Agent skill

sigma-algebras

Problem-solving strategies for sigma algebras in measure theory

parcadei3,879★ · 1 repos on radarProfile →
claude-codecan modify filesMIT
Install
npx skills add parcadei/Continuous-Claude-v3 --skill sigma-algebras --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 3 KB
Bundled scripts: none
Allowed tools: BashRead
Path: .claude/skills/math/measure-theory/sigma-algebras/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,885
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 ```bash 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 ```bash uv run python -m runtime.harness scripts/z3_solve.py prove "pi_system_subset_lambda implies sigma_equal" ``` ### Sympy_Preimage ```bash uv run python -m runtime.harness scrip

What's inside
Steps it walks through
  1. When to Use
  2. Decision Tree
  3. Tool Commands
  4. Z3SigmaAxioms
  5. Z3DynkinPiLambda
  6. SympyPreimage
  7. Key Techniques
  8. Cognitive Tools Reference
Commands it runs
uv run python -m runtime.harness scripts/z3_solve.py prove "X_in_F and closed_under_complement and closed_under_countable_union"
uv run python -m runtime.harness scripts/z3_solve.py prove "pi_system_subset_lambda implies sigma_equal"
uv run python -m runtime.harness scripts/sympy_compute.py simplify "f_inv(A_union_B) == f_inv(A) | f_inv(B)"
More from Continuous-Claude-v3
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About this skill
What does the sigma-algebras skill do?

Problem-solving strategies for sigma algebras in measure theory

How do I install it?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill sigma-algebras --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From parcadei/Continuous-Claude-v3, a repository with 3,885 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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