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Found 3,130 Skills
Use this skill when the user wants to create a new issue, report a bug, submit a feature request, or discuss a requirement before implementation. "create issue", "report bug", "create-issue", "submit issue", "新建 issue", "提需求", "提 bug". Requires Gitee MCP Server to be configured.
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Use this skill to query Solana blockchain data via the Solscan Pro API. Triggers: look up wallet address, check token price, analyze NFT collection, inspect transaction, explore DeFi activities, get account metadata/label/tags, fetch block info, monitor API usage, search token by keyword.
Integrates GuaraCloud into local development workflows — project linking, remote shell access, port forwarding, log streaming, and environment management. Use when the user wants to connect their local environment to GuaraCloud, tail logs, exec into a container, or forward ports.
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.
Triage a daily msverl regression run by reading the baseline comparison log, stopping on success, extracting the most relevant training failure evidence from the daily training log when needed, collecting recent commits from verl main and MindSpeed master, and ranking the most likely culprit commits with concise fix-direction guidance.
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.