orcaflex-model-sanitization
Sanitize OrcaFlex models by stripping client-identifiable references, converting binary .dat to YAML .yml, and organizing into the reference model library.
Best use case
orcaflex-model-sanitization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sanitize OrcaFlex models by stripping client-identifiable references, converting binary .dat to YAML .yml, and organizing into the reference model library.
Teams using orcaflex-model-sanitization 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/model-sanitization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-model-sanitization Compares
| Feature / Agent | orcaflex-model-sanitization | 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?
Sanitize OrcaFlex models by stripping client-identifiable references, converting binary .dat to YAML .yml, and organizing into the reference model library.
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 Model Sanitization Skill
## Description
Sanitize OrcaFlex models from client projects by stripping identifiable references (project names, vessel names, operator codes, user/machine metadata), converting binary `.dat` to YAML `.yml`, deduplicating, and organizing into the reference model library.
## When to Use
- Importing OrcaFlex models from external/client sources
- Converting `.dat` (binary) to `.yml` (YAML) format
- Stripping client-identifiable references before committing
- Organizing models into the `docs/modules/orcaflex/` library structure
- Deduplicating model collections (hash-based)
## Sanitization Pipeline
### Overview
```
Source .dat/.yml → Dedup → Convert → Sanitize Text → Strip Metadata → Organize → Legal Scan → Extract Spec
```
### Step 1: Inventory & Dedup
- Walk source directory for `.dat` and `.yml` files
- Compute SHA-256 hash for each file
- Skip files with identical content (keep first occurrence)
- Exclude: oversized files (>50MB), non-model files (CAD, reports, scripts)
### Step 2: Format Conversion (.dat → .yml)
```python
import OrcFxAPI
model = OrcFxAPI.Model(str(dat_path))
model.SaveData(str(output_yml_path))
```
**Gotchas:**
- `SaveData()` embeds `User:`, `Machine:`, `File:` metadata in YAML header
- `SaveData()` exports ALL properties including dormant ones
- Binary `.dat` files may reference external DLLs or data files
- Large models (>50MB) may timeout — skip and log
### Step 3: Text-Based Sanitization
Apply regex replacements using a mapping table:
```python
SANITIZATION_MAP = {
"ClientProject": "generic_project_a",
"VesselName": "installation_vessel_01",
# ... sorted by key length descending to avoid partial matches
}
# Sort keys longest-first
sorted_keys = sorted(SANITIZATION_MAP.keys(), key=len, reverse=True)
text = Path(yml_file).read_text(encoding="utf-8", errors="replace")
for key in sorted_keys:
replacement = SANITIZATION_MAP[key]
text = text.replace(key, replacement)
```
**Critical: Sort by length descending** — prevents "Seven Arctic" from being partially matched by "Seven".
### Step 4: Strip OrcaFlex Metadata
Remove embedded user/machine/file path lines:
```python
import re
# Remove User/Machine/File header lines
text = re.sub(r"^User:.*$", "", text, flags=re.MULTILINE)
text = re.sub(r"^Machine:.*$", "", text, flags=re.MULTILINE)
text = re.sub(r"^File:.*$", "", text, flags=re.MULTILINE)
# Clean up resulting blank lines
text = re.sub(r"\n{3,}", "\n\n", text)
```
### Step 5: Organize into Library
Target structure:
```
docs/modules/orcaflex/<category>/
├── <subcategory>/
│ ├── monolithic/ # Sanitized original YAML
│ │ ├── model_SZ.yml
│ │ └── model_DZ.yml
│ └── spec.yml # Extracted spec for modular builder
```
Categories: `jumper/`, `installation/`, `mooring/`, `training/`, `regional/`, `vessel_raos/`
### Step 6: Legal Scan Gate
```bash
bash scripts/legal/legal-sanity-scan.sh --repo=digitalmodel
# Must exit 0 before proceeding to spec extraction
```
### Step 7: Extract Spec
Only after legal scan passes:
```python
from digitalmodel.solvers.orcaflex.modular_generator.extractor import MonolithicExtractor
extractor = MonolithicExtractor(Path("monolithic/model.yml"))
spec_dict = extractor.extract()
```
## Sanitization Mapping Best Practices
### Pattern Categories
| Category | Examples | Replacement Convention |
|----------|----------|----------------------|
| Project names | Field names, codenames | `deepwater_field_a`, `shallow_field_b` |
| Vessel names | Installation vessels, rigs | `installation_vessel_01`, `fpso_01` |
| Operator names | Oil companies, contractors | `operator_a`, `contractor_b` |
| Location names | Pipeline routes, regions | `pipeline_route_a`, `region_b` |
| User/machine IDs | Hostnames, usernames | Remove entirely (empty string) |
### Rules
1. **Longest match first** — sort replacement keys by length descending
2. **Case variants** — include both original case and common variants (CamelCase, snake_case, lowercase)
3. **Empty replacements** — for user/machine IDs, replace with empty string and clean up artifacts
4. **Audit trail** — log every transformation to `sanitization_audit.json`
## Deny List Integration
The sanitization mapping should be mirrored in `.legal-deny-list.yaml`:
- Every key in `SANITIZATION_MAP` → pattern in deny list with `severity: block`
- The sanitization script itself is excluded from scanning
## Commands
### Run sanitization
```bash
uv run python scripts/sanitize_s7_models.py \
--s7-root D:/workspace-hub/client-b/s7 \
--output-root docs/modules/orcaflex \
--dry-run # Preview first
```
### Run without OrcFxAPI (.yml only)
```bash
uv run python scripts/sanitize_s7_models.py \
--s7-root D:/workspace-hub/client-b/s7 \
--output-root docs/modules/orcaflex \
--skip-dat
```
### Verify sanitization
```bash
# Check no client references remain
grep -ri "ClientProject\|VesselName" docs/modules/orcaflex/
# Run legal scan
bash scripts/legal/legal-sanity-scan.sh --repo=digitalmodel
```
## Audit Output
The sanitization script generates `sanitization_audit.json`:
```json
{
"timestamp": "2026-02-11T10:00:00",
"source_root": "D:/workspace-hub/client-b/s7",
"files_processed": 180,
"files_skipped_dedup": 45,
"files_skipped_excluded": 12,
"files_failed": 3,
"categories": {
"jumper": 15,
"installation": 80,
"mooring": 10
},
"transformations": [
{
"source": "s7/ballymore/Jumper_Manifold to PLET/SZ.yml",
"target": "docs/modules/orcaflex/jumper/manifold_to_plet/monolithic/SZ.yml",
"hash": "sha256:abc123...",
"replacements": ["Ballymore→deepwater_field_a", "Candies→installation_vessel_01"],
"size_bytes": 245000
}
]
}
```
## Related Skills
- `/legal-sanity-scan` — Legal compliance scanning
- `/orcaflex-file-conversion` — Format conversion (.dat ↔ .yml)
- `/orcaflex-monolithic-to-modular` — Monolithic → modular conversion
- `/orcaflex-jumper-analysis` — Jumper-specific modelling concepts
- `/orcaflex-model-generator` — Modular generation from spec.ymlRelated Skills
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