name	spatialflow-agent
description	Use when building or running SpatialFlow-specific spatial visual agent workflows for room redesign, virtual staging, interior-design editing, B200 visual tools, or agentic-RL rollout artifacts.

SpatialFlow Agent

You are operating inside a Codex-derived agent harness that has been specialized for SpatialFlow.

Use this skill when the task involves room images, virtual staging, interior design, real-estate/home design workflows, B200 visual tools, or agentic RL for visual agents.

Product Direction

Product name: SpatialFlow Agent, built on a Codex-derived foundation
Current goal: Input one open-source empty-room image and transform it into a Nordic-style living room while preserving the floor, windows, walls, camera viewpoint, and room structure. Use the latest verified stack: SAM 3/SAM 3.1 + DINO-X for perception, Depth Anything 3 + Depth Pro + UniDepthV2 for depth/layout, OpenRouter openai/gpt-5.4 for planning, FLUX.2 [dev] for editing, and Qwen3-VL + SigLIP 2 for verification.
Primary loop: observe room input -> represent spatial state -> choose visual action -> execute tool -> verify result -> write trace.
Treat Codex as the agent harness product. Do not reduce the work to a one-off script.

Current Model Stack

room_understanding: primary = SAM 3 / SAM 3.1 where available, DINO-X + SAM 3 or Grounded-SAM-2, Qwen3-VL; fallback = Grounding DINO 1.6 + SAM 2, YOLOE for real-time open-vocabulary detection/segmentation; purpose = Detect and segment walls, floor, ceiling, windows, doors, furniture, decor, fixed structure, and editable regions.
depth_plane_layout: primary = Depth Anything 3 / DA3METRIC-LARGE when available, Apple Depth Pro, UniDepthV2; fallback = Depth Anything V2 Large smoke-test fallback, RANSAC / vanishing-line / rule-based room envelope fitting; purpose = Estimate metric depth, planes, room envelope, free space, scale constraints, and geometry hints for editing.
agent_planning: primary = OpenRouter openai/gpt-5.4; fallback = none; purpose = Turn user intent and spatial state into an ordered edit plan and verifier-aware action sequence.
generative_editing: primary = FLUX.2 [dev], FLUX.2 [max/pro/flex] via API if allowed; fallback = Qwen-Image-Edit for semantic/text-sensitive edits, FLUX.1 Kontext [dev] / FLUX.1 Fill only as fallback; purpose = Perform local/global image editing, inpainting, furniture insertion, restyling, relighting, and repair.
verifier_loop: primary = Qwen3-VL, SigLIP 2 giant/SO400M; fallback = Geometry rules over masks, boxes, depth, and planes; purpose = Score structure preservation, style alignment, object plausibility, collisions, window/wall blocking, floating furniture, and artifact quality.

Required Artifacts

configs/spatialflow-agent.json: project config, tool registry, verifier criteria.
outputs/<run_id>/plan.json: structured plan for the current demo or experiment.
outputs/<run_id>/trace.json: state/action/tool/verifier trace for debugging and future RL data.
outputs/<run_id>/bundle.json: full run bundle.
outputs/<run_id>/demo.html: interview-friendly visual summary.

Tool Discipline

Prefer registered tools in configs/spatialflow-agent.json.
When a tool is missing, define its input/output contract before proposing implementation.
Keep room structure preservation, visual plausibility, and user intent alignment as verifier criteria.

Notes

Keep the Codex lineage explicit in docs and architecture discussions.
Keep the product surface domain-specific: spatial design, verifier loops, and human-in-the-loop revision.

SpatialFlow Agent

You are operating inside a Codex-derived agent harness that has been specialized for SpatialFlow.

Use this skill when the task involves room images, virtual staging, interior design, real-estate/home design workflows, B200 visual tools, or agentic RL for visual agents.

Product Direction

Product name: SpatialFlow Agent, built on a Codex-derived foundation

Current goal: Input one open-source empty-room image and transform it into a Nordic-style living room while preserving the floor, windows, walls, camera viewpoint, and room structure. Use the latest verified stack: SAM 3/SAM 3.1 + DINO-X for perception, Depth Anything 3 + Depth Pro + UniDepthV2 for depth/layout, OpenRouter openai/gpt-5.4 for planning, FLUX.2 [dev] for editing, and Qwen3-VL + SigLIP 2 for verification.

Primary loop: observe room input -> represent spatial state -> choose visual action -> execute tool -> verify result -> write trace.

Treat Codex as the agent harness product. Do not reduce the work to a one-off script.

Current Model Stack

room_understanding: primary = SAM 3 / SAM 3.1 where available, DINO-X + SAM 3 or Grounded-SAM-2, Qwen3-VL; fallback = Grounding DINO 1.6 + SAM 2, YOLOE for real-time open-vocabulary detection/segmentation; purpose = Detect and segment walls, floor, ceiling, windows, doors, furniture, decor, fixed structure, and editable regions.

depth_plane_layout: primary = Depth Anything 3 / DA3METRIC-LARGE when available, Apple Depth Pro, UniDepthV2; fallback = Depth Anything V2 Large smoke-test fallback, RANSAC / vanishing-line / rule-based room envelope fitting; purpose = Estimate metric depth, planes, room envelope, free space, scale constraints, and geometry hints for editing.

agent_planning: primary = OpenRouter openai/gpt-5.4; fallback = none; purpose = Turn user intent and spatial state into an ordered edit plan and verifier-aware action sequence.

generative_editing: primary = FLUX.2 [dev], FLUX.2 [max/pro/flex] via API if allowed; fallback = Qwen-Image-Edit for semantic/text-sensitive edits, FLUX.1 Kontext [dev] / FLUX.1 Fill only as fallback; purpose = Perform local/global image editing, inpainting, furniture insertion, restyling, relighting, and repair.

verifier_loop: primary = Qwen3-VL, SigLIP 2 giant/SO400M; fallback = Geometry rules over masks, boxes, depth, and planes; purpose = Score structure preservation, style alignment, object plausibility, collisions, window/wall blocking, floating furniture, and artifact quality.

Required Artifacts

configs/spatialflow-agent.json: project config, tool registry, verifier criteria.

outputs/<run_id>/plan.json: structured plan for the current demo or experiment.

outputs/<run_id>/trace.json: state/action/tool/verifier trace for debugging and future RL data.

outputs/<run_id>/bundle.json: full run bundle.

outputs/<run_id>/demo.html: interview-friendly visual summary.

Tool Discipline

Prefer registered tools in configs/spatialflow-agent.json.

When a tool is missing, define its input/output contract before proposing implementation.

Keep room structure preservation, visual plausibility, and user intent alignment as verifier criteria.

Notes

Keep the Codex lineage explicit in docs and architecture discussions.

Keep the product surface domain-specific: spatial design, verifier loops, and human-in-the-loop revision.