signal-analysis-complete-signal-analysis-workflow
Sub-skill of signal-analysis: Complete Signal Analysis Workflow (+1).
Best use case
signal-analysis-complete-signal-analysis-workflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of signal-analysis: Complete Signal Analysis Workflow (+1).
Teams using signal-analysis-complete-signal-analysis-workflow 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/complete-signal-analysis-workflow/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How signal-analysis-complete-signal-analysis-workflow Compares
| Feature / Agent | signal-analysis-complete-signal-analysis-workflow | 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: Complete Signal Analysis Workflow (+1).
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
# Complete Signal Analysis Workflow (+1)
## Complete Signal Analysis Workflow
```yaml
basename: signal_analysis_workflow
signal_analysis:
# Step 1: Condition raw signals
conditioning:
flag: true
input_file: "data/raw_stress.csv"
output_file: "data/conditioned_stress.csv"
operations:
*See sub-skills for full details.*
## Fatigue-Focused Analysis
```yaml
signal_analysis:
rainflow:
flag: true
input_file: "data/stress_history.csv"
output:
cycles_file: "results/fatigue_cycles.csv"
summary_file: "results/fatigue_summary.json"
options:
hysteresis_filter: 0.01 # Filter cycles < 1% of range
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