mooring-analysis-1-mooring-types
Sub-skill of mooring-analysis: 1. Mooring Types (+1).
Best use case
mooring-analysis-1-mooring-types is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of mooring-analysis: 1. Mooring Types (+1).
Teams using mooring-analysis-1-mooring-types 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-mooring-types/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mooring-analysis-1-mooring-types Compares
| Feature / Agent | mooring-analysis-1-mooring-types | 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 mooring-analysis: 1. Mooring Types (+1).
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. Mooring Types (+1)
## 1. Mooring Types
**Catenary Mooring:**
```yaml
characteristics:
restoring_force: "Weight of suspended line"
typical_water_depth: "< 2000m"
materials: ["chain", "wire", "combination"]
advantages:
- Simple and reliable
- Well-proven technology
- Good energy absorption
disadvantages:
- Large footprint
- Heavy at great depths
```
**Taut Mooring:**
```yaml
characteristics:
restoring_force: "Elastic elongation"
typical_water_depth: "Any depth"
materials: ["polyester", "steel wire"]
advantages:
- Small footprint
- Suitable for ultra-deep water
- Lower weight
disadvantages:
- Complex dynamics
- Requires higher pretension
- More sensitive to installation
```
## 2. Catenary Equations
**Basic Catenary:**
```python
import numpy as np
def catenary_profile(
horizontal_tension: float, # kN
weight_per_length: float, # kN/m
length_on_seabed: float = 0 # m
) -> dict:
"""
Calculate catenary mooring line profile.
Catenary equations:
- x = a * sinh(s/a)
- z = a * (cosh(s/a) - 1)
Where a = H/w (catenary parameter)
Args:
horizontal_tension: Horizontal tension at touchdown
weight_per_length: Weight per unit length in water
length_on_seabed: Length of line on seabed
Returns:
Catenary parameters
"""
# Catenary parameter
a = horizontal_tension / weight_per_length
return {
'catenary_parameter_m': a,
'horizontal_tension_kN': horizontal_tension,
'weight_per_length_kN_m': weight_per_length
}
def catenary_suspended_length(
water_depth: float,
horizontal_distance: float,
horizontal_tension: float,
weight_per_length: float
) -> float:
"""
Calculate suspended length of catenary line.
Solve: z = a(cosh(x/a) - 1) for length s
Args:
water_depth: Water depth
horizontal_distance: Horizontal distance to anchor
horizontal_tension: Horizontal tension
weight_per_length: Weight per length
Returns:
Suspended line length
"""
from scipy.optimize import fsolve
a = horizontal_tension / weight_per_length
def equations(s):
# Horizontal: x = a*sinh(s/a)
# Vertical: z = a*(cosh(s/a) - 1)
eq1 = a * np.sinh(s/a) - horizontal_distance
eq2 = a * (np.cosh(s/a) - 1) - water_depth
return [eq1, eq2]
# Initial guess
s0 = np.sqrt(horizontal_distance**2 + water_depth**2)
# Solve
solution = fsolve(equations, s0)[0]
return solution
def catenary_top_tension(
water_depth: float,
horizontal_tension: float,
weight_per_length: float
) -> float:
"""
Calculate tension at top of catenary line.
T_top = sqrt(H² + (w*z)²)
Args:
water_depth: Water depth
horizontal_tension: Horizontal tension
weight_per_length: Weight per length
Returns:
Top tension in kN
"""
vertical_component = weight_per_length * water_depth
T_top = np.sqrt(horizontal_tension**2 + vertical_component**2)
return T_top
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