Refactor MbTorch source code with test-first atomic steps and layering rules.
npx skills add https://github.com/c-tomioka/mbtorch --skill mbtorch-refactor
Please help me install this Agent Skill. Skill: mbtorch-refactor Source: https://github.com/c-tomioka/mbtorch/tree/main/.claude/skills/mbtorch-refactor Command: npx skills add https://github.com/c-tomioka/mbtorch --skill mbtorch-refactor
Provides a disciplined, test-first approach to refactoring the MbTorch codebase so that internal structure, readability, and layering are improved without changing external behavior or breaking tests. It turns vague impulses to "clean up code" into concrete, auditable steps that respect project layering and TDD requirements.
Ask the assistant to analyze the target files, list concrete problems in the prescribed "問題/影響/対象/提案" format, and propose the first atomic refactoring step with required tests.
High-intent search queries and answers about installing and using this skill.
Safely refactoring MbTorch source code requires a test-first workflow that adds or strengthens tests before structural changes, using atomic steps to keep tests green at every checkpoint.
Preventing layer violations when moving numeric ops requires enforcing layering dependency rules across core, nn, optim, and io modules, pausing to present dependency-violation proposals until user confirmation.
You need to split files in a MoonBit ML framework when source files grow large, dependencies risk layer violations, or duplicated logic appears across modules like tensor and io.
Yes, you can consolidate scattered I/O operations into the io module using TDD by prescribing atomic steps that keep tests green and verifying external behavior remains unchanged.
Yes, this refactoring approach works with existing MoonBit test suites by requiring tests to pass at every checkpoint and integrating TDD workflows before any structural changes are applied.
The refactoring process pauses before applying dependency changes to present dependency-violation proposals explicitly, ensuring the user confirms layering adjustments before any code modifications proceed.
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