What problem does it solve? Codebases accumulate over-engineering: unnecessary abstractions, premature dependencies, boilerplate scaffolding, and bloated diffs. This Skill forces the simplest solution that actually works, cutting speculative features and custom code in favor of standard library and native platform capabilities. ## Core Features & Use Cases - The Ladder: A decision hierarchy that stops at the first sufficient rung — question whether the task needs to exist (YAGNI), reuse existing code, prefer stdlib, then native platform features, then existing dependencies, and only then write minimal new code. - Intensity Levels: Three modes (lite, full, ultra) control how aggressively simplification is enforced, from suggesting lazier alternatives to challenging the requirement itself. - Root-Cause Bug Fixes: Directs fixes to the shared function all callers route through rather than patching individual symptoms. - Use Case: When asked to add a response cache, instead of building a custom cache class, the Skill applies @lru_cache(maxsize=1000) and notes when a custom solution would become necessary. ## Quick Start Ask the AI to implement or refactor any coding task using ponytail mode, for example: "Use ponytail to add caching to this API fetch function."