phase-portrait-generator
Generate phase portraits for 2D dynamical systems. Use when visualizing vector fields, nullclines, and trajectories.
Best use case
phase-portrait-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate phase portraits for 2D dynamical systems. Use when visualizing vector fields, nullclines, and trajectories.
Teams using phase-portrait-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/phase-portrait-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How phase-portrait-generator Compares
| Feature / Agent | phase-portrait-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 phase portraits for 2D dynamical systems. Use when visualizing vector fields, nullclines, and trajectories.
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
# Phase Portrait Generator
Generates phase portraits showing vector fields and trajectories in 2D state space.
## When to Use
- Visualizing 2D autonomous systems
- Plotting nullclines and equilibria
- Trajectory analysis in phase space
## GF(3) Role
PLUS (+1) Generator - creates visual outputs from differential equations.
## Quick Example
```python
import numpy as np
import matplotlib.pyplot as plt
def phase_portrait(f, xlim=(-3,3), ylim=(-3,3), density=20):
x = np.linspace(*xlim, density)
y = np.linspace(*ylim, density)
X, Y = np.meshgrid(x, y)
U, V = f(X, Y)
plt.streamplot(X, Y, U, V, density=1.5)
plt.xlabel('x'); plt.ylabel('y')
# Van der Pol oscillator
phase_portrait(lambda x, y: (y, -x + (1 - x**2) * y))
```
## Integration with bifurcation skills
Forms triad with:
- `bifurcation` (0): detects transitions
- `bifurcation-generator` (+1): parameter space
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