integration-theory
Provides problem-solving strategies and script-based verification for measure theory integration problems.
Install
mkdir -p .claude/skills/integration-theory && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4595" && unzip -o skill.zip -d .claude/skills/integration-theory && rm skill.zipInstalls to .claude/skills/integration-theory
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 integration theory in measure theoryKey capabilities
- →Apply integration theorems (MCT, DCT, Fatou, Fubini)
- →Compute integrals of simple functions
- →Verify convergence proofs
- →Calculate lower bounds
How it works
The skill uses a decision tree to identify the applicable integration theorem and provides automated scripts to compute or verify the result.
Inputs & outputs
When to use integration-theory
- →Compute integrals of simple functions
- →Verify convergence using Monotone Convergence Theorem
- →Prove limit exchange using Dominated Convergence Theorem
- →Calculate lower bounds with Fatou's Lemma
About this skill
Integration Theory
When to Use
Use this skill when working on integration-theory problems in measure theory.
Decision Tree
-
Simple function integration
- For s = sum(a_i * chi_{E_i}): integral s dmu = sum(a_i * mu(E_i))
sympy_compute.py simplify "simple_integral"
-
Monotone Convergence Theorem (MCT)
- If 0 <= f_n <= f_{n+1} and f_n -> f:
- lim integral(f_n) = integral(lim f_n)
- Use for increasing sequences
-
Dominated Convergence Theorem (DCT)
- If |f_n| <= g (integrable) and f_n -> f pointwise:
- lim integral(f_n) = integral(f)
z3_solve.py prove "dominated_convergence"
-
Fatou's Lemma
- integral(liminf f_n) <= liminf(integral f_n)
- Use as lower bound when MCT/DCT don't apply
-
Fubini-Tonelli
- For product measures: switch order of integration
- Tonelli: non-negative functions (always valid)
- Fubini: integrable functions
Tool Commands
Sympy_Simple_Integral
uv run python -m runtime.harness scripts/sympy_compute.py integrate "sum(a_i * chi_E_i)" --var mu
Z3_Mct
uv run python -m runtime.harness scripts/z3_solve.py prove "f_n_increasing implies lim_integral_equals_integral_lim"
Z3_Dct
uv run python -m runtime.harness scripts/z3_solve.py prove "abs(f_n) <= g and g_integrable implies limit_exchange"
Sympy_Fatou
uv run python -m runtime.harness scripts/sympy_compute.py limit "liminf(integral_f_n)" --comparison "integral_liminf_f_n"
Key Techniques
From indexed textbooks:
- [Measure, Integration Real Analysis (... (Z-Library)] If you go at a leisurely pace, then covering Chapters 1–5 in the rst semester may be a good goal. If you go a bit faster, then covering Chapters 1–6 in the rst semester may be more appropriate. For a second-semester course, covering some subset of Chapters 6 through 12 should produce a good course.
- [Measure, Integration Real Analysis (... (Z-Library)] Suppose B is a Borel set and f : B R is a Lebesgue measurable function. B : g(x) = f (x) gj Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4. International License (http://creativecommons.
- [Measure, Integration Real Analysis (... (Z-Library)] Statue in Milan of Maria Gaetana Agnesi, who in 1748 published one of the rst calculus textbooks. A translation of her book into English was published in 1801. In this chapter, we develop a method of integration more powerful than methods contemplated by the pioneers of calculus.
- [Measure, Integration Real Analysis (... (Z-Library)] Preface for Instructors Chapter 3: Integration with respect to a measure is dened in this chapter in a natural fashion rst for nonnegative measurable functions, and then for real-valued measurable functions. The Monotone Convergence Theorem and the Dominated Convergence Theorem are the big results in this chapter that allow us to interchange integrals and limits under appropriate conditions. Preface for Instructors Chapter 8: This chapter focuses on Hilbert spaces, which play a central role in modern mathematics.
- [Measure, Integration Real Analysis (... (Z-Library)] Chapter 6: After a quick review of metric spaces and vector spaces, this chapter denes normed vector spaces. The big result here is the Hahn–Banach Theorem about extending bounded linear functionals from a subspace to the whole space. Then this chapter introduces Banach spaces.
Cognitive Tools Reference
See .claude/skills/math-mode/SKILL.md for full tool documentation.
When not to use it
- →When the problem is outside measure theory
- →When the integration theorem conditions are not met
Prerequisites
Limitations
- →Requires understanding of measure theory
- →Dependent on script availability
How it compares
It automates the selection and verification of integration theorems, which is more reliable than manual proof checking.
Compared to similar skills
integration-theory side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| integration-theory (this skill) | 1 | 7mo | Review | Advanced |
| literature-review | 559 | 2mo | Review | Advanced |
| openalex-database | 48 | 7mo | Review | Intermediate |
| scientific-critical-thinking | 18 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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