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CMake build options, custom functions, and backend patterns for LuisaCompute.

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Comprehensive best practices, design patterns, and common pitfalls for ROS2 (Robot Operating System 2) development. Use this skill when building ROS2 nodes, packages, launch files, components, or debugging ROS2 systems. Trigger whenever the user mentions ROS2, colcon, rclpy, rclcpp, DDS, QoS, lifecycle nodes, managed nodes, ROS2 launch, ROS2 parameters, ROS2 actions, nav2, MoveIt2, micro-ROS, or any ROS2-era robotics middleware. Also trigger for ROS2 workspace setup, DDS tuning, intra-process communication, ROS2 security, or deploying ROS2 in production. Also trigger for colcon build issues, ament_cmake, ament_python, CMakeLists.txt for ROS2, package.xml dependencies, rosdep, workspace overlays, custom message generation, or ROS2 build troubleshooting. Covers Humble, Iron, Jazzy, and Rolling distributions.

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Use when developing desktop applications with Qt in C++ - CMake setup, signals and slots, QThread and QtConcurrent, QML integration, model/view programming, Qt5 to Qt6 migration, or windeployqt/macdeployqt/linuxdeployqt deployment

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Modern C++ project setup: CMake/CMakePresets, vcpkg, Ninja, VS Code/clangd, GoogleTest, clang-format/tidy, Doxygen, CI, macOS/Linux/Windows. Use for bootstrapping or modernizing host-side C++ apps and libraries; prefer embedded-project-setup for MCU firmware and cross-debug flows.

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Embedded firmware setup: STM32, ESP32, AVR, RP2040, Nordic nRF, Zephyr/RTOS, vendor SDKs, CMake/CMakePresets, VS Code/clangd, cross toolchains, flash/debug, Docker/CI. Use for MCU scaffolds, existing firmware modernization, BSPs, linker/startup files, SDK package selection, and embedded CI; prefer cpp-project-setup for host-side C++.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Build text tokenizers that split multilingual text into small units for AI models. Learn how to train SentencePiece on raw text, configure BPE or Unigram splitting methods, and convert text to token IDs and back.

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• Preparing Japanese, Chinese, or Korean text for AI models
• Training a custom vocabulary from raw text
• Choosing between BPE and Unigram

Build Zoom meeting integrations in Windows C++ applications, including custom interfaces and bots that run without a visible interface. Guidance covers authentication, audio and video data access, project setup, and troubleshooting callbacks through the Windows message loop.

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• Embedding Zoom meetings in a Windows desktop application
• Building a meeting bot without a visible interface
• Receiving meeting audio and video data

Turn a validated build sheet into four API request files and a resumable launch script for Claude Managed Agent. Check the files for exposed keys before users launch with their own Anthropic key.

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• Preparing launch files from a validated build sheet
• Checking generated files for exposed API keys
• Preparing a launch that can resume after interruption

Close out a launched Claude Managed Agent by listing its recorded components and completed phases. Refresh a shareable, self-contained HTML overview and rank the next one or two upgrades from deferred work and operational improvements.

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• Reviewing the components of a launched agent
• Updating a shareable agent overview page
• Choosing the next one or two upgrades

Add new operators to MNN through a staged workflow covering data definitions, output shapes, computation, and tests. It prioritizes combining existing operators, with CPU implementations and additional hardware backends used when needed.

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• Adding a new operator to MNN
• Implementing output shape calculations
• Combining existing operators for new operations

Build text tokenizers, which split text into units for AI models, directly from raw multilingual text. Learn custom vocabularies with BPE or Unigram, convert text to pieces or numeric IDs, and decode the results.

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• Preparing text for multilingual AI models
• Training a vocabulary from Japanese text
• Reusing a tokenizer for reproducible text splitting

Write or modernize C++ with guidance on C++20, C++23, and C++26 features. Review memory ownership, error handling, and template constraints, with recommendations for compiler protections and runtime checks that detect bugs.

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• Modernize legacy C++ patterns
• Choose approaches for new C++ code
• Review memory ownership and error handling

Migrate legacy .NET project files to SDK-style format, reducing repetitive XML and explicit file lists. Convert package references, move assembly metadata into project settings, and consolidate shared build and package settings across multiple projects.

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• Convert legacy .csproj or .vbproj files to SDK-style
• Migrate packages.config to PackageReference
• Replace AssemblyInfo.cs with generated assembly metadata

Capture MSBuild execution records as binary logs during .NET builds, tests, packaging, publishing, or restores. It explains unique filenames, PowerShell quoting, and file checks so logs remain available for later failure or performance analysis.

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• Saving logs before investigating a build failure
• Keeping separate logs for Debug and Release builds
• Choosing log filenames for CI artifact uploads

Build Holoscan SDK from source via the in-tree ./run script. Use only when published packages don't meet the user's needs.

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Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

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Retro arcade-inspired design with pixel fonts, dotted borders, playful high-contrast colors, and 8-bit game aesthetics.

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Diagnoses and resolves Amazon RDS for Oracle connectivity, authentication, networking, and driver troubleshooting. Applicable to any RDS-for-Oracle question including connecting a Python Lambda to RDS Oracle in a VPC with pooling and cold-start optimization, EKS pods to RDS Oracle via the Secrets Manager CSI driver with IRSA and SecretProviderClass, ORA-12170 cross-VPC timeouts from EC2, DPI-1047 cannot-locate-64-bit-Oracle-Client errors, and Oracle Connection Manager (CMAN) on EC2 as a proxy with HA across two AZs. Covers python-oracledb thin vs thick mode, init_oracle_client, RDS Proxy does NOT support RDS Oracle, port 1521, VPC peering, Transit Gateway, Kerberos with AWS Managed Microsoft AD, SSL/TLS/NNE, SSM port forwarding, EC2/ECS Fargate/EKS/Lambda, SQL Developer/DBeaver/Toad/SQLcl, and Secrets Manager.

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Prepare and run XQUIC local compilation. Use when asked to compile, build, configure, or diagnose the local build environment for XQUIC, especially across macOS/Linux. Detect intent and platform, then delegate actual compilation to scripts/validate.sh instead of hand-writing CMake commands.

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Light and shade a three.js scene: choose materials (MeshStandardMaterial PBR vs unlit MeshBasicMaterial), add ambient/hemisphere/directional/point/spot lights, turn on shadow maps, and use an environment map (IBL) for realistic reflections. Use when a three.js model looks black, flat, or wrong — when the user mentions three.js materials, MeshStandardMaterial, lights, shadows, envMap, or PBR. For renderer/loop setup use threejs-scene-setup; for loading models use threejs-gltf-loading.

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