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Found 104 Skills
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Byzantine consensus voting for multi-agent decision making. Implements voting protocols, conflict resolution, and agreement algorithms for reaching consensus among multiple agents.
Business logic implementation. Apply when implementing core business rules, validation logic, workflows, state machines, and domain-specific algorithms for new features.
Analyze candidate algorithms for time/space complexity, scalability limits, and resource-budget fit (CPU, memory, I/O, concurrency). Use when feasibility depends on input growth or latency/memory constraints and quantitative bounds are required before implementation; do not use for persistence schema or deployment topology decisions.
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
Implement quiz game logic including game creation, player turn management, score calculation, answer validation, and game completion. Use when building game flows, turn-based mechanics, and scoring algorithms.
Combining IoT sensor data using algorithms like Kalman filters for improved accuracy and reliability
DeepMind Researcher: AGI through deep understanding, AlphaGo/AlphaZero RL, AlphaFold scientific discovery, Gemini multimodal, neuroscience-inspired architectures. Scientific rigor + industrial scale. Triggers: DeepMind research, AlphaGo algorithms, protein folding AI, scientif...