Agent skill

aqwa-analysis

Integrate with AQWA hydrodynamic software for RAO computation, damping analysis, and coefficient extraction. Use for AQWA file processing, RAO calculation, hydrodynamic coefficient extraction, and pre/post processing workflows.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill aqwa-analysis-vamseeachanta-digitalmodel-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 18 KB
Bundled scripts: none
Version: 3.0.0
Path: skills/analysis/aqwa-analysis-vamseeachanta-digitalmodel-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Integrates with ANSYS AQWA to compute RAOs, extract added mass and damping matrices, and manage hydrodynamic coefficients. It processes AQWA output files (LIS, DAT, MES) and supports pre/post-processing workflows, including diffraction/radiation, viscosity damping, and coupled analyses.

How it works

The skill provides a structured workflow to:

  • Parse AQWA output files (LIS, DAT, MES) and load results via provided readers and analyzers.
  • Extract RAOs for specified motions and directions, producing CSV/plots and export options (e.g., OrcaFlex format).
  • Retrieve hydrodynamic coefficients: added mass and damping matrices at chosen frequencies, and output to CSV/JSON files.
  • Process AQWA files: identify and extract requested items (RAOs, added mass, damping, wave forces, drift forces) and write results to an output directory.
  • Compute viscous damping using empirical methods or decay tests, with vessel geometry inputs and motions; output damping data.
  • Provide a Python API for a modular pipeline: AqwaAnalysis (router), AqwaRAOs, AqwaReader, AqwaPreProcess, AqwaPostProcess, AqwaValidator.
  • Support pre-processing (input generation from geometry) and post-processing (comprehensive reports and validation).

When to use it

Use for AQWA post-processing, RAO computation, hydrodynamic coefficient extraction, and AQWA file processing for LIS/DAT/MES. It also enables added mass/damping extraction, viscous damping estimation, and pre/post processing workflows, including diffraction/radiation analysis (AQWA-LINE), time-domain motions (AQWA-DRIFT), stability analysis (AQWA-LIBRIUM), cable dynamics (AQWA-NAUT), and coupled analyses (AQWA-WAVE).

What it can touch

The skill integrates agent capabilities from /agents/aqwa/ and utilizes tools labeled as claude-code for implementation. It touches AQWA output files (LIS, DAT, MES) via its readers and processors and can export results to ORCAFlex-compatible formats. It also references internal Python modules such as digitalmodel.aqwa.* for RAO extraction, reading, preprocessing, postprocessing, and validation.

Caveats

Requires Python 3.10+ and a compatible AQWA environment with an ANSYS AQWA license, plus AQWA output files (LIS, DAT, MES). Dependencies specify a hydrodynamics package within a controlled range. Detailed behavior relies on the presence and structure of AQWA output files and the availability of the related digitalmodel modules.

From the SKILL.md

# AQWA Analysis Skill Integrate with ANSYS AQWA hydrodynamic software for RAO computation, added mass/damping extraction, and hydrodynamic coefficient management. ## Version Metadata ```yaml version: 3.0.0 python_min_version: '3.10' dependencies: hydrodynamics: '>=1.0.0,<2.0.0' compatibility: tested_python: - '3.10' - '3.11' - '3.12' - '3.13' os: - Windows - Linux - macOS ``` ## Changelog ### [3.0.0] - 2026-01-07 **Added:** - Initial version metadata and dependency management - Semantic versioning support - Compatibility information for Python 3.10-3.13 **Changed:** - Enhanced skill documentation structure ## When to Use - AQWA hydrodynamic analysis post-processing - RAO (Response Amplitude Operator) computation - Hydrodynamic coefficient extraction - AQWA file processing (LIS, DAT, MES) - Added mass and damping matrix extraction - Viscous damping determination - Pre/post processing workflows - Diffraction/radiation analysis (AQWA-LINE) - Time domain motions (AQWA-DRIFT) - Stability analysis (AQWA-LIBRIUM) - Cable dynamics (AQWA-NAUT) - Coupled analysis (AQWA-WAVE) ## Agent Capabilities This skill integrates agent capabilities from `/agents/aqwa/`: ### Domain Expertise - **Software

What's inside
Steps it walks through
  1. Version Metadata
  2. Changelog
  3. [3.0.0] - 2026-01-07
  4. When to Use
  5. Agent Capabilities
  6. Domain Expertise
  7. Core Capabilities
  8. Industry Standards
  9. Context Optimization
  10. Prerequisites
  11. Analysis Types
  12. 1. RAO Extraction
  13. 2. Hydrodynamic Coefficients
  14. 3. AQWA File Processing
Ships with 1 file
  • metadata.json
Commands it runs
Run peak-focused comparison (AQWA vs OrcaWave)
cd docs/modules/orcawave/L01_aqwa_benchmark
python run_comparison_peaks.py
Run comprehensive heading-by-heading comparison
python run_proper_comparison.py
More from claude-skill-registry
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About this skill
What does the aqwa-analysis skill do?

Integrate with AQWA hydrodynamic software for RAO computation, damping analysis, and coefficient extraction. Use for AQWA file processing, RAO calculation, hydrodynamic coefficient extraction, and pre/post processing workflows.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill aqwa-analysis-vamseeachanta-digitalmodel-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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