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Agent skills by ascend-ai-coding
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Repositories by ascend-ai-coding
Skills by ascend-ai-coding, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | 当用户需要对华为昇腾 NPU 进行硬件层面的管理、测试或诊断时使用此 skill。典型场景： - 查看 NPU 卡的状态、温度、利用率 - 测试内存带宽（h2d/d2h/d2d/p2p） - 跑算力/功耗基准测试（TFLOPS、TOPS） - 诊断 NPU 硬件故障或做健康检查 - 对 NPU 卡做压力测试（aicore、内存） - 复位/恢复卡住或异常的 NPU 卡 典型用户问题（即使不提… | ascend-ai-coding/ | 174 | — | ~1.7k | Automated safety check: Pass | No licence | today |
| 2 | 2.Ascendc End-to-end AscendC custom operator development for Ascend NPU in an ascend-kernel (csrc/ops + build.sh + torchnpu PyTorch custom op) project. | ascend-ai-coding/ | 174 | — | ~3.5k | Automated safety check: Pass | No licence | today |
| 3 | Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. | ascend-ai-coding/ | 174 | — | ~4.6k | Automated safety check: Pass | No licence | today |
| 4 | 当需要编写 PyPTO 算子实现时使用此 skill。基于需求规格、设计方案和参考实现，生成完整可运行的 PyPTO 算子实现与配套测试、文档。Triggers: 实现算子、写 kernel、编写实现、写 impl、算子编码、开始编码、code the op、写 test、生成测试、写实现代码、op develop、kernel 实现。 | ascend-ai-coding/ | 174 | — | ~2.1k | Automated safety check: Pass | No licence | today |
| 5 | Analyze official Megatron-LM commits, PRs, and branch change sets to identify feature evolution, candidate breaking changes, and migration-relevant events. | ascend-ai-coding/ | 174 | — | ~1.2k | Automated safety check: Pass | No licence | today |
| 6 | Track and normalize change requests against the official Megatron-LM repository by branch, PR, commit, commit range, or time window. | ascend-ai-coding/ | 174 | — | ~1.1k | Automated safety check: Pass | No licence | today |
| 7 | Map migration-relevant Megatron changes onto the official MindSpeed repository by resolving branch alignment, locating affected subsystems, and identifying concrete adaptation points. | ascend-ai-coding/ | 174 | — | ~1.3k | Automated safety check: Pass | No licence | today |
| 8 | Automates GitCode open-source repo merge workflow: commit → push → issue → PR → pipeline → review → /lgtm & /approve → merge. | ascend-ai-coding/ | 174 | — | ~1.4k | Automated safety check: Pass | No licence | today |
| 9 | Analyze closed GitHub issues to create troubleshooting case studies with root cause analysis and lessons learned. | ascend-ai-coding/ | 174 | — | ~1.4k | Automated safety check: Pass | No licence | today |
| 10 | Automatically fetch InferenceX benchmark data and generate daily performance reports for LLM inference on various hardware (NVIDIA, AMD, etc.). | ascend-ai-coding/ | 174 | — | ~1.1k | Automated safety check: Pass | No licence | today |
| 11 | 11.Npu Smi Huawei Ascend NPU npu-smi command reference. An agent skill from ascend-ai-coding/awesome-ascend-skills. | ascend-ai-coding/ | 174 | — | ~2k | Automated safety check: Pass | No licence | today |
| 12 | 12.Create PR Creates GitHub pull requests with properly formatted titles that pass the check-pr-title CI validation. | ascend-ai-coding/ | 174 | 1 repo | ~1.2k | Automated safety check: Pass | No licence | today |
| 13 | Migrate any HuggingFace model to Ascend NPU torchair graph mode (torch.compile) and benchmark it for accuracy and performance against NPU eager and CPU eager. | ascend-ai-coding/ | 174 | — | ~1.3k | Automated safety check: Pass | No licence | today |
| 14 | 通过 PyTorch torch.distributed 接口测试昇腾 NPU 通信算子性能。支持指定任意 tensor shape、dtype，使用 torchrun 启动，贴近真实训练场景的通信算子测试与性能分析。Use for testing collective communication operators (AllReduce, AllGather, ReduceScatter… | ascend-ai-coding/ | 174 | — | ~2.2k | Automated safety check: Pass | No licence | today |
| 15 | 15.Vllm Ascend vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. | ascend-ai-coding/ | 174 | — | ~2.7k | Automated safety check: Pass | No licence | today |
| 16 | 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… | ascend-ai-coding/ | 174 | — | ~731 | Automated safety check: Pass | No licence | today |
| 17 | 17.Hccl Test HCCL (Huawei Collective Communication Library) performance testing for Ascend NPU clusters. | ascend-ai-coding/ | 174 | — | ~2.2k | Automated safety check: Warn | No licence | today |
| 18 | Interactive online benchmark orchestrator for vLLM inference services using vllm bench serve. | ascend-ai-coding/ | 174 | — | ~5.6k | Automated safety check: Pass | No licence | today |
| 19 | 19.Ais Bench AISBench Benchmark - AI model evaluation tool for Ascend NPU. | ascend-ai-coding/ | 174 | — | ~2.7k | Automated safety check: Pass | No licence | today |
| 20 | Create Docker containers for Huawei Ascend NPU development with proper device mappings and volume mounts. | ascend-ai-coding/ | 174 | — | ~1.3k | Automated safety check: Pass | No licence | today |
| 21 | Analyze Huawei Ascend NPU profiling data to discover hidden performance anomalies and produce a detailed model architecture report reverse-engineered from profiling. | ascend-ai-coding/ | 174 | — | ~5.8k | Automated safety check: Pass | No licence | today |
| 22 | Verl 分布式训练服务一键拉起与配置。触发场景：(1) 用户要启动 Verl 训练任务或部署 RLHF/DAPO 训练环境 (2) 在 NPU 集群上拉起 Verl 训练容器 (3) 配置 Ray 集群和 SwanLab 监控 (4) 根据 7 位二进制掩码灵活配置加速特性。支持 Qwen3-8B 等 Megatron 模型的 DAPO 训练全流程。 | ascend-ai-coding/ | 174 | — | ~2k | Automated safety check: Pass | No licence | today |
| 23 | 昇腾 NPU 单算子性能基准测试 Skill；当前版本只做现有环境检查、CANN 版本识别、用户确认后执行 benchmark，不负责修复或安装环境。 | ascend-ai-coding/ | 174 | — | ~617 | Automated safety check: Pass | No licence | today |
| 24 | Skill for analyzing communication performance bottlenecks and detecting slow/fast rank issues in Ascend NPU systems. | ascend-ai-coding/ | 174 | — | ~590 | Automated safety check: Pass | No licence | today |
| 25 | 25.Rl Msprobe 自动化 verl msprobe 精度数据采集；开始前检查/预装 msprobe（pip install mindstudio-probe）。自动识别三种模式：(1) 训练采集——globalprofiler + precisiondebugger stages；(2) 推理采集——vLLM/SGLang rollout dump；(3) 训推一致性——engine patch +… | ascend-ai-coding/ | 174 | — | ~2.4k | Automated safety check: Pass | No licence | today |
| 26 | AI for Science 场景下的昇腾 NPU Profiling 采集与性能分析 Skill，用于在华为 Ascend NPU 上使用 torchnpu.profiler 采集 L0、L1、L2 级性能数据，分析训练或推理中的算子耗时、调用栈、内存与瓶颈，并指导后续调优。 | ascend-ai-coding/ | 174 | — | ~3k | Automated safety check: Pass | No licence | today |
| 27 | Ankh 蛋白质语言模型昇腾 NPU 迁移 Skill，适用于 Ankh base/large、Ankh3 large/XL 以及同类基于 HuggingFace Transformers 与 PyTorch 的蛋白模型从 CUDA/GPU 到华为 Ascend NPU 的环境检查、代码适配、权重加载、验证脚本补齐与文档沉淀。 | ascend-ai-coding/ | 174 | — | ~2.1k | Automated safety check: Pass | No licence | today |
| 28 | 昇腾 TensorFlow Community 迁移适配 Skill，适用于将基于 TensorFlow 2.x 的模型原生部署到华为 Ascend NPU，而不经过 TF 到 PyTorch 转换，覆盖 aarch64 源码编译 TF 2.6.5、tfplugin 安装、自动迁移工具使用、手动适配与精度验证。 | ascend-ai-coding/ | 174 | — | ~2.1k | Automated safety check: Pass | No licence | today |
| 29 | Boltz2 蛋白质结构预测模型的昇腾 NPU 迁移与复现 Skill，适用于在华为 Ascend 910、910B、910C 上准备权重、适配 Lightning 和 CUDA only kernel、完成 Boltz2 端到端结构预测推理，并沉淀可复现的环境与验证命令。 | ascend-ai-coding/ | 174 | — | ~2.8k | Automated safety check: Pass | No licence | today |
| 30 | BoltzGen 昇腾 NPU 迁移与复现 Skill，适用于在华为 Ascend NPU 上部署 BoltzGen 生成式蛋白设计与逆折叠流程，覆盖环境准备、权重缓存、cuEquivariance 兼容、源码适配和端到端推理验证。 | ascend-ai-coding/ | 174 | — | ~3.8k | Automated safety check: Pass | No licence | today |
| 31 | DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。 | ascend-ai-coding/ | 174 | — | ~2.4k | Automated safety check: Pass | No licence | today |
| 32 | DeepFRI TensorFlow 原生昇腾 NPU 迁移 Skill，适用于不做 TF 到 PyTorch 转换、而是直接使用 TensorFlow 2.6.5 与 npudevice 在华为 Ascend 上运行 DeepFRI 的场景，覆盖源码编译、tfplugin 安装、代码适配、推理与 CPU 对比验证。 | ascend-ai-coding/ | 174 | — | ~2.1k | Automated safety check: Pass | No licence | today |
| 33 | DiffSBDD 昇腾 NPU 迁移 Skill，适用于将基于等变扩散模型的结构化药物设计项目从 CUDA 迁移到华为 Ascend NPU，覆盖环境搭建、依赖安装、torchscatter 源码编译、代码适配以及 de novo 推理验证。 | ascend-ai-coding/ | 174 | — | ~898 | Automated safety check: Pass | No licence | today |
| 34 | GENERator DNA 序列生成模型的昇腾 NPU 迁移 Skill，适用于将基于 HuggingFace Transformers 的 Causal LM 从 CUDA 迁移到华为 Ascend NPU，覆盖环境搭建、依赖安装、代码适配、多进程处理和 sequence recovery 验证。 | ascend-ai-coding/ | 174 | — | ~827 | Automated safety check: Pass | No licence | today |
| 35 | OligoFormer 昇腾 NPU 迁移 Skill，适用于将基于 PyTorch Transformer 的 siRNA 效能预测模型迁移到华为 Ascend NPU，覆盖环境搭建、RNA-FM 依赖安装、代码适配、推理验证与可选训练流程。 | ascend-ai-coding/ | 174 | — | ~1.3k | Automated safety check: Pass | No licence | today |
| 36 | ProteinBERT 昇腾 NPU 部署与迁移 Skill，适用于将 TensorFlow 或 Keras 版 ProteinBERT 转成基于 PyTorch 与 torchnpu 的实现，覆盖权重转换、embedding 提取、微调训练、注意力可视化和 GPU 与 NPU 精度验证。 | ascend-ai-coding/ | 174 | — | ~1.9k | Automated safety check: Pass | No licence | today |
| 37 | TensorFlow 或 Keras 模型改写到 PyTorch 的通用 Skill，适用于在华为 Ascend NPU 或其他依赖 PyTorch 生态的平台上完成层级映射、权重转换、逐层数值验证和端到端精度对比，尤其适合 ProteinBERT、DeepFRI 这类科学模型的跨框架迁移。 | ascend-ai-coding/ | 174 | — | ~3.4k | Automated safety check: Pass | No licence | today |
| 38 | HuggingFace Diffusers 环境配置指南，用于华为昇腾 NPU。覆盖 CANN 版本检测、PyTorch + torchnpu 安装、Diffusers 库安装及环境验证。当用户需要在昇腾 NPU 上配置 Diffusers 环境时使用。 | ascend-ai-coding/ | 174 | — | ~566 | Automated safety check: Pass | No licence | today |
| 39 | Diffusers Pipeline 推理指南，用于华为昇腾 NPU。覆盖环境预检、通用 Pipeline 推理（图像/视频模型）、内存优化（CPU offload、attention slicing、VAE slicing）、LoRA 加载与融合、多卡推理和按版本检索 Diffusers API。用户一旦提到在昇腾 NPU 上运行 FLUX、SDXL、Wan、CogVideoX 等… | ascend-ai-coding/ | 174 | — | ~3.2k | Automated safety check: Pass | No licence | today |
| 40 | 基于 PyTorch 框架的昇腾 NPU 模型推理融合算子优化技能。分析模型代码，识别可替换为 torchnpu 融合算子的计算模式，生成替换方案。触发场景：torchnpu 融合算子替换、MoE/Attention/FFN/Norm 等模块的推理算子适配、torchnpu API 使用咨询。基于仓库已有模型的融合算子经验，按计算语义推荐最佳方案。 | ascend-ai-coding/ | 174 | — | ~1.7k | Automated safety check: Pass | No licence | today |
| 41 | 基于 PyTorch 框架的昇腾 NPU 模型推理适配与部署基线技能。从 HF 链接或本地模型代码出发，按 cann-recipes-infer 仓库规范适配到 ModelRunner 推理框架，输出可运行的标准模型目录和性能基线数据。触发场景：新模型适配到昇腾 NPU 推理框架、已有模型的部署基线采集、模型迁移和初始跑通验证。 | ascend-ai-coding/ | 174 | — | ~1.8k | Automated safety check: Pass | No licence | today |
| 42 | Ascend C 算子卡死/崩溃调试技能。用于处理程序无法运行完的场景：(1) 程序卡死/挂起/超时，Kernel 无响应，(2) 程序崩溃（Segmentation Fault、Abort），(3) Buffer 冲突/死锁导致的核心挂起，(4) 需要解析 plog 日志定位卡死/崩溃位置。触发关键词：卡死、挂起、超时、崩溃、hang、crash、deadlock、Segmentation… | ascend-ai-coding/ | 174 | — | ~502 | Automated safety check: Pass | No licence | today |
| 43 | Ascend C 开发资源检索技能。通过本地 API 文档索引、示例代码映射和在线文档兜底搜索定位开发资料，优先查本地、缺失时再查在线。当需要查询 API 用法、示例代码、兼容性信息、官方资料入口或定位文档来源时使用。 | ascend-ai-coding/ | 174 | — | ~989 | Automated safety check: Pass | No licence | today |
| 44 | Ascend C 算子精度调试技能，提供精度问题诊断和解决方法。触发：输出异常（全为0、随机值、未初始化）、精度验证失败（rtol/atol 不达标）、FP16 精度差于预期、Cast 后数据错误、需要排查流水线同步（EnQue/DeQue）或 DataCopy 对齐问题。 | ascend-ai-coding/ | 174 | — | ~2.2k | Automated safety check: Pass | No licence | today |
| 45 | Ascend C 算子运行时错误调试技能。用于处理算子运行时问题：(1) aclnn 返回错误码（161xxx/361xxx/561xxx，包括环境配置、Tiling、Kernel 查找等错误），(2) 需要解析 plog 日志定位问题。触发关键词：运行时错误、错误码、Tiling错误、Kernel查找失败、环境变量、plog。 | ascend-ai-coding/ | 174 | — | ~528 | Automated safety check: Pass | No licence | today |
| 46 | NPU 性能采集与分析，用于采集算子性能数据、定位性能瓶颈并给出优化建议。当用户在算子开发过程中提到"上板性能"、"算子性能测试"、"硬件性能验证"、"NPU性能采集"、"NPU profiling"等场景时触发。 | ascend-ai-coding/ | 174 | — | ~2k | Automated safety check: Pass | No licence | today |
| 47 | 当需要设计 PyPTO 算子实现方案时使用此 skill。基于算子规格与相关上下文，生成 DESIGN.md（含 API 映射、Tiling 策略、Loop 结构）。Triggers: 生成设计方案、生成 design、设计这个算子、写 DESIGN.md、算子设计、API 映射、Tiling 策略、tiling strategy、Loop 结构、数据切分、怎么切分数据、怎么做… | ascend-ai-coding/ | 174 | — | ~2.4k | Automated safety check: Pass | No licence | today |
| 48 | 从用户 PyTorch/Python 代码中提取算子实现，构建为算子任务格式的标准化 任务文件。支持两种模式：单 case（单一自包含 .py，getinputs 返回单组）和 多 case（.py + 同名 .json 配对，getinputgroups 返回多组）。 | ascend-ai-coding/ | 174 | — | ~1.4k | Automated safety check: Pass | No licence | today |
Questions, answered from the data.
What is the best skill by ascend-ai-coding?
Ascend Dmi from ascend-ai-coding/awesome-ascend-skills ranks first of the 70 skills by ascend-ai-coding listed here, with the highest score: its repository has 174 GitHub stars, its SKILL.md loads about 1.7k tokens and it passes the automated safety check with no findings. Next come Ascendc and Atc Model Converter.
Are ascend-ai-coding's skills official?
None yet. All 70 skills by ascend-ai-coding listed here come from community repositories; a skill counts as official when the product's own GitHub organization publishes it.
How are these skills ranked?
By Skill Navigator score, which combines the GitHub stars of the skill's repository (shared across that repo's skills and discounted for large collections), how many other GitHub owners carry a copy of the skill, and automated SKILL.md quality checks, minus penalties for safety-check warnings and for each further skill from the same repository. Skills that fail the safety check are not listed.