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    AI Short Drama Scene Assets

    1

    Generate spatially consistent 4-angle scene asset grids for AI video and game environments.

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    AI Short Drama Scene Assets

    AI Short Drama Scene Assets

    Example session with this skill installed

    Generate a consistent scene asset grid for a cyberpunk back alley. It's midnight, raining, with a neon-lit ramen stand as the central anchor. Start with the front view.

    • Read your context and instructions
    • Compiled the ai short drama
    • Generated the UI component

    I have established the Scene Bible for the "Cyberpunk Back Alley" and initiated the generation chain. Spatial anchors include a central ramen stand with a red neon sign, wet asphalt textures, and a fire escape on the left wall.

    Asset Generation Status: Cyberpunk Back Alley

    PerspectiveStatusFilenameKey Anchors
    V1: Front ViewCompleted01-front.pngRamen stand (center), Fire escape (left), Neon glow
    V2: Reverse ViewCompleted02-reverse.pngAlley exit to main street, Back of ramen stand
    V3: 45° ObliqueCompleted03-oblique-45.pngFull alley length, Z-axis depth of stalls
    V4: 90° Top-DownCompleted04-top-down.pngOrthographic layout of stalls and alley walls

    The images have been verified for spatial consistency. The red neon light source correctly illuminates the wet ground in all four perspectives, and the fire escape position remains fixed at coordinate [X: -4, Y: +2] relative to the ramen stand.

    Final Composite Deliverable
    The high-resolution 2x2 grid has been assembled using the compose_grid.py script.

    
# Output generated at: ./outputs/scene-asset-grid-cyberpunk-alley.png
# Resolution: 4096 x 2304 (2048 x 1152 per cell)

    Next steps

    1. Review the spatial alignment of the fire escape in the Oblique (V3) view.
    2. Run scripts/compose_grid.py with the --label flag if you require text annotations on the grid.
    3. Export the Top-Down (V4) view specifically for use as a level map layout.

    01-front.png

    TSX · React component

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Maintaining spatial consistency across multiple camera angles is one of the hardest tasks in AI image generation. This skill solves the problem of "drifting" environments where doors, windows, and landmarks change position between shots. It provides a structured workflow for developers and creators to generate production-ready scene assets for AI short dramas and games.

    What it does

    • Spatial Anchoring creates a "Scene Bible" to lock down structures, lighting, and object coordinates before generation.
    • Chain-of-Perspective Generation executes a sequential V1 to V4 workflow where each new angle builds directly on previous visual data.
    • Multi-Angle Coverage produces four distinct views: Front (0°), Reverse (180°), Oblique (45°), and Top-Down (90°).
    • Consistency Verification runs a checklist for structural identity, lighting logic, and color density across the set.
    • Automated Compositing merges the final high-resolution assets into a clean 2x2 grid for immediate use in production pipelines.

    How it works

    1. Define the Bible: Provide a scene description or a reference image to lock in fixed structures, era, and lighting.
    2. Execute the Chain: The agent generates the Front View first, then uses it as a reference for the Reverse, Oblique, and Top-Down views in order.
    3. Verify and Adjust: Review each perspective for spatial logic. If an object "drifts," the agent rolls back to the earliest point of error and re-generates.
    4. Final Assembly: Run the provided composition script to merge the four views into a high-res 2x2 grid.

    Frameworks & tools

    This skill uses image generation models with reference-image capabilities. It includes a scripts/compose_grid.py Python utility for asset stitching and requires high-resolution PNG output management.

    Why this beats prompting it yourself

    Standard prompting often treats angles as independent illustrations, leading to layouts that are physically impossible. This skill enforces a "3D space" logic that ensures the door on the left in your front view is the door on the right in your reverse view, saving hours of manual retouching.

    Use cases

    • Generating environment concepts for AI-generated short films and web series.
    • Creating consistent level designs and room layouts for indie game development.
    • Producing architectural visualization assets with interior/exterior consistency.
    • Building visual reference sheets for continuity in multi-scene narratives.

    Known limitations

    This skill is designed for static scene assets only and does not support character consistency or dynamic objects. It requires a model capable of image-to-image or strong reference-based generation.

    Does not support character design or dynamic objects. Requires image-to-image or reference-based generation capabilities. Best suited for static architectural or landscape environments.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    What's inside

    Frequently Asked Questions

    This skill uses a 'Chain-of-Perspective' workflow to ensure that landmarks, windows, and light sources remain in the same physical location across four different camera angles, preventing the 'hallucination' or shifting of objects common in standard prompting.

    The skill is designed for high-end image generation models that support image-to-image or reference-based workflows, along with a Python environment to run the included grid-stitching utility.

    You will receive the structured 'Scene Bible' framework, the sequential generation workflow for the four specific angles, and the `scripts/compose_grid.py` Python utility for automated asset merging.

    While this skill is optimized for static environments like room layouts and architectural sets, you can use the generated 4-angle grids as a fixed background reference for your character-based projects.

    Setup is straightforward; once you have your base environment model ready, you simply follow the 'Scene Bible' steps and use the provided Python script to compile your final 2x2 production-ready grids.

    Yes, buyers receive lifetime access to all future updates, including optimizations for new model versions and improvements to the spatial anchoring logic.

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