SharpInference

Drive the architect→workhorse flow — turn an approved plan into delegated Sonnet implementation with test and review gates, keeping the main (Opus/Fable) thread as coordinator.

Work a GitHub issue end-to-end — fetch it, verify the claim, turn it into an architect spec, and drive the delegated implementation flow with correct issue references in branches and commits.

Checklist for adding support for a new GGUF model architecture (or a variant of an existing one) to SharpInference.

Cross-check SharpInference numerics against llama.cpp and run accuracy gates. Use when output quality or numerics are in question — new architecture bring-up, kernel or quantization changes, KV-compression work, "model produces garbage" reports.

CLI run recipes for specific models and modes — VibeThinker, Ornith-1.0, Gemma 4 vision, image generation + upscaling, perplexity gating, whole-turn JSON-schema structured output, DSpark speculative decoding, download-model presets. Use when actually running inference for one of these.

Required workflow when adding, editing, or removing Vulkan GLSL shader consts — the committed precompiled SPIR-V table must be regenerated or tests fail on drift.