Math Cognitive Stack Guide

Cognitive prosthetics for exact mathematical computation. This guide helps you choose the right tool for your math task.

Quick Reference I want to... Use this Example Solve equations sympy_compute.py solve solve "x2 - 4 = 0" --var x Integrate/differentiate sympy_compute.py integrate "sin(x)" --var x Compute limits sympy_compute.py limit limit "sin(x)/x" --var x --to 0 Matrix operations sympy_compute.py / numpy_compute.py det "[[1,2],[3,4]]" Verify a reasoning step math_scratchpad.py verify verify "x = 2 implies x^2 = 4" Check a proof chain math_scratchpad.py chain chain --steps '[...]' Get progressive hints math_tutor.py hint hint "Solve x^2 - 4 = 0" --level 2 Generate practice problems math_tutor.py generate generate --topic algebra --difficulty 2 Prove a theorem (constraints) z3_solve.py prove prove "x + y == y + x" --vars x y Check satisfiability z3_solve.py sat sat "x > 0, x < 10, x*x == 49" Optimize with constraints z3_solve.py optimize optimize "x + y" --constraints "..." Plot 2D/3D functions math_plot.py plot2d "sin(x)" --range -10 10 Arbitrary precision mpmath_compute.py pi --dps 100 Numerical optimization scipy_compute.py minimize "x2 + 2*x" "5" Formal machine proof Lean 4 (lean4 skill) /lean4 The Five Layers Layer 1: SymPy (Symbolic Algebra)

When: Exact algebraic computation - solving, calculus, simplification, matrix algebra.

Key Commands:

Solve equation

uv run python -m runtime.harness scripts/sympy_compute.py \ solve "x*2 - 5x + 6 = 0" --var x --domain real

Integrate

uv run python -m runtime.harness scripts/sympy_compute.py \ integrate "sin(x)" --var x

Definite integral

uv run python -m runtime.harness scripts/sympy_compute.py \ integrate "x**2" --var x --bounds 0 1

Differentiate (2nd order)

uv run python -m runtime.harness scripts/sympy_compute.py \ diff "x**3" --var x --order 2

Simplify (trig strategy)

uv run python -m runtime.harness scripts/sympy_compute.py \ simplify "sin(x)2 + cos(x)2" --strategy trig

Limit

uv run python -m runtime.harness scripts/sympy_compute.py \ limit "sin(x)/x" --var x --to 0

Matrix eigenvalues

uv run python -m runtime.harness scripts/sympy_compute.py \ eigenvalues "[[1,2],[3,4]]"

Best For: Closed-form solutions, calculus, exact algebra.

Layer 2: Z3 (Constraint Solving & Theorem Proving)

When: Proving theorems, checking satisfiability, constraint optimization.

Key Commands:

Prove commutativity

uv run python -m runtime.harness scripts/cc_math/z3_solve.py \ prove "x + y == y + x" --vars x y --type int

Check satisfiability

uv run python -m runtime.harness scripts/cc_math/z3_solve.py \ sat "x > 0, x < 10, x*x == 49" --type int

Optimize

uv run python -m runtime.harness scripts/cc_math/z3_solve.py \ optimize "x + y" --constraints "x >= 0, y >= 0, x + y <= 100" \ --direction maximize --type real

Best For: Logical proofs, constraint satisfaction, optimization with constraints.

Layer 3: Math Scratchpad (Reasoning Verification)

When: Verifying step-by-step reasoning, checking derivation chains.

Key Commands:

Verify single step

uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \ verify "x = 2 implies x^2 = 4"

Verify with context

uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \ verify "x^2 = 4" --context '{"x": 2}'

Verify chain of reasoning

uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \ chain --steps '["x^2 - 4 = 0", "(x-2)(x+2) = 0", "x = 2 or x = -2"]'

Explain a step

uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \ explain "d/dx(x^3) = 3*x^2"

Best For: Checking your work, validating derivations, step-by-step verification.

Layer 4: Math Tutor (Educational)

When: Learning, getting hints, generating practice problems.

Key Commands:

Step-by-step solution

uv run python scripts/cc_math/math_tutor.py steps "x*2 - 5x + 6 = 0" --operation solve

Progressive hint (level 1-5)

uv run python scripts/cc_math/math_tutor.py hint "Solve x**2 - 4 = 0" --level 2

Generate practice problem

uv run python scripts/cc_math/math_tutor.py generate --topic algebra --difficulty 2

Best For: Learning, tutoring, practice.

Layer 5: Lean 4 (Formal Proofs)

When: Rigorous machine-verified mathematical proofs, category theory, type theory.

Access: Use /lean4 skill for full documentation.

Best For: Publication-grade proofs, dependent types, category theory.

Numerical Tools

For numerical (not symbolic) computation:

NumPy (160 functions)

Matrix operations

uv run python scripts/cc_math/numpy_compute.py det "[[1,2],[3,4]]" uv run python scripts/cc_math/numpy_compute.py inv "[[1,2],[3,4]]" uv run python scripts/cc_math/numpy_compute.py eig "[[1,2],[3,4]]" uv run python scripts/cc_math/numpy_compute.py svd "[[1,2,3],[4,5,6]]"

Solve linear system

uv run python scripts/cc_math/numpy_compute.py solve "[[3,1],[1,2]]" "[9,8]"

SciPy (289 functions)

Minimize function

uv run python scripts/cc_math/scipy_compute.py minimize "x*2 + 2x" "5"

Find root

uv run python scripts/cc_math/scipy_compute.py root "x**3 - x - 2" "1.5"

Curve fitting

uv run python scripts/cc_math/scipy_compute.py curve_fit "aexp(-bx)" "0,1,2,3" "1,0.6,0.4,0.2" "1,0.5"

mpmath (153 functions, arbitrary precision)

Pi to 100 decimal places

uv run python scripts/cc_math/mpmath_compute.py pi --dps 100

Arbitrary precision sqrt

uv run python -m scripts.mpmath_compute mp_sqrt "2" --dps 100

Visualization math_plot.py

2D plot

uv run python scripts/cc_math/math_plot.py plot2d "sin(x)" \ --var x --range -10 10 --output plot.png

3D surface

uv run python scripts/cc_math/math_plot.py plot3d "x2 + y2" \ --xvar x --yvar y --range 5 --output surface.html

Multiple functions

uv run python scripts/cc_math/math_plot.py plot2d-multi "sin(x),cos(x)" \ --var x --range -6.28 6.28 --output multi.png

LaTeX rendering

uv run python scripts/cc_math/math_plot.py latex "\int e^{-x^2} dx" --output equation.png

Educational Features 5-Level Hint System Level Category What You Get 1 Conceptual General direction, topic identification 2 Strategic Approach to use, technique selection 3 Tactical Specific steps, intermediate goals 4 Computational Intermediate results, partial solutions 5 Answer Full solution with explanation

Usage:

Start with conceptual hint

uv run python scripts/cc_math/math_tutor.py hint "integrate x*sin(x)" --level 1

Get more specific guidance

uv run python scripts/cc_math/math_tutor.py hint "integrate x*sin(x)" --level 3

Step-by-Step Solutions uv run python scripts/cc_math/math_tutor.py steps "x*2 - 5x + 6 = 0" --operation solve

Returns structured steps with:

Step number and type From/to expressions Rule applied Justification Common Workflows Workflow 1: Solve and Verify Solve with sympy_compute.py Verify solution with math_scratchpad.py Plot to visualize (optional)

Solve

uv run python -m runtime.harness scripts/sympy_compute.py \ solve "x**2 - 4 = 0" --var x

Verify the solutions work

uv run python -m runtime.harness scripts/cc_math/math_scratchpad.py \ verify "x = 2 implies x^2 - 4 = 0"

Workflow 2: Learn a Concept Generate practice problem with math_tutor.py Use progressive hints (level 1, then 2, etc.) Get full solution if stuck

Generate problem

uv run python scripts/cc_math/math_tutor.py generate --topic calculus --difficulty 2

Get hints progressively

uv run python scripts/cc_math/math_tutor.py hint "..." --level 1 uv run python scripts/cc_math/math_tutor.py hint "..." --level 2

Full solution

uv run python scripts/cc_math/math_tutor.py steps "..." --operation integrate

Workflow 3: Prove and Formalize Check theorem with z3_solve.py (constraint-level proof) If rigorous proof needed, use Lean 4

Quick check with Z3

uv run python -m runtime.harness scripts/cc_math/z3_solve.py \ prove "xy == yx" --vars x y --type int

For formal proof, use /lean4 skill

Choosing the Right Tool Is it SYMBOLIC (exact answers)? └─ Yes → Use SymPy ├─ Equations → sympy_compute.py solve ├─ Calculus → sympy_compute.py integrate/diff/limit └─ Simplify → sympy_compute.py simplify

Is it a PROOF or CONSTRAINT problem? └─ Yes → Use Z3 ├─ True/False theorem → z3_solve.py prove ├─ Find values → z3_solve.py sat └─ Optimize → z3_solve.py optimize

Is it NUMERICAL (approximate answers)? └─ Yes → Use NumPy/SciPy ├─ Linear algebra → numpy_compute.py ├─ Optimization → scipy_compute.py minimize └─ High precision → mpmath_compute.py

Need to VERIFY reasoning? └─ Yes → Use Math Scratchpad ├─ Single step → math_scratchpad.py verify └─ Chain → math_scratchpad.py chain

Want to LEARN/PRACTICE? └─ Yes → Use Math Tutor ├─ Hints → math_tutor.py hint └─ Practice → math_tutor.py generate

Need MACHINE-VERIFIED formal proof? └─ Yes → Use Lean 4 (see /lean4 skill)

Related Skills /math or /math-mode - Quick access to the orchestration skill /lean4 - Formal theorem proving with Lean 4 /lean4-functors - Category theory functors /lean4-nat-trans - Natural transformations /lean4-limits - Limits and colimits Requirements

All math scripts are installed via:

uv sync

Dependencies: sympy, z3-solver, numpy, scipy, mpmath, matplotlib, plotly