wave-theory-6-extreme-value-analysis
Sub-skill of wave-theory: 6. Extreme Value Analysis.
Best use case
wave-theory-6-extreme-value-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of wave-theory: 6. Extreme Value Analysis.
Teams using wave-theory-6-extreme-value-analysis 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/6-extreme-value-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How wave-theory-6-extreme-value-analysis Compares
| Feature / Agent | wave-theory-6-extreme-value-analysis | 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 wave-theory: 6. Extreme Value Analysis.
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
# 6. Extreme Value Analysis
## 6. Extreme Value Analysis
**Design Wave from Return Period:**
```python
def calculate_extreme_wave_height(
return_period_years: float,
Hs_annual_max: np.ndarray = None,
distribution: str = 'weibull'
) -> dict:
"""
Calculate design wave height for given return period.
Args:
return_period_years: Return period (years)
Hs_annual_max: Array of annual maximum Hs values
distribution: 'weibull' or 'gumbel'
Returns:
Extreme wave height statistics
"""
from scipy.stats import weibull_min, gumbel_r
if Hs_annual_max is None:
# Example data: 25 years of annual maxima
np.random.seed(42)
Hs_annual_max = weibull_min.rvs(c=2, scale=10, size=25)
# Fit distribution
if distribution == 'weibull':
params = weibull_min.fit(Hs_annual_max)
c, loc, scale = params
dist = weibull_min(c, loc, scale)
elif distribution == 'gumbel':
loc, scale = gumbel_r.fit(Hs_annual_max)
dist = gumbel_r(loc, scale)
else:
raise ValueError("Unknown distribution")
# Exceedance probability for return period
exceedance_prob = 1 / return_period_years
# Extreme value
Hs_extreme = dist.ppf(1 - exceedance_prob)
# Confidence intervals (simplified)
Hs_lower = dist.ppf(1 - exceedance_prob - 0.1)
Hs_upper = dist.ppf(1 - exceedance_prob + 0.1)
return {
'return_period_years': return_period_years,
'Hs_extreme': Hs_extreme,
'Hs_lower_bound': Hs_lower,
'Hs_upper_bound': Hs_upper,
'distribution': distribution,
'exceedance_probability': exceedance_prob
}
# Example: 100-year return period
extreme_100yr = calculate_extreme_wave_height(
return_period_years=100,
distribution='weibull'
)
print(f"100-Year Wave:")
print(f" Hs: {extreme_100yr['Hs_extreme']:.2f} m")
print(f" Range: {extreme_100yr['Hs_lower_bound']:.2f} - {extreme_100yr['Hs_upper_bound']:.2f} m")
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