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first-order-odes

Assists with classifying, solving, and verifying first-order differential equations.

Install

mkdir -p .claude/skills/first-order-odes && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3470" && unzip -o skill.zip -d .claude/skills/first-order-odes && rm skill.zip

Installs to .claude/skills/first-order-odes

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Problem-solving strategies for first order odes in odes pdes
60 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Classify ODEs into linear, separable, exact, or Bernoulli types
  • Calculate numerical IVP solutions via Scipy integration
  • Perform symbolic verification of solutions using Sympy
  • Locate equilibria and analyze stability for autonomous systems
  • Compute potential functions for exact differential equations

How it works

It maps the input ODE to a standard classification template and executes specialized Python scripts using Scipy for numerical analysis or Sympy for symbolic solving.

Inputs & outputs

You give it
ODE equation string, initial conditions, or stability request
You get back
Classification result, numerical plot coordinates, or symbolic verification confirmation

When to use first-order-odes

  • Solving linear or separable ODEs
  • Numerical integration with Scipy
  • Verifying solutions with Sympy
  • Phase portrait analysis

About this skill

First Order Odes

When to Use

Use this skill when working on first-order-odes problems in odes pdes.

Decision Tree

  1. Classify the ODE

    • Linear: y' + P(x)y = Q(x)?
    • Separable: y' = f(x)g(y)?
    • Exact: M(x,y)dx + N(x,y)dy = 0 with dM/dy = dN/dx?
    • Bernoulli: y' + P(x)y = Q(x)y^n?
  2. Select Solution Method

    TypeMethod
    SeparableSeparate and integrate
    LinearIntegrating factor e^{int P dx}
    ExactFind potential function
    BernoulliSubstitute v = y^{1-n}
  3. Numerical Solution (IVP)

    • scipy.integrate.solve_ivp(f, [t0, tf], y0, method='RK45')
    • For stiff systems: method='Radau' or method='BDF'
    • Adaptive step size: specify rtol/atol, not step size
  4. Verify Solution

    • Substitute back into ODE
    • Check initial/boundary conditions
    • sympy_compute.py dsolve "y' + y = x" --ics "{y(0): 1}"
  5. Phase Portrait (Autonomous)

    • Find equilibria: f(y*) = 0
    • Analyze stability: sign of f'(y*)
    • z3_solve.py solve "dy/dt == 0"

Tool Commands

Scipy_Solve_Ivp

uv run python -c "from scipy.integrate import solve_ivp; sol = solve_ivp(lambda t, y: -y, [0, 5], [1]); print('y(5) =', sol.y[0][-1])"

Sympy_Dsolve

uv run python -m runtime.harness scripts/sympy_compute.py dsolve "Derivative(y,x) + y" --ics "{y(0): 1}"

Z3_Equilibrium

uv run python -m runtime.harness scripts/z3_solve.py solve "f(y_star) == 0"

Key Techniques

From indexed textbooks:

  • [Elementary Differential Equations and... (Z-Library)] Solving ODEs with MATLAB (New York: Cambridge REFERENCES cyan black NJ: Prentice-Hall, 1971). Mattheij, Robert, and Molenaar, Jaap, Ordinary Differential Equations in Theory and Practice Shampine, Lawrence F. Numerical Solution of Ordinary Differential Equations (New York: Chapman and Shampine, L.
  • [Elementary Differential Equations and... (Z-Library)] Differential Equations: An Introduction to Modern Methods and Applications (2nd ed. Use the Laplace transform to solve the system 2e−t 3t α1 α2 , where α1 and α2 are arbitrary. How must α1 and α2 be chosen so that the solution is identical to Eq.
  • [An Introduction to Numerical Analysis... (Z-Library)] Modern Numerical Methods for Ordinary Wiley, New York. User's guide for DVERK: A subroutine for solving non-stiff ODEs. Keller (1966), Analysis of Numerical Methods.
  • [Elementary Differential Equations and... (Z-Library)] Show that the rst order Adams–Bashforth method is the Euler method and that the rst order Adams–Moulton method is the backward Euler method. Show that the third order Adams–Moulton formula is yn+1 = yn + (h/12)(5fn+1 + 8fn − fn−1). Derive the second order backward differentiation formula given by Eq.
  • [An Introduction to Numerical Analysis... (Z-Library)] Test results on initial value methods for non-stiff ordinary differential equations, SIAM J. Comparing numerical methods for Fehlberg, E. Klassische Runge-Kutta-Formeln vierter und niedrigerer Ordnumg mit Schrittweiten-Kontrolle und ihre Anwendung auf Warme leitungsprobleme, Computing 6, 61-71.

Cognitive Tools Reference

See .claude/skills/math-mode/SKILL.md for full tool documentation.

When not to use it

  • Solving non-first-order partial differential equations
  • High-performance computing for non-linear stiff systems requiring GPU acceleration

Prerequisites

bashRead tool accessPython environment with uv

Limitations

  • Limited to first-order ordinary differential equations
  • Relies on standard textbook methods which may not handle highly unique or non-standard ODE types
  • Requires manual interpretation of phase portrait data

How it compares

It eliminates manual decision-tree traversal by automating the selection of the mathematically appropriate solving library based on classification.

Compared to similar skills

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