Build OBS Studio plugins for Windows using MSVC or MinGW. Covers Visual Studio setup, .def file exports, Windows linking (ws2_32, comctl32), platform-specific sources, and DLL verification. Use when building OBS plugins natively on Windows or troubleshooting Windows builds.
Create new vision detector plugins following Bob The Skull's detector architecture. Use when adding new detectors like object detection, pose estimation, gesture recognition, or any computer vision detector.
Solves linear systems (Ax=b) using NumPy, JAX, PyTorch, and Lineax. Covers dense, sparse, and specialized (tridiagonal) solvers.
High-performance C development for data-intensive systems, with explicit emphasis on time-indexed / log-structured in-memory engines (e.g., Timelog-class designs). Use when building advanced data structures, algorithms, or libraries in C with focus on: memory efficiency, cache locality, immutable segment layouts, atomic publication, snapshot reads, SIMD/bit operations, and (future) Python bindings. Applies to: custom allocators, paged/segment storage, compression and bitmaps, index structures, single-writer/multi-reader concurrency patterns, background maintenance (flush/compaction), and performance-critical library development.
Avoid non-null assertion operator (!) and use type-safe alternatives instead
Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows.
ChimeraX bundle/extension development reference
Astral社製ツール（uv, Ruff, ty）でPython開発環境を構築。プロジェクト作成、依存関係管理、コード品質改善、CI/CD設定時に使用。
Fix common PyTorch bugs: percentile calculations, LayerNorm for Conv1d, buffer edge cases. Trigger when writing PyTorch code for RL or neural networks.
Use when improving data loading, validation, and IO boundaries in Python research code.
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use for ML training, analytics tools, performance profiling, or any Python heavy lifting.
Universal functions (ufuncs) for vectorization, including reductions, in-place operations, and custom Python-function wrapping. Triggers: ufunc, vectorize, reduce, accumulate, frompyfunc, in-place.
Protocol untuk meningkatkan kecerdasan, presisi, dan keamanan dalam coding dan debugging.
Exporting PyTorch models to ONNX format for cross-platform deployment. Includes handling dynamic axes, graph optimization in ONNX Runtime, and INT8 model quantization. (onnx, onnxruntime, torch.onnx.export, dynamic_axes, constant-folding, edge-deployment)
Assert CPU-only runtime inside a container using PyTorch (torch.cuda.is_available()==False) and optional env var checks. Use to prevent accidental GPU execution during CPU smoke tests.
WebGPU fundamentals for high-performance canvas rendering. Covers device initialization, buffer management, WGSL shaders, render pipelines, compute shaders, and web component integration. Use when building GPU-accelerated graphics, particle systems, or compute-intensive visualizations.
统一开发编排器：将 Superpowers 方法论框架与 AI 代码工厂深度整合， 创建统一的开发工作流，结合 TDD 纪律、SoT 合规和自动化代码生成。
Guides adding new Higher Inductive Types to the ComputationalPaths library. Use when creating new HITs, defining fundamental group (pi1) calculations, implementing encode-decode proofs, or adding new topological spaces.
Optimizes TensorFlow.js WebGPU backend, WebLLM offline inference, and implements memory safety for heavy ML models
Audit and remove unnecessary abstractions from the codebase. Use when user wants to simplify, reduce dependencies, or eliminate complexity that slows down AI agents.
