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Generate production-ready 3D models using each::sense. This skill creates 3D assets from text descriptions or reference images, suitable for games, e-commerce, architecture, product visualization, and more.

Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation inline as decisions crystallise.

Comprehensive guide to using Meta AI's Segment Anything Model for zero-shot image segmentation.

AI text generation and streaming for WeChat Mini Programs via wx.cloud.extend.AI. \n \n Supports two methods: generateText() for non-streaming responses and streamText() for real-time streaming with callback support ( onText , onEvent , onFinish ) \n Built-in models include Hunyuan (recommended: hunyuan-2.0-instruct-20251111 ) and DeepSeek (recommended: deepseek-v3.2 ) \n API differs from JS/Node SDK: generateText() returns raw model response; streamText() requires parameters wrapped in a data o

Cooperative async patterns using explicit state machines, completions, and re-entrancy safeguards for Turso's I/O model. \n \n Core types: IOResult<T> (returns Done or IO requiring re-call) and Completion for tracking individual operations \n CompletionGroup aggregates multiple completions into one, with nesting and cancellation support \n State machine pattern encodes progress in enum variants to safely handle re-entry across yield points \n Critical pitfall: mutating shared state before y

Per-model cost summaries from CodexBar CLI logs for Codex or Claude providers. \n \n Supports two summary modes: \"current\" (most recent daily model with highest cost) and \"all\" (full model breakdown across all logged days) \n Accepts input via live CodexBar CLI invocation, JSON file, or stdin; outputs as plain text or formatted JSON \n Requires CodexBar CLI installed locally (macOS only via Homebrew; Linux support pending) \n Falls back to last entry in modelsUsed when model breakdowns are u

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Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions.

Monitoring deployed machine learning models ensures they continue to perform well in production, detecting data drift, concept drift, and performance degradation.

Hyperparameter tuning is the process of systematically searching for the best combination of model configuration parameters to maximize performance on validation data.