react-pattern
Reasoning and Acting patterns for agentic LLM workflows
Reasoning and Acting patterns for agentic LLM workflows
Advanced human-AI co-creation methodology - the Centaur Principle in practice. Master the art of combining human creativity with AI capability for results neither could achieve alone.
Explains how to execute Synapse plugins programmatically. Use when the user mentions "run_plugin", "ExecutionMode", "LocalExecutor", "RayActorExecutor", "RayJobExecutor", "PluginDiscovery", "from_path", "from_module", or needs help with running plugins programmatically.
Expert knowledge for UX Layer modeling in Documentation Robotics
Use when designing futuristic agentic workflows, when wanting AI to proactively act on team communications, or when eliminating the bottleneck of formal specifications
Deploy and monitor model API endpoints in Domino. Covers creating prediction endpoints, version management, Grafana dashboards for latency/errors/resources, alerting, and GPU inference with NVIDIA Triton. Use when deploying models as APIs, monitoring production endpoints, or debugging endpoint issues.
Evaluates the user experience of the bot's command interface and provides actionable recommendations
Automated context retrieval from Transmission Packet archive using iterative research loop. Implements GAM "Read Path" to complement manual "Write Path" (Memorizer).
Answers questions about Claude Code features, configuration, and usage from local documentation synced from code.claude.com. Use when users ask about hooks, plugins, skills, MCP servers, slash commands, sub-agents, settings, permissions, sandboxing, CLAUDE.md memory files, model selection, costs, IDE integrations (VS Code, JetBrains), CI/CD (GitHub Actions, GitLab), or cloud providers (Bedrock, Vertex, Azure).
Forecast convergence patterns in multi-model consensus scenarios.
Model optimization techniques including hyperparameter tuning, architecture search, training optimization, and performance profiling for ML systems.
Master ML experiment tracking - MLflow, W&B, Neptune, versioning, reproducibility
Build production-ready classification and regression models with hyperparameter tuning
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
Real-time monitoring and detection of adversarial attacks and model drift in production
Extract, learn, and integrate PR feedback into the Violet brain
Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.
Execute mechanistic interpretability experiments from JSON specs - family sweeps, itemsets, interactions, minimal cores, validation
Integrate adaptive temporal analysis for drift detection.
Track ML experiments with proper logging and reproducibility. Use when training models or running experiments.
Run credibility checks on feature interpretations including split-half stability and shuffle null tests
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection