orcaflex-spec-audit
Audit, classify, and score OrcaFlex spec.yml files across the model library for quality, schema validity, and structure type categorization.
Best use case
orcaflex-spec-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Audit, classify, and score OrcaFlex spec.yml files across the model library for quality, schema validity, and structure type categorization.
Teams using orcaflex-spec-audit 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/spec-audit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-spec-audit Compares
| Feature / Agent | orcaflex-spec-audit | 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?
Audit, classify, and score OrcaFlex spec.yml files across the model library for quality, schema validity, and structure type categorization.
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 Spec Library Audit
Audit and classify all `spec.yml` files in the OrcaFlex library. Scores quality (0-100), validates against `ProjectInputSpec` schema, and infers structural category from path and content signals.
## Usage
```bash
# Basic audit (YAML output)
uv run python scripts/audit_spec_library.py
# With HTML report
uv run python scripts/audit_spec_library.py --html benchmark_output/spec_audit.html
# Custom root directory
uv run python scripts/audit_spec_library.py --root docs/modules/orcaflex/library/templates
```
## Quality Scoring (0-100)
| Criterion | Points | Check |
|-----------|--------|-------|
| Schema valid | +30 | `ProjectInputSpec(**data)` passes |
| Has comments | +20 | Lines starting with `#` |
| Meaningful name | +15 | Not temp/single-char/digits-only |
| Meaningful description | +10 | Not "Extracted from...", not "generic", len >= 10 |
| No raw_properties | +15 | `environment.raw_properties` absent |
| Compact file | +10 | < 1000 lines |
## Category Inference
Priority order:
1. **Schema key**: `riser:` key -> riser, `pipeline:` key -> pipeline
2. **Path parts**: `tier2_fast` -> riser, `jumper` -> jumper, `training` -> training
3. **Model library prefix**: `a`=riser, `b`=drilling, `c`=mooring, `d-g`=installation, `h`=heavy_lift, `k`=wind_turbine, `l`=vessel, `m`=pipeline
4. **Metadata fallback**: `metadata.structure` field value
## Output
### YAML (`audit_results.yaml`)
```yaml
summary:
total: 81
avg_quality: 53.0
schema_valid_count: 73
by_category: {riser: 19, installation: 18, mooring: 10, ...}
specs:
- path: library/templates/riser_catenary/spec.yml
name: riser_catenary_template
quality_score: 100
schema_valid: true
category: riser
model_type: riser
line_count: 108
```
### HTML Report
Single-page report with summary cards, per-category tables, color-coded quality scores (green >= 70, yellow 40-69, red < 40).
## Template Library
6 curated templates at `docs/modules/orcaflex/library/templates/`:
| Template | Source | Lines |
|----------|--------|-------|
| riser_catenary | tier2_fast/a01_catenary_riser | 108 |
| riser_lazy_wave | tier2_fast/a01_lazy_wave_riser | 188 |
| pipeline_installation | pipeline/.../24in_pipeline | 101 |
| mooring_buoy | model_library/c07_metocean_buoy | 763 |
| installation_subsea | installation/mudmat | 196 |
| installation_pull_in | model_library/d02_pull_in_analysis | 645 |
All templates score 100/100 quality and pass schema validation.
## References
- Audit script: `scripts/audit_spec_library.py`
- Tests: `tests/scripts/test_audit_spec_library.py`
- Template catalog: `docs/modules/orcaflex/library/templates/catalog.yaml`
- Audit results: `docs/modules/orcaflex/library/templates/audit_results.yaml`
- Schema: `src/digitalmodel/solvers/orcaflex/modular_generator/schema/root.py`Related Skills
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