hydrodynamic-pipeline-checkpoint-1-mesh-quality
Sub-skill of hydrodynamic-pipeline: Checkpoint 1: Mesh Quality (+2).
Best use case
hydrodynamic-pipeline-checkpoint-1-mesh-quality is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of hydrodynamic-pipeline: Checkpoint 1: Mesh Quality (+2).
Teams using hydrodynamic-pipeline-checkpoint-1-mesh-quality 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/checkpoint-1-mesh-quality/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hydrodynamic-pipeline-checkpoint-1-mesh-quality Compares
| Feature / Agent | hydrodynamic-pipeline-checkpoint-1-mesh-quality | 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 hydrodynamic-pipeline: Checkpoint 1: Mesh Quality (+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
# Checkpoint 1: Mesh Quality (+2)
## Checkpoint 1: Mesh Quality
```python
def validate_panel_mesh(gdf_path, min_panels=200, max_aspect=3.0):
"""Validate panel mesh before diffraction analysis."""
checks = {"passed": True, "issues": []}
# Parse GDF and count panels
# Check aspect ratios, normal directions, waterline closure
# (implementation depends on mesh format)
return checks
```
## Checkpoint 2: Diffraction Results
```python
def validate_diffraction_results(results):
"""Validate diffraction results before passing to OrcaFlex."""
checks = {"passed": True, "issues": []}
# RAO sanity checks
heave_rao_at_long_period = results["raos"]["Heave"]["amplitude"][-1]
if abs(heave_rao_at_long_period - 1.0) > 0.1:
checks["issues"].append(
f"Heave RAO at long period = {heave_rao_at_long_period:.3f} "
"(expected ~1.0 — vessel should follow wave at low frequency)"
)
# Added mass should be positive on diagonal
# Damping should be positive (energy dissipation)
# Roll/pitch RAO should have clear resonance peak
# Check for numerical issues
for dof, data in results["raos"].items():
if any(a > 100 for a in data["amplitude"]):
checks["issues"].append(f"{dof} RAO has spikes > 100 — likely resonance without damping")
checks["passed"] = False
return checks
```
## Checkpoint 3: OrcaFlex Results
```python
def validate_orcaflex_moored_results(sim_path):
"""Validate final OrcaFlex simulation results."""
import OrcFxAPI
model = OrcFxAPI.Model()
model.LoadSimulation(sim_path)
checks = {"passed": True, "issues": []}
# Check vessel offset
vessel = model['FPSO']
max_surge = max(abs(vessel.TimeHistory('X', OrcFxAPI.oeEndA)))
if max_surge > 50:
checks["issues"].append(f"Max surge = {max_surge:.1f}m — check mooring stiffness")
# Check mooring tensions
for obj in model.objects:
if obj.typeName == 'Line' and 'Mooring' in obj.name:
max_tension = max(obj.TimeHistory('Effective tension', OrcFxAPI.oeEndA))
min_tension = min(obj.TimeHistory('Effective tension', OrcFxAPI.oeEndB))
if min_tension < 0:
checks["issues"].append(f"{obj.name}: negative tension (compression) — line may be slack")
checks["passed"] = False
return checks
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