orcaflex-modal-analysis-expected-frequency-ranges
Sub-skill of orcaflex-modal-analysis: Expected Frequency Ranges (+1).
Best use case
orcaflex-modal-analysis-expected-frequency-ranges is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-modal-analysis: Expected Frequency Ranges (+1).
Teams using orcaflex-modal-analysis-expected-frequency-ranges 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/expected-frequency-ranges/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-modal-analysis-expected-frequency-ranges Compares
| Feature / Agent | orcaflex-modal-analysis-expected-frequency-ranges | 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-modal-analysis: Expected Frequency Ranges (+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
# Expected Frequency Ranges (+1) ## Expected Frequency Ranges | Structure Type | Typical Period Range | Notes | |---------------|---------------------|-------| | SCR (Steel Catenary Riser) | 2-30s | In-line and cross-flow modes | | Mooring line | 5-60s | Depends on length and pretension | | FPSO (hull) | 5-15s (heave/pitch) | Compare with RAO peaks | | TTR (Top Tensioned Riser) | 1-20s | Higher tension = higher frequency | | Jumper | 1-10s | Short span, higher frequencies | ## Validation Checks | Check | Method | Pass Criteria | |-------|--------|---------------| | Fundamental mode period | Compare with analytical catenary formula | Within 20% of T = 2L/n * sqrt(m/T) | | Mode shape continuity | Plot mode shapes | No discontinuities or jumps | | VIV susceptibility | Check 4 < Vr < 8 for any mode | Flag modes with lock-in risk | | DOF energy sum | Sum DOF percentages per mode | Should sum to ~100% | | Symmetric model | Check paired modes | Degenerate pairs should have similar periods | ```python def validate_modal_results(modes, expected_period_range=(1.0, 60.0)): *See sub-skills for full details.*
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