Formalize real analysis and topology in Lean 4 using Mathlib filters.
npx skills add https://github.com/r-irbe/proof-skills --skill lean-math-analysis
Please help me install this Agent Skill. Skill: lean-math-analysis Source: https://github.com/r-irbe/proof-skills/tree/main/skills/lean-math-analysis Command: npx skills add https://github.com/r-irbe/proof-skills --skill lean-math-analysis
Formalizes real analysis, topology, and measure theory in Lean 4 using Mathlib's filter-based approach to provide a scalable workflow for rigorous proofs.
Describe your analysis goal and apply Mathlib's filter-based architecture to structure a proof, starting with the relevant Part (continuity, derivatives, measure theory) and handing off to the appropriate specialist when needed.
High-intent search queries and answers about installing and using this skill.
Formalizing real analysis in Lean 4 involves mapping proofs to Mathlib's filter-based architecture, starting with a relevant Part like continuity or derivatives, and applying Tendsto or ContinuousAt patterns to structure the verification.
The filter-based approach in Mathlib uses filters to generalize concepts like limits and continuity, allowing you to uniformly handle convergence and topological properties using Tendsto and ContinuousAt patterns in Lean 4.
Yes, you can formalize measure theory and differentiation together in Mathlib4 by following the Part-based workflow, which coordinates continuity, derivatives, normed spaces, and measure theory under a unified filter-based analysis architecture.
Yes, the workflow explicitly coordinates handoffs to proof engineering and foundations skills when a gap is found, streamlining complex verification tasks across real analysis and topology in Mathlib4.
The best way to structure a measure theory proof in Lean 4 is to apply Mathlib's filter-based architecture, select the appropriate Part-based workflow section, and consult the structured reference backbone when encountering formalization gaps.
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