bifurcation-generator
Generate bifurcation diagrams for dynamical systems. Use when visualizing parameter-dependent behavior transitions.
Best use case
bifurcation-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate bifurcation diagrams for dynamical systems. Use when visualizing parameter-dependent behavior transitions.
Teams using bifurcation-generator 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/bifurcation-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bifurcation-generator Compares
| Feature / Agent | bifurcation-generator | 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?
Generate bifurcation diagrams for dynamical systems. Use when visualizing parameter-dependent behavior transitions.
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
# Bifurcation Generator
Generates bifurcation diagrams showing how system behavior changes with parameters.
## When to Use
- Visualizing Hopf, pitchfork, saddle-node bifurcations
- Parameter sweeps in dynamical systems
- Stability boundary identification
## GF(3) Role
PLUS (+1) Generator - creates visual outputs from system parameters.
## Quick Examples
```python
# Logistic map bifurcation
import numpy as np
import matplotlib.pyplot as plt
def logistic_bifurcation(r_min=2.5, r_max=4.0, steps=1000):
r_vals = np.linspace(r_min, r_max, steps)
x = 0.5
for r in r_vals:
for _ in range(100): # transient
x = r * x * (1 - x)
for _ in range(50): # attractor
x = r * x * (1 - x)
yield r, x
```
## Integration with bifurcation (0) skill
This skill (PLUS +1) pairs with `bifurcation` (ERGODIC 0) for balanced analysis:
- bifurcation: detects and classifies transitions
- bifurcation-generator: visualizes parameter spaceRelated Skills
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