What problem does it solve? When running video generation across multiple GPUs/NPUs, every card often redundantly decodes the same VAE/TAE output, and naive attempts to shard the decoder silently corrupt frames or deadlock. This Skill determines whether a decoder can be sharded along a given axis, how to shard it bit-exactly, and whether the sharding is worth the communication cost. ## Core Features & Use Cases - Independence Auditing: Audits every operator for cross-axis coupling (e.g., recursive MemBlock prefix dependencies) to decide between frame-axis sharding, spatial tiling with halo, or state-carrying sliced decoding. - Four-Gate Verification: Enforces CPU bitwise equivalence (max|d| = 0), end-to-end md5 comparison, frame-level health metrics (high-frequency energy, frame diffs, frozen segments), and exchange-budget accounting before claiming losslessness. - Silent-Failure Traps: Documents pitfalls like all_gather_into_tensor writing to stack copies, repeat_interleave over-allocating memory, rank-dependent gating deadlocks, and CANN nearest-neighbor upsample defects on large batches. - Use Case: You run 8-card video generation where each card decodes the full video. Use this Skill to prove the decoder is frame-independent, shard it across cards, verify 12/12 md5 matches, and cut decode time to roughly a quarter. ## Quick Start Ask the AI to check whether your VAE decoder can be sharded across cards without changing the output, and to run the CPU bitwise equivalence check before deploying.