| name | generate-scene |
| description | Generate a complete 3D interior scene from a text prompt through the 9-gate recipe — plan, retrieve procedurally-similar traces, audit assets, author a phase-gated IDSDL program, build phase-by-phase with verification at every gate, judge against the plan, write back what you learned. Use when asked to "make/build a <room type>" end to end. |
Generate a scene from a text prompt — the nine gates
Scenes are built through a fixed recipe distilled from the ~26 worked examples:
verify EARLY and CHEAP at every step, never write the whole scene and hope. Three
ways to run the same recipe:
Requires OPENAI_API_KEY, the datasets under IDSDL/datasets/, and Blender via
SceneProgExec. Builds run under the minimal render policy: the 4-wall room strip
in tmp/<run>/vlm_views/ is your critique image.
The gates
1. PLAN — get the design target
python -m planner_core "<prompt>" --out tmp/<run>/plan
Open plan.png, read skill.txt: anchors / secondary items / wall+decor /
palette / lighting mood. The plan image is what gate 8 judges against — keep it open.
2. RETRIEVE — reason over the knowledge catalog
python -m retriever_core "<prompt>" --plan tmp/<run>/plan/skill.txt --out tmp/<run>/ctx
Read bundle.md IN FULL: recipes matched by layout pattern (not category name),
their polished programs, and the atomic lessons selected for this scene.
Via MCP: retrieve_context.
3. AUDIT ASSETS — eyeball before placements
Batch-resolve your shopping list (skills/workflow/asset_selection.md) and —
non-negotiable — eyeball the preview of every mesh you pin (caption≠mesh; the
#1 late-caught failure class). Verify the category's IDENTITY props exist; if a
key fixture is missing, mass the product instead of shipping an empty fixture.
4. AUTHOR — write the phase-gated program, lint it
Follow the matched recipe's skeleton, gated on IDSDL/phases.py (canonical form:
skills/examples/coffee_shop_v1.py):
from IDSDL.phases import current_phase
PHASE = current_phase()
...
if PHASE >= 2: station.place_on_top([...])
if PHASE >= 3: room.add_lighting(...)
Later phases only ADD — never move phase-1 geometry. Hard rules the bundle
enforces: room size is a CONSEQUENCE (few slots, modest hero widths, never
modulate_scale > 1.0 to dodge overlaps); product at viewing height; rigid
composed stations; N * unit duplication; wall-hung = flat only (<0.25 m);
lighting density 0.01–0.02 small room. Then:
python workbench.py lint <program>.py
5. BUILD PHASE 1 — verify the floor layout (~1 min)
python workbench.py run <program>.py --phase 1
Strip check: room size right? overlaps? clearances? orientation? The report's
[Lint]/WARNING lines (floaters, starfield, overlap, over-height) must be
clean. Fix and rebuild phase 1 until they are — this loop is cheap, use it.
6. BUILD PHASE 2 — dress the surfaces
python workbench.py run <program>.py --phase 2
Product at viewing height, stocked shelves, items sized to their surfaces,
nothing floating.
7. BUILD PHASE 3 — full scene + converge
python workbench.py run <program>.py
Apply the skills/workflow/vlm_feedback.md policy: render is the arbiter; ONE
decisive change; rotation flags are weak alarms; converge, don't chase. If
exactly one object floats while neighbours rest, interrogate the exported blend
(bottom = loc_z - dims_z/2) — off-center mesh origin means SWAP the mesh.
8. JUDGE — against the plan, then the vibe layer
Compare the strip to plan.png: does it instantly read as the category? Are the
plan's identity elements present? Then add the vibe layer (see
skills/examples/coffee_shop.md): stocked shelves, menu/signage, one warm accent
seat, warm envelope, greenery. The VLM loop converging is necessary, not
sufficient — gut-check legibility yourself.
9. WRITE BACK — grow the knowledge base
Distill skills/examples/<name>.md (+ the program as <name>_v1.py beside it),
add its row to skills/examples/README.md keeping the table format (the
retriever parses it), and append concrete feedback→action entries to
skills/workflow/vlm_feedback.md's decision log.
Pointers
- DSL API:
../dsl_reference.md · phases: IDSDL/phases.py + ../workflow/coarse_to_fine.md
- Deterministic checks:
IDSDL/lints.py (compile lints + lint_program)
- Worked reference end-to-end:
../examples/coffee_shop.md
- Pipeline internals (lint/phase gates, critic/judge policies):
generator_core/pipeline.py
- Guided-flow internals (gate validation, provenance):
IDSDL/service/flow.py
