Results for “scene-optimization”

Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.

Optimize context usage for the session by reducing bloat, improving token efficiency, and focusing on relevant areas. Use when context is growing large, responses are slow, or switching between unrelated tasks. Covers context analysis, compacting, targeted pruning, MCP-first strategies, and focused scoping by area.

Generate a high-cut-density action/fight scene by composing a 16-cell storyboard image and driving Seedance 2.0 image-to-video from that storyboard.

Optimizes long-running agent sessions with structured context compression, summarization, and durable handoff summaries that preserve decisions, files, risks, and next actions.

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates with evals.

Design, implement, budget, and verify readable real-time game visual effects: particles, smoke/fog, lightning, fire, trails, impacts, bloom/glow, and layered spell timelines. Use when a user needs a VFX spec, lifecycle, engine handoff, particle pooling, effect composition, reduce-motion fallback, or frame-budget diagnosis. Triggers on: game VFX, spell effects, particle system, impact burst, procedural lightning, smoke, bloom, magic aura, or VFX performance.

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

nuScenes: A Multimodal Dataset for Autonomous Driving

Reduces AI API costs by 30-75% by classifying task complexity, checking prompt quality, and routing tasks to the most cost-efficient model tier before execution.

Generate visual storyboards with AI image generation, covering shot types, camera angles, movement, continuity rules, and panel layout for video planning and pre-production.

Transform the agent into a performance engineer. Apply methodologies for measuring, profiling, and optimizing code (caching, algorithm complexity, resource usage).