orcaflex-modal-analysis-mode-shapes-csv
Sub-skill of orcaflex-modal-analysis: Mode Shapes CSV (+2).
Best use case
orcaflex-modal-analysis-mode-shapes-csv is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-modal-analysis: Mode Shapes CSV (+2).
Teams using orcaflex-modal-analysis-mode-shapes-csv 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/mode-shapes-csv/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-modal-analysis-mode-shapes-csv Compares
| Feature / Agent | orcaflex-modal-analysis-mode-shapes-csv | 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?
Sub-skill of orcaflex-modal-analysis: Mode Shapes CSV (+2).
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
# Mode Shapes CSV (+2)
## Mode Shapes CSV
```csv
modeIndex,name,node,dof,shapeWrtGlobal
0,Riser1,1,X,0.0012
0,Riser1,1,Y,0.0001
0,Riser1,1,Z,0.8523
0,Riser1,2,X,0.0015
...
```
## Mode Summary CSV
```csv
modeIndex,period,name,abs_max_dof,max_dof_values,max_dof_nodes,max_dof_percentages,modes_selected
0,8.523,Riser1,0.852,{'X': 0.001, 'Y': 0.0, 'Z': 0.852},{'X': 1, 'Y': 1, 'Z': 75},{'X': 0.1, 'Y': 0.0, 'Z': 99.8},{'X': False, 'Y': False, 'Z': True}
1,5.234,Riser1,0.723,{'X': 0.723, 'Y': 0.001, 'Z': 0.05},{'X': 50, 'Y': 1, 'Z': 1},{'X': 99.5, 'Y': 0.1, 'Z': 0.4},{'X': True, 'Y': False, 'Z': False}
```
## DOF-Filtered Summary
Output file: `{model_name}_modes_summary_{dof}.csv`
Contains only modes where the specified DOF exceeds the threshold percentage.Related Skills
OrcaFlex Specialist Skill
```yaml
mnt-analysis-cleanup
Survey, classify, and clean up `/mnt/local-analysis/` (or any sibling-to-workspace-hub directory holding orphan worktrees, codex-burn artifacts, agent log accumulations, and outer-clone duplicates) without losing useful code/work. Surfaces a tiered approval menu rather than baking decisions; defers all destructive ops until user confirms.
orcaflex-reporting-fixture-proof-pattern
Build and extend fixture-backed OrcaFlex reporting proof paths in digitalmodel using stable metadata baselines, normalized HTML snapshots, and reusable reporting test helpers.
digitalmodel-worktree-test-execution-with-shared-venv
Run digitalmodel tests from isolated worktrees without uv editable-dependency failures by using the main repo's existing virtualenv and PYTHONPATH.
digitalmodel-orcawave-orcaflex-proof-workflows
Class-level digitalmodel OrcaWave/OrcaFlex readiness, semantic-proof, fixture-proof, and closeout workflows.
digitalmodel-code-explorer
Fast orientation guide for the digitalmodel codebase, with module lookup, source-to-test mapping, and targeted inspection patterns to avoid repeated bulk-reading of digitalmodel/src and tests.
orcawave-orcaflex-readiness-audit
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.
digitalmodel-orcawave-orcaflex-workflow
Current-state workflow for navigating and extending digitalmodel OrcaWave/OrcaFlex capabilities across code, tests, issues, queue tooling, and licensed-machine boundaries.
orcawave-orcaflex-semantic-proof-wave-closeout
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.
godmode
Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Hermes-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.
segment-anything-model
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.