jpa-patterns
JPA/Hibernate 模式,涵盖 Spring Boot 中的实体设计、关系、查询优化、事务、审计、索引、分页和连接池。
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
JPA/Hibernate 模式,涵盖 Spring Boot 中的实体设计、关系、查询优化、事务、审计、索引、分页和连接池。
Add a new AWS event source attribute (e.g., Kinesis, Kafka, MQ) to the Lambda .NET Annotations framework, including the attribute class, source generator integration, CloudFormation writer, unit tests, writer tests, source generator tests, and integration tests
Migrate a TensorRT build from weak typing (deprecated 10.12, removed 11.0) to strong typing — across Python INetworkDefinition builders, the trtexec CLI, and C++ builder code. Use when a TRT 11 upgrade breaks a weakly-typed build. Triggers: weakly typed to strongly typed, kSTRONGLY_TYPED, weak typing deprecated, kFP16/kINT8 removed, setPrecision rejected, setComputePrecision deprecated, do I still need --stronglyTyped, how to add the kSTRONGLY_TYPED flag, ModelOpt autocast, INT8 on TRT 11. NOT for ONNX import (`trt-onnx-quickstart`), Torch-TRT (`trt-torch-quickstart`), or C++ deploy (`trt-cpp-runtime-quickstart`).
Compile SQL migration files into Go source code for embedding in binaries
Load and run a TensorRT engine (.plan / .engine) from C++ using the TensorRT 11 / 10.x **modern Runtime API**, avoiding the deprecated TRT 8.x binding-index APIs that older guidance still promotes. Use whenever the user asks about loading or running a TensorRT .plan/.engine from C++, even on "minimal example" requests — without this skill the default reply uses deprecated enqueueV2-style code. Also use when the user hits "Engine plan file is generated on an incompatible device", deserializeCudaEngine returns nullptr, gets an enqueueV2 / IStreamReader deprecation warning, or wants to stream a .plan via IStreamReaderV2. Triggers: TensorRT C++ inference, load TensorRT plan C++, run .plan from C++, IRuntime example, deserializeCudaEngine, enqueueV3, enqueueV2 deprecated, setTensorAddress, getBindingIndex, IStreamReaderV2, libnvinfer C++. NOT for building engines (`trt-onnx-quickstart`), Python deploy, plugins, multi-GPU.
Build and verify a TensorRT engine from a Hugging Face model ID or ONNX file, with numerical parity checked against ONNX Runtime. Use when the user imports a non-LLM model to TensorRT, needs a verified engine from ONNX, hits trtexec "unsupported operator", must verify the engine matches ONNX numerically, debugs a polygraphy parity failure (large max abs diff at FP16), or configures multi-input dynamic shapes. Triggers: convert ONNX to TensorRT, Hugging Face to TensorRT, trtexec onnx, trtexec unsupported operator, optimum-cli export, polygraphy parity check, polygraphy run --trt --onnxrt, parity check failed, max abs diff, verify engine matches ONNX, --minShapes, dynamic shapes trtexec, multi-input shape profile, FP16 engine, INT64 warning. Adjacent skills: `trt-torch-quickstart` (PyTorch frontend), `trt-cpp-runtime-quickstart` (C++ engine load). LLM token generation belongs in TensorRT-LLM, not here.
Compile a PyTorch model to a TensorRT engine via Torch-TensorRT — AOT or JIT — under the new strong-typing default. Use when the user compiles PyTorch to TensorRT without ONNX, hits "enabled_precisions should not be used when use_explicit_typing=True", sees Dynamo graph breaks or PyTorch fallback, debugs ABI errors at import torch_tensorrt, or needs the compatible torch / torch_tensorrt / tensorrt-cu13 version pins for TensorRT 11. Triggers: torch_tensorrt, torch_tensorrt.dynamo.compile, torch.compile backend torch_tensorrt, pytorch to tensorrt, ExportedProgram, Dynamo graph break, use_explicit_typing, enabled_precisions, torch_tensorrt.Input, min_block_size, truncate_double, tensorrt-cu13, version pinning, version compatibility. Adjacent skills: `trt-onnx-quickstart`, `trt-cpp-runtime-quickstart`. LLM token generation belongs in TensorRT-LLM.
Implements new RobustMQ MQTT connector integrations end-to-end using project conventions. Use when the user asks to add, implement, or support a new connector type such as webhook, opentsdb, clickhouse, influxdb, cassandra, mqtt bridge, or protocol-compatible targets.
Complete step-by-step guide for implementing a new protocol Broker in RobustMQ. Use when the user asks to add a new broker, implement a new protocol, or scaffold a new broker crate.
Analyze a website's API by capturing network traffic (HAR) and generating an OpenAPI spec via mitmproxy2swagger.