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Convert OrcaFlex .dat binary model files to enriched YAML fixtures using worldenergydata public databases (vessel fleet, riser components, pipe schedules). All stages run on licensed-win-1. Output committed to digitalmodel as test fixtures.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill orcaflex-enrichment명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
SOC 직업 분류 기준
| name | orcaflex-enrichment |
| version | 1.0.0 |
| description | Convert OrcaFlex .dat binary model files to enriched YAML fixtures using worldenergydata public databases (vessel fleet, riser components, pipe schedules). All stages run on licensed-win-1. Output committed to digitalmodel as test fixtures. |
| type | reference |
| triggers | ["orcaflex dat to yaml","enrich orcaflex","extract orcaflex model","orcaflex pipeline fixtures"] |
| computer | licensed-win-1 |
| repos | ["workspace-hub","worldenergydata","digitalmodel","client-c"] |
| related_wrk | ["WRK-589","WRK-593","WRK-594","WRK-595"] |
| tags | [] |
| category | data |
Converts binary OrcaFlex .dat model files to enriched, client-clean YAML
test fixtures using worldenergydata public databases.
Stage 1 Extract .dat + OrcFxAPI → raw YAML params
Stage 2 Enrich raw YAML + worldenergydata lookups → enriched YAML → client-c/
Stage 3 Clean enriched YAML → strip client names → legal scan → digitalmodel/
python scripts\data\orcaflex\dat-to-yaml.py ^
--input "\\dev-secondary\dde\Orcaflex\0000 Drilling Riser Development\Latest" ^
--output "client-c\data\raw\orcaflex-extracted\drilling-riser-development" ^
--project drilling-riser-development
Extracts: general, environment, lines[], vessels[] per .dat file.
python scripts\data\orcaflex\enrich-and-clean.py ^
--input client-c\data\raw\orcaflex-extracted\ ^
--output digitalmodel\data\orcaflex\ ^
--dry-run
| Object | OrcaFlex field | worldenergydata lookup | Result field |
|---|---|---|---|
| Line | OD (m→in) | DrillingRiserLoader.filter_by_size(od_in) | component_type, grade_range, api_standard |
| Line | OD + WT | PipelineSpecLookup.match_od_wt(od_m, wt_m) | nps_in, schedule, grade_range |
| Vessel | type hint from context | BSEE rig fleet .bin | vessel_class, water_depth_rating_m, dp_class |
lines:
- component_type: marine_drilling_riser_joint
nps_in: 21.0
public_match:
[, ]
[]
[]
[, , ]
vessels:
- vessel_class: drillship
water_depth_rating_m: 3658
dp_class: DP3
public_source: worldenergydata.vessel_fleet.BSEE_rig_fleet
dat_properties:
InitialX: 0.0
InitialY: 0.0
InitialZ: 0.0
# name: stripped
bash scripts/legal/legal-sanity-scan.sh digitalmodel/data/orcaflex/
cd digitalmodel
git add data/orcaflex/
git commit -m "data(orcaflex): enriched fixtures from public vessel/riser databases"
| Project | .dat count | Standards | worldenergydata lookup |
|---|---|---|---|
0000 Drilling Riser Dev/Latest | 31 | DNV-OS-F201 | DrillingRiserLoader (21" joints) |
31290 WellHead Fatigue/BOP on Wellhead | 240 | API-RP-2A-WSD | DrillingRiserLoader (BOP) |
611 Mecor S Lay/Reference | 3 | DNV-OS-F101 | PipelineSpecLookup (pipe OD) |
5 - OrcaFlex API Check | 1 | — | minimal fixture |
| Dataset | Status | Location |
|---|---|---|
drilling_riser_components.csv | 24 rows (partial) | data/modules/vessel_fleet/curated/ |
rig_fleet_full.bin | 2,320 rigs (Feb 2026) | data/modules/bsee/.local/rig_fleet/ |
construction_vessels.csv | missing — see WRK-593 | data/modules/vessel_fleet/curated/ |
api_5l_pipe_schedule.csv | missing — see WRK-594 | data/modules/pipeline/ |
from worldenergydata.vessel_fleet import DrillingRiserLoader
loader = DrillingRiserLoader()
# Match riser joint by OD
joints = loader.filter_by_size(od_in=21.0)
# Get all riser joints
all_joints = loader.get_riser_joints()
# Get BOPs
bops = loader.get_bops()
# Pipeline spec lookup (after WRK-594)
from worldenergydata.bsee.pipeline import PipelineSpecLookup
lookup = PipelineSpecLookup()
spec = lookup.match_od_wt(od_m=0.2731, wt_m=0.012)
# → {nps_in: 10.75, schedule: "XS", grade_range: ["X60", "X65"], ...}
llm-wiki base: data/llm-wiki/orcaflex/
Relevant OrcaFlex topics:
Textdatafiles.md -- YAML/text data file format for .dat extractionVesseldata.md, Vesseltypes.md -- vessel object properties extracted in Stage 1Lines.md, Linedata.md, Linetypes,Geometry,massexpansiondata.md -- line object properties (OD, WT)Environment.md, Generaldata.md -- general/environment parametersOrcFxAPI topics (data/llm-wiki/orcfxapi/):
Modelbuilding,Settingthedata.md -- programmatic data extraction via OrcFxAPIPythoninterface,Objectdata.md -- Python object data access for .dat readingWrite outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.