ode-solver-library

Numerical methods for ordinary differential equations

509 stars

Best use case

ode-solver-library is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Numerical methods for ordinary differential equations

Teams using ode-solver-library should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/ode-solver-library/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/ode-solver-library/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/ode-solver-library/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How ode-solver-library Compares

Feature / Agent	ode-solver-library	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Numerical methods for ordinary differential equations

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# ODE Solver Library

## Purpose

Provides numerical methods and solvers for ordinary differential equations in mathematical modeling and dynamical systems analysis.

## Capabilities

- Runge-Kutta methods (explicit and implicit)
- Multistep methods (Adams-Bashforth, BDF)
- Stiff equation handling
- Adaptive step size control
- Event detection and root finding
- Sensitivity analysis

## Usage Guidelines

1. **Stiffness Assessment**: Determine if problem is stiff
2. **Method Selection**: Choose explicit or implicit methods accordingly
3. **Tolerance Setting**: Set appropriate error tolerances
4. **Event Handling**: Configure event detection for discontinuities

## Tools/Libraries

- SUNDIALS
- scipy.integrate
- DifferentialEquations.jl