orcaflex-mooring-iteration-1-scipy-optimization-recommended
Sub-skill of orcaflex-mooring-iteration: 1. Scipy Optimization (Recommended) (+2).
Best use case
orcaflex-mooring-iteration-1-scipy-optimization-recommended is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-mooring-iteration: 1. Scipy Optimization (Recommended) (+2).
Teams using orcaflex-mooring-iteration-1-scipy-optimization-recommended 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-scipy-optimization-recommended/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-mooring-iteration-1-scipy-optimization-recommended Compares
| Feature / Agent | orcaflex-mooring-iteration-1-scipy-optimization-recommended | 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?
Sub-skill of orcaflex-mooring-iteration: 1. Scipy Optimization (Recommended) (+2).
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
# 1. Scipy Optimization (Recommended) (+2) ## 1. Scipy Optimization (Recommended) Uses `scipy.optimize.fsolve` for robust multi-variable root finding. **Best for:** - Well-behaved systems - Multiple lines with coupling - Fast convergence ## 2. Newton-Raphson Multi-dimensional Newton-Raphson with numerical Jacobian. **Best for:** - Systems needing fine control - Cases with known sensitivity - Debugging convergence issues ## 3. EA-Based (Catenary Theory) Uses effective axial stiffness from catenary equations. **Best for:** - Simple catenary systems - Initial estimates - Lightweight iteration
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