continuous-learning-v2

Pattern extraction identifies repeated design and usage patterns across your mobile codebase using instinct-based learning. This skill automatically detects recurring patterns in Android, iOS, and Kotlin Multiplatform projects, then clusters and scores them by confidence to guide adoption and reusability.

Yes, this skill applies across Android, iOS, and Kotlin Multiplatform projects. It extracts and evolves patterns specific to each platform while enabling cross-platform pattern sharing and reuse across your multiplatform codebase.

Confidence scoring ranks patterns by frequency and relevance within your codebase. Higher-confidence patterns represent proven, widely-used approaches; lower-confidence patterns flag emerging or experimental designs, letting you focus refinement and adoption on the most impactful patterns first.

Export instincts and analytics to shareable formats using the export feature. This enables incremental learning where teams import, refine, and collectively build a library of proven mobile patterns without duplicating discovery work.

The skill identifies frequent state-hoisting patterns by analyzing your Compose codebase, then proposes centralized, reusable implementations. Confidence scores guide which patterns to standardize across your Compose layers.

The skill requires sufficient codebase volume to identify statistically meaningful patterns. Smaller projects or those with minimal pattern repetition may show low-confidence results; confidence scores reflect this limitation, helping you decide when pattern extraction is actionable.