xiaohongshu
xiaohongshu
math-intuition-builder
Develops mathematical understanding through examples, visualization, and analogy
Full skill instructions
Trigger on phrases like:
Use before computation to establish understanding (Polya's "understand the problem" phase).
Guide through Mason's specializing-generalizing cycle:
Ask: "Can you state the problem in your own words?"
Ask: "What would a concrete example look like?"
Ask: "What form should the answer take?"
Ask: "Can you draw a picture or diagram?"
Ask: "What's a simpler version of this problem?"
Ask: "Does this remind you of anything you've solved before?"
Understanding Checkpoint:
Restated Problem:
[User's words, verified]
Concrete Instance:
[Specific numerical example]
Visual Interpretation:
[Description or ASCII diagram]
Simplified Version:
[Special case that's easier]
Analogous Problems:
[Similar structures from other domains]
Intuition Summary:
[1-2 sentence "aha" insight]
Ready to Proceed:
- Next step: [specific action]
- Verify by: [sanity check]
User: I'm trying to understand why eigenvalues matter for stability
Claude: Let's build intuition step by step.
Q1: Restate the problem?
> I have x' = Ax and want to know if solutions grow or shrink.
Q2: Let's try a concrete example.
A = [[0, 1], [-1, 0]]
Computing eigenvalues: +/- i (purely imaginary)
What happens to solutions?
> They neither grow nor shrink?
Exactly. Solutions are circles - oscillation without growth.
Q3: Now try A = [[-1, 0], [0, -1]]
Eigenvalues: -1, -1 (negative real)
What happens?
> Shrinks to zero.
Yes. Negative real parts = decay.
Q4: Visual pattern:
Im
| x unstable
|
---+------- Re
|
| x stable
Re(lambda) < 0: stable (decay)
Re(lambda) = 0: marginal (oscillate)
Re(lambda) > 0: unstable (blow up)
Q5: Analogies:
- Springs: eigenvalues = decay rates
- Populations: growth/death rates
- Circuits: time constants
Intuition Summary:
Eigenvalues are the system's "DNA" - they encode fundamental rates.
Look at real parts for stability.
Ready to Proceed:
- Next: Analyze your specific A matrix
- Verify: trace(A) < 0 and det(A) > 0 implies stability (2x2 case)
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