SI

sigma-algebras

Assists with verifying measure theory axioms and sigma-algebra properties.

Install

mkdir -p .claude/skills/sigma-algebras && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2675" && unzip -o skill.zip -d .claude/skills/sigma-algebras && rm skill.zip

Installs to .claude/skills/sigma-algebras

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 sigma algebras in measure theory
63 charsno explicit “when” trigger
Advanced

Key capabilities

  • Verify sigma-algebra axioms
  • Generate sigma-algebras from collections
  • Test function measurability
  • Compute preimages for Borel sets

How it works

It uses symbolic computation and Z3 solvers to prove properties of sigma-algebras and verify measurability.

Inputs & outputs

You give it
Mathematical set or function definition
You get back
Proof or verification result

When to use sigma-algebras

  • Verify sigma-algebra axioms
  • Test measurability of functions
  • Apply Dynkin's pi-lambda theorem
  • Compute preimages for Borel sets

About this skill

Sigma Algebras

When to Use

Use this skill when working on sigma-algebras problems in measure theory.

Decision Tree

  1. Verify sigma-algebra axioms

    • X in F (whole space is measurable)
    • A in F implies A^c in F (closed under complements)
    • A_n in F implies union(A_n) in F (closed under countable unions)
    • z3_solve.py prove "sigma_algebra_axioms"
  2. sigma-algebra generation

    • Start with generating collection C
    • sigma(C) = smallest sigma-algebra containing C
    • Use Dynkin's pi-lambda theorem for uniqueness
  3. Measurability verification

    • f is measurable if f^{-1}(B) in F for all Borel B
    • Sufficient: check for open sets or intervals
    • sympy_compute.py simplify "preimage(f, interval)"
  4. Product sigma-algebras

    • F1 x F2 = sigma{A x B : A in F1, B in F2}
    • Projections are measurable

Tool Commands

Z3_Sigma_Axioms

uv run python -m runtime.harness scripts/z3_solve.py prove "X_in_F and closed_under_complement and closed_under_countable_union"

Z3_Dynkin_Pi_Lambda

uv run python -m runtime.harness scripts/z3_solve.py prove "pi_system_subset_lambda implies sigma_equal"

Sympy_Preimage

uv run python -m runtime.harness scripts/sympy_compute.py simplify "f_inv(A_union_B) == f_inv(A) | f_inv(B)"

Key Techniques

From indexed textbooks:

  • [Statistical Inference (George Casella... (Z-Library)] PROBABILITY THEORY Definition 1. A collection of subsets of S is called a sigma algebra (or Borel field), denoted by B, if it satisfies the following three properties: a. B (the empty set is an element of B).
  • [Measure, Integration Real Analysis (... (Z-Library)] S T is the smallest s-algebra containing the measurable rectangles). S T The technique outlined above should be used when possible. However, in some situations there seems to be no reasonable way to verify that the collection of sets with the desired property is a s-algebra.
  • [Statistical Inference (George Casella... (Z-Library)] Thus, again by property (b), N2, A; € B. Associated with sample space § we can have many different sigma algebras. For example, the collection of the two sets {#, S} is a sigma algebra, usually called the trivial sigma algebra.
  • [Real Analysis (Halsey L. Royden, Patr... (Z-Library)] Proposition 13 Let F be a collection of subsets of a set X. Then the intersection A of all σ-algebras of subsets of X that contain F is a σ-algebra that contains F. Moreover, it is the smallest σ-algebra of subsets of X that contains F, in the sense that any σ-algebra that contains F also contains A.
  • [Real Analysis (Halsey L. Royden, Patr... (Z-Library)] Let M be the collection of subsets of X that are either countable or have a countable complement in X. For E ∈ M, dene µ(E) = 0 if E is countable and µ(E) = 1, if E has a countable complement. Is this measure space complete?

Cognitive Tools Reference

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

When not to use it

  • When the problem does not involve measure theory

Limitations

  • Limited to problems within measure theory scope
  • Requires clear set definitions

How it compares

It uses automated symbolic verification instead of manual proof construction.

Compared to similar skills

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