software-ios-ai-engine
Local AI Engine on iOS
Use this skill when an iOS app should run useful AI behavior locally before spending cloud quota: Apple Foundation Models, deterministic local NLG, local retrieval stitching, local classifiers, extraction, summarization, tagging, rewrite helpers, and tool calls into app state. The common constraint is not "make chat smarter"; it is pick the right local engine, shape the data contract, gate capability correctly, and keep cloud as an explicit upgrade or fallback.
A major scenario is a rich, per-user structured context bundle (chart, Human Design, planning cache, knowledge chunks, activity ratings, dream themes, mood/energy, etc.) that needs to produce a real answer without cloud quota. In that case, the fix is never "make the reject card nicer." The fix is adding a local Composer tier between intent routing and cloud fallback.
This skill is for iOS product surfaces and local app-engine design. For pure retrieval/chunking/grounding strategy upstream of the local engine, route to ai-rag. For model serving/quantization tradeoffs beyond Apple platform APIs, route to ai-llm-inference. For evaluation of generated or extracted output, route to ai-evals-observer.