digitalmodel-orcawave-orcaflex-proof-workflows

Class-level digitalmodel OrcaWave/OrcaFlex readiness, semantic-proof, fixture-proof, and closeout workflows.

5 stars

Best use case

digitalmodel-orcawave-orcaflex-proof-workflows is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Class-level digitalmodel OrcaWave/OrcaFlex readiness, semantic-proof, fixture-proof, and closeout workflows.

Teams using digitalmodel-orcawave-orcaflex-proof-workflows 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

$curl -o ~/.claude/skills/digitalmodel-orcawave-orcaflex-proof-workflows/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.claude/skills/digitalmodel/digitalmodel-orcawave-orcaflex-proof-workflows/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/digitalmodel-orcawave-orcaflex-proof-workflows/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How digitalmodel-orcawave-orcaflex-proof-workflows Compares

Feature / Agentdigitalmodel-orcawave-orcaflex-proof-workflowsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Class-level digitalmodel OrcaWave/OrcaFlex readiness, semantic-proof, fixture-proof, and closeout workflows.

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

# Digitalmodel Orcawave Orcaflex Proof Workflows

## When to Use
Use for digitalmodel readiness audits, OrcaWave/OrcaFlex semantic proof waves, fixture-backed reporting proof, closeout verification, and marine-engineering calculation reports that turn legacy workbook/source-pack logic into tested digitalmodel inputs, charts, and report artifacts.

## Class-Level Workflow
1. Separate semantic proof readiness from production solver readiness.
2. Use fixture-backed reports to prove parser/reporting behavior before external solver assumptions.
3. Close waves only after local artifacts, issue comments, and follow-up blockers are reconciled.

## Consolidated Session Learnings

The `references/` directory contains archived narrow skills absorbed during the 2026-04-29 umbrella consolidation pass. Use the subsections below as the class-level index, then open the named reference when a case-specific recipe is needed.
## Absorbed Narrow Skills (2026-04-29)

### `orcawave-orcaflex-readiness-audit`

- Former skill demoted to `references/orcawave-orcaflex-readiness-audit.md`.
- Preserved insight: 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.

### `orcawave-orcaflex-semantic-proof-wave-closeout`

- Former skill demoted to `references/orcawave-orcaflex-semantic-proof-wave-closeout.md`.
- Preserved insight: Close out an OrcaWave/OrcaFlex semantic-proof wave after a PR merges, split unrelated CI blockers, and seed the next semantic-proof issue wave without duplicating existing issues.

Related Skills

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```yaml

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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.

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Close out an OrcaWave/OrcaFlex semantic-proof wave after a PR merges, split unrelated CI blockers, and seed the next semantic-proof issue wave without duplicating existing issues.

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Viscous damping analysis for OrcaWave. Use when running parametric damping sweeps, optimizing roll damping coefficients, computing critical damping, or comparing damping results with model test data for vessel motion tuning.