Write correct AND fast Unity Sentis (com.unity.ai.inference) 2.6 inference code in C#. Use whenever generating or reviewing Sentis / Inference Engine code: loading models, running Workers, tensor I/O, TextureConverter, quantization, NMS, or LLM/Whisper inference — and whenever the task is making a Sentis model faster: warming up, quantizing (Float16/Uint8), async readback, ScheduleIterable frame-splitting, KV cache decisions, smoothing per-frame inference cost, backend choice, or cold-vs-warm latency. Encodes the EXACT 2.6 API so the model does not mix in the old Barracuda/Sentis-1.x API (IWorker/Execute) or invent Unity.Sentis, plus optimization decision rules verified by measurement in this project (details: reference/optimization-rules.md). ALSO owns the demo/test SCENE around the model: creating or porting a demo scene, runtime command UI (uGUI InputField + button), NavMeshAgent characters driven by model output, Korean/CJK text in uGUI, and the Unity-6 / New-Input-System gotchas that silently break these
Convert ANY model (PyTorch / HF transformers / prebuilt ONNX) into ONNX that Unity Sentis 2.6 actually imports AND runs correctly. Owns the whole conversion layer: choosing the right tool (torch.onnx.export vs optimum-cli vs Olive vs onnx-graphsurgeon), structural rewrites (KV-cache as plain I/O, unmerged seq2seq decoders, hoisting RNN state to tensors, splitting pre/post-processing into their own graphs), graph surgery on unsupported or attribute-dropping ops, weight inlining, f16 downcast for the 2 GB cap, and the ORT-parity verification ladder. Use whenever the task mentions: exporting or converting a model for Sentis/Unity, ONNX export flags, KV-cache export, onnx-graphsurgeon / graph surgery / editing an ONNX graph, optimum-cli or Olive, a model that imports into Unity but outputs garbage, or unsupported-operator import errors — even if the user doesn't say "convert". (Model *selection* = sentis-model-scout; fine-tuning = sentis-training-pipeline; C# runtime = sentis-inference.)
Find and judge Hugging Face models for Unity Sentis (com.unity.ai.inference) 2.6 on-device use, BEFORE downloading weights. Use whenever the task is: searching HF for a model ("find a model for X that runs in Unity/Sentis"), deciding whether a candidate model will import into Sentis, comparing model candidates, checking a model's size/license/language fit for on-device, or scanning an ONNX graph for unsupported operators, or judging whether a FAILED export is repairable (torch eager re-export / optimum-cli / Olive) or a structural dead end. Runs a bundled scan script instead of guessing. Hands off to sentis-model-converter (conversion/fixes) once a candidate is chosen.
Measure and diagnose Unity Sentis 2.6 (com.unity.ai.inference) inference performance. PRIMARY path = an agent-drivable code warm-bench (editor Unity_RunCommand) plus the Unity_Profiler_* MCP tools — these yield NUMBERS; the AI Assistant integration (6.4+) is a secondary, human-driven qualitative aid. Use whenever the task is: getting warm/cold latency numbers for a Sentis model, A/B-ing quantization or backends, finding the real bottleneck (compute vs readback vs KV round-trip), explaining a frame spike / stall / GC, detecting CPU-fallback layers, checking FPS budget, or deciding whether async readback / ScheduleIterable is defending FPS. Trigger even for vague asks like "why is my game slow when the model runs", or measuring/validating on a REAL build target (Android via adb, macOS standalone) — the editor only proxies a desktop ship, not a phone. Owns this project's measurement log (reference/measurements.md). Pairs with sentis-inference (the optimization levers the diagnosis points to).
Training pipeline for Unity Sentis 2.6 on-device inference: find the training recipe in the model's HuggingFace repo, reshape the user's data to that recipe's schema, fine-tune, and choose the quantization method and ORDER. Use whenever the task is: fine-tuning any vision / audio / SLM model on a dataset, preparing a dataset for a specific model, or evaluating a fine-tune without fooling yourself (held-out split, separated capability axes). Encodes the recipe-from-repo rule, template byte-identity, the quantization ORDER, and Apple-MPS training economics that a general model gets wrong. ONNX conversion / graph surgery / import debugging = sentis-model-converter; C# runtime = sentis-inference.