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Found 3,526 Skills
Use when deleting a Zeabur project. Use when user says "delete project", "remove project", or "clean up project". Use when tearing down test or temporary projects. Always confirm project name and ID with the user before deleting.
Use when choosing the best first failing RSpec spec or vertical slice for a Ruby on Rails change. Covers request vs model vs service vs job vs engine spec selection, system spec escalation, smallest safe slice planning, and Rails-first TDD sequencing. Trigger words: where to start testing, what test to write first, RSpec, test-driven development, TDD, first failing test.
Use when investigating a bug, error, or regression in a Ruby on Rails codebase. Creates a failing RSpec reproduction test, isolates the broken code path, and produces a minimal fix plan. Trigger words: debug, broken, error, regression, stack trace, failing test, RSpec, bug report, Rails app.
Catlass Operator End-to-End Development Orchestrator. Based on ascend-kernel (csrc/ops), it connects catlass design, catlass-operator-code-gen and ascendc sub-skills to complete the closed loop from project initialization to documentation, precision, and performance. Keywords: Catlass, end-to-end, ascend-kernel, operator development, workflow orchestration.
Verify and build the required environment for Triton operator development on the Ascend platform, including configurations of dependencies such as CANN, Python/torch/torch_npu/triton-ascend and PATH environment variables. This is used when users need to configure the Triton operator development environment, check the installation of CANN/torch/triton-ascend, or verify whether the environment is available.
Generate PyTorch-style interface documentation (README.md) for AscendC operators. Trigger scenarios: Use this when interface documentation needs to be generated after compilation and debugging are completed, or when the user mentions "generate operator documentation", "create README", "document operator", "help me write documentation" (in operator context), "operator documentation".
Python code refactoring skills, covering code smell identification, design pattern application, readability improvement, and practical experience. This skill is applicable when users request "refactor code", "refactor", "code optimization", "improve code quality", "code smell review", "apply design patterns", "enhance readability", or submit code review requests. It supports generating structured refactoring documents after refactoring completion ("output refactoring document", "generate refactoring report"). It includes practical patterns extracted from 20+ real refactoring PRs in the vllm-ascend repository.
Guide Catlass operator performance tuning. Process: Read the Catlass optimization guide, obtain/update profiler baseline, modify tiling according to the guide, recompile, **mandatorily generate and display performance comparison report**, iterate and compare. Tuning strategies are based on Catlass documentation. Ask for clarification if conditions are unclear.
HCCL (Huawei Collective Communication Library) performance testing for Ascend NPU clusters. Use for testing distributed communication bandwidth, verifying HCCL functionality, and benchmarking collective operations like AllReduce, AllGather. Covers MPI installation, multi-node pre-flight checks (SSH/CANN version/NPU health), and production testing workflows.
Provides installation guidance for CANN on Ascend NPU. Call this skill when users need to install CANN, configure the Ascend environment, or resolve installation issues.
Maintain JSONL-only profiler performance test cases under csrc/ops/<op>/test in ascend-kernel. Collect data using torch_npu.profiler (with fixed warmup=5 and active=5), aggregate the Total Time(us) from ASCEND_PROFILER_OUTPUT/op_statistic.csv, and output a unified Markdown comparison report (custom operator vs baseline) that includes a DType column. Do not generate perf_cases.json or *_profiler_results.json. Refer to examples/layer_norm_profiler_reference/ for the reference implementation.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.