signal-analysis-1-rainflow-cycle-counting-astm-e1049-85
Sub-skill of signal-analysis: 1. Rainflow Cycle Counting (ASTM E1049-85) (+4).
Best use case
signal-analysis-1-rainflow-cycle-counting-astm-e1049-85 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of signal-analysis: 1. Rainflow Cycle Counting (ASTM E1049-85) (+4).
Teams using signal-analysis-1-rainflow-cycle-counting-astm-e1049-85 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/1-rainflow-cycle-counting-astm-e1049-85/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How signal-analysis-1-rainflow-cycle-counting-astm-e1049-85 Compares
| Feature / Agent | signal-analysis-1-rainflow-cycle-counting-astm-e1049-85 | 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 signal-analysis: 1. Rainflow Cycle Counting (ASTM E1049-85) (+4).
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
# 1. Rainflow Cycle Counting (ASTM E1049-85) (+4)
## 1. Rainflow Cycle Counting (ASTM E1049-85)
Extract stress/load cycles for fatigue analysis using industry-standard rainflow algorithm.
```yaml
signal_analysis:
rainflow:
flag: true
input_file: "data/stress_time_history.csv"
time_column: "time"
signal_column: "stress"
output:
*See sub-skills for full details.*
## 2. FFT Spectral Analysis
Compute frequency content using Fast Fourier Transform.
```yaml
signal_analysis:
fft:
flag: true
input_file: "data/motion_time_history.csv"
time_column: "time"
signal_column: "heave"
output:
*See sub-skills for full details.*
## 3. Power Spectral Density (Welch Method)
Estimate power spectral density with reduced variance using overlapping segments.
```yaml
signal_analysis:
psd:
flag: true
input_file: "data/vessel_motion.csv"
time_column: "time"
signal_columns:
- "surge"
*See sub-skills for full details.*
## 4. Time Series Conditioning
Prepare raw time series for analysis with filtering and preprocessing.
```yaml
signal_analysis:
conditioning:
flag: true
input_file: "data/raw_signal.csv"
output_file: "data/conditioned_signal.csv"
operations:
- type: "resample"
*See sub-skills for full details.*
## 5. OrcaFlex Signal Batch Processing
Process multiple OrcaFlex time histories in parallel.
```yaml
signal_analysis:
orcaflex_batch:
flag: true
sim_directory: "results/.sim/"
sim_pattern: "*.sim"
variables:
- object: "Line1"
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Perform signal processing, rainflow cycle counting, and spectral analysis for fatigue and time series data. Use for analyzing stress time histories, computing FFT/PSD, extracting fatigue cycles (ASTM E1049-85), and batch processing OrcaFlex signals.
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