematics"}
## What I do
- Implement numerical integration (quadrature)
- Solve nonlinear equations numerically
- Approximate derivatives and integrals
- Interpolate data points
- Solve linear systems iteratively
- Analyze numerical stability and error
## When to use me
When analytical solutions are unavailable and numerical approximation is needed.
## Key Concepts
- **Numerical Error**: Truncation (method) + rounding (floating point) errors
- **Newton-Raphson**: x_{n+1} = x_n - f(x_n)/f'(x_n) for root finding
- **Gaussian Quadrature**: Optimal nodes/weights for exact polynomial integration
- **Lagrange Interpolation**: Polynomial through given points
- **Condition Number**: κ = ||A||·||A^{-1}|| measures problem sensitivity
- **Iterative Solvers**: Jacobi, Gauss-Seidel, Conjugate Gradient methods
View on GitHub