Menu
当需要为 DAV_3510 RegBase 算子确认 API 约束、实现结构、排查常见陷阱或选择真实参考算子时使用。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
评估 CANN 算子仓的「进阶教程 / 开发指南」类文档质量——通读文档 + 对照算子代码**静态**查证(默认不跑),按五轴(找得到/信得过/学得会/可操作/读得懂)找出 漏讲/讲不清/过时/对不上代码/概念讲错,产出带证据与改进建议的体检报告(MD + HTML)。涉及「评进阶教程 / 开发指南文档质量 / 文档对不对得上代码 / 教程审稿 / tutorial 体检 / 文档信不信得过」等意图时使用。只评不改不跑,只对着文档与代码出诊断。
Run NPU inference/training tests on a remote SSH server with vllm-ascend Docker container. Use when the user asks to test models on NPU, run inference on Ascend devices, or deploy models to an SSH server.
Track daily PRs and Issues from vllm-project/vllm and vllm-project/vllm-ascend, filter by model (DeepSeek/Qwen/GLM/MiniMax/Kimi) and tech topics (PD disaggregation, MTP, quantization, graph mode, performance), analyze with LLM, and generate a Markdown report. Use when user wants vllm daily tracker, PR/Issue digest, or Ascend inference ecosystem monitoring.
Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware (NVIDIA, AMD, etc.). Supports email delivery, data change detection, and 8k1k sequence length performance analysis. Use when needing to track LLM inference performance trends, compare hardware configurations, or monitor benchmark updates.
通用 PyTorch 项目 Ascend NPU 迁移可行性分析。系统化扫描代码库中的 CUDA/GPU 依赖，按 7 大域分类评估（设备层、注意力机制、自定义算子、分布式通信、精度策略、第三方依赖、编译加速），并基于 Wan2.2 实际迁移经验提供逐项替代方案。适用于评估任何 DiT/Transformer 类模型（视频生成、LLM、多模态等）在昇腾 NPU 上的运行可行性与迁移工作量估算。
End-to-end AscendC custom operator development for Ascend NPU in an ascend-kernel (csrc/ops + build.sh + torch_npu PyTorch custom op) project. Use to design, generate, build, test, document, and tune a new AscendC operator from a name and a math/functional spec. Covers project init, two-level tiling design, op_host/op_kernel code generation, framework registration, compile/install/debug, PyTorch-style API docs, precision evaluation and root-cause debugging, torch_npu.profiler performance benchmarking, performance optimization, and security code review.
| name | external-cannbot-ops-ascendc-regbase-best-practice |
| description | 当需要为 DAV_3510 RegBase 算子确认 API 约束、实现结构、排查常见陷阱或选择真实参考算子时使用。 |
| original-name | ascendc-regbase-best-practice |
| synced-from | https://gitcode.com/cann/cannbot-skills |
| synced-date | 2026-05-26 |
| synced-commit | ac5bbd2b4cf427d011874e11f8d1e8b1bef66eda |
| license | UNKNOWN |
该能力解决 RegBase 路线下的四类问题：API 约束确认、实现结构参考、常见陷阱排查、真实参考算子选择。
reference-ops 子树是轻量参考资产。算子名称或基础类型匹配只表示“值得查看的候选参考”，不能证明目标任务可以直接复用、融合或复制该实现。是否复用必须重新做 RegBase 专项设计判断。
满足以下任一条件时使用该能力：
不要把该能力当成默认算子开发路径的通用替代品。技术路线未决时，由 Architect 完成 SIMD/MemBase 与 RegBase 的方案决策。
references/regbase_development_guide.mdreferences/api/index.mdreferences/pitfalls/index.mdreferences/dev-experience/index.mdreferences/reference-ops/open_source_operator_table.mdreferences/regbase_development_guide.md、references/dev-experience/index.mdreferences/regbase_development_guide.md、references/api/index.mdreferences/api/index.md、references/dev-experience/regbase_programming_notes.md、references/reference-ops/open_source_operator_table.mdreferences/pitfalls/index.md、references/dev-experience/index.mdreferences/pitfalls/symptom_to_cause.md、references/dev-experience/index.md进入 api/、pitfalls/ 或 dev-experience/ 后，先读该目录的 index.md，再打开叶子文档。针对一个子问题，最多展开 5 篇叶子文档；仍不够时拆分问题，而不是一次加载整个子树。
open_source_operator_table.md 定位候选目录，并阅读至少一个经确认包含 RegBase 证据的真实实现。RegTensor / MaskReg / asc_vf_call / __simd_vf__ 等关键词时，优先检查 API 层和 pitfalls 层。DAV_3510 / RegBase，需要说清楚。