with one click
math-intuition-builder
Develops mathematical understanding through examples, visualization, and analogy
Develops mathematical understanding through examples, visualization, and analogy
Create git commits with user approval and no Claude attribution
Create or update continuity ledger for state preservation across clears
Create handoff document for transferring work to another session
Generate comprehensive PR descriptions following repository templates
Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.
Search Mathlib for lemmas by type signature pattern
| name | math-intuition-builder |
| description | Develops mathematical understanding through examples, visualization, and analogy |
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)
Suggest alongside: