tune-model

tune-model is an MLOps & model serving skill for AI agents, published by cloudrift-ai in the emmy repository on GitHub. Skillspace rates tune-model D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Use this skill when the user asks to "tune model X", "analyze tune findings for <model>", "tune and analyze model X", "why is emmy slower than eager / torch.compile on X", "do a per-kernel performance analysis", "drill into kernel performance", "profile the compiled kernels with NCU", or otherwise wants a clean autotune of a model (or one layer), an end-to-end + per-kernel bench against PyTorch eager and torch.compile, a root-cause analysis of every underperforming kernel (search shortfall, tier/optimization lockout, codegen quality, bench failures), and a findings report saved to plans/. For a whole-model tune it also validates the full model end-to-end (eager / torch.compile / emmy) and, when the model is servable via `emmy serve` (an embedding model), benches the served model via `vllm bench serve` against vanilla vLLM. Modeled on plans/qwen3-embedding-layer0-tune-findings.md. For the golden matmul shapes use the tune-golden skill instead — there the target config is known, so the…

Grade

D

Quality

66 / 100

Safety

Passed

GitHub stars

66

Unproven: rated by static analysis only, not yet compared head-to-head.

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