orcaflex-rao-import-orcaflex-yaml-format
Sub-skill of orcaflex-rao-import: OrcaFlex YAML Format (+2).
Best use case
orcaflex-rao-import-orcaflex-yaml-format is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-rao-import: OrcaFlex YAML Format (+2).
Teams using orcaflex-rao-import-orcaflex-yaml-format 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/orcaflex-yaml-format/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-rao-import-orcaflex-yaml-format Compares
| Feature / Agent | orcaflex-rao-import-orcaflex-yaml-format | 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-rao-import: OrcaFlex YAML Format (+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
# OrcaFlex YAML Format (+2)
## OrcaFlex YAML Format
```yaml
VesselTypes:
- Name: FPSO_RAOs
DisplacementRAOs:
RAOOrigin: [150.0, 0.0, 0.0] # meters from vessel origin
Directions: [0, 45, 90, 135, 180] # degrees
Periods: [5.0, 7.0, 10.0, 12.0, 15.0, 20.0] # seconds
# Amplitude and Phase for each DOF
# Format: [heading][period]
*See sub-skills for full details.*
## DataFrame Export
```python
# Export to DataFrame for analysis
df = processor.to_dataframe(rao_data)
# Multi-level columns: (DOF, Heading)
# Index: Frequency
print(df.head())
# Surge Sway
# 0 45 90 0 45 90
# 0.02 0.95 0.92 0.88 0.02 0.45 0.92
# 0.05 0.92 0.90 0.85 0.05 0.48 0.95
```
## Validation Report
```json
{
"is_valid": false,
"issues": [
{
"type": "amplitude_exceeded",
"dof": "roll",
"heading": 90,
"frequency": 0.6,
"value": 55.2,
*See sub-skills for full details.*Related Skills
OrcaFlex Specialist Skill
```yaml
orcaflex-reporting-fixture-proof-pattern
Build and extend fixture-backed OrcaFlex reporting proof paths in digitalmodel using stable metadata baselines, normalized HTML snapshots, and reusable reporting test helpers.
digitalmodel-orcawave-orcaflex-proof-workflows
Class-level digitalmodel OrcaWave/OrcaFlex readiness, semantic-proof, fixture-proof, and closeout workflows.
orcawave-orcaflex-readiness-audit
Audit the real readiness of digitalmodel OrcaWave/OrcaFlex spec-driven workflows by reconciling workspace-hub issues, source/tests, semantic-equivalence boundaries, and wiki synthesis gaps.
python-import-path-mismatch-debugging
Diagnose and fix ModuleNotFoundError when a package is installed but imports still fail due to environment/path mismatches
python-import-path-debugging
Diagnose ModuleNotFoundError when a package is installed but still fails to import
multi-format-transaction-parser
Parse and consolidate financial transaction data across multiple CSV formats and years
multi-format-csv-parser-with-deduplication
Parse brokerage CSV exports that exist in multiple formats with overlapping data across files
multi-format-csv-detection-and-deduplication
Detect and handle multiple CSV format versions from the same data source; deduplicate records across format variants
diagnose-venv-shebang-import-errors
Debugging pattern for ModuleNotFoundError when CLI entry points use wrong Python interpreter
diagnose-shebang-virtualenv-import-errors
Debugging pattern for ModuleNotFoundError when CLI entry points use wrong Python interpreter
diagnose-shebang-venv-import-errors
Troubleshoot ModuleNotFoundError in CLI tools by identifying shebang-venv mismatches