tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.{
"file_path": "/home/user/task/scenes/car.tscn"
}1 [gd_scene load_steps=1 format=3 uid="uid://codexcarsetupscene"] 2 3 [node name="Car" type="CharacterBody2D"] 4
ls /home/user/task && echo "---ASSETS---" && ls /home/user/task/assets 2>/dev/null && echo "---SCENES---" && ls /home/user/task/scenes 2>/dev/null
{
"description": "List project files and assets"
}assets project.godot scenes scripts tmp_check.gd.uid ---ASSETS--- spritesheet_vehicles.png spritesheet_vehicles.png.import ---SCENES--- car.tscn main.tscn test.tscn [stdout] assets project.godot scenes scripts tmp_check.gd.uid ---ASSETS--- spritesheet_vehicles.png spritesheet_vehicles.png.import ---SCENES--- car.tscn main.tscn test.tscn
{
"file_path": "/home/user/task/assets/spritesheet_vehicles.png.import"
}1 [remap]
2
3 importer="texture"
4 type="CompressedTexture2D"
5 uid="uid://4j3r3nq5e1ub"
6 path="res://.godot/imported/spritesheet_vehicles.png-e16a3162cc0edb8275366b9e44a79438.ctex"
7 metadata={
8 "vram_texture": false
9 }
10
11 [deps]
12
13 source_file="res://assets/spritesheet_vehicles.png"
14 dest_files=["res://.godot/imported/spritesheet_vehicles.png-e16a3162cc0edb8275366b9e44a79438.ctex"]
15
16 [params]
17
18 compress/mode=0
19 compress/high_quality=false
20 compress/lossy_quality=0.7
21 compress/hdr_compression=1
22 compress/normal_map=0
23 compress/channel_pack=0
24 mipmaps/generate=false
25 mipmaps/limit=-1
26 roughness/mode=0
27 roughness/src_normal=""
28 process/fix_alpha_border=true
29 process/premult_alpha=false
30 process/normal_map_invert_y=false
31 process/hdr_as_srgb=false
32 process/hdr_clamp_exposure=false
33 process/size_limit=0
34 detect_3d/compress_to=1
35
{
"file_path": "/home/user/task/assets/spritesheet_vehicles.png"
}{"type": "image", "source": {"type": "base64", "data": "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…[truncated]{
"file_path": "/home/user/task/scenes/main.tscn"
}1 [gd_scene load_steps=2 format=3 uid="uid://codexcarsetupmain"]
2
3 [ext_resource type="PackedScene" path="res://scenes/car.tscn" id="1_j7vsa"]
4
5 [node name="Main" type="Node2D"]
6
7 [node name="Car" parent="." instance=ExtResource("1_j7vsa")]
8
python3 -c "
from PIL import Image
im = Image.open('assets/spritesheet_vehicles.png')
print('size', im.size, 'mode', im.mode)
" 2>/dev/null || (command -v identify >/dev/null && identify assets/spritesheet_vehicles.png) || file assets/spritesheet_vehicles.png
{
"description": "Get spritesheet dimensions"
}Exit code 127 /bin/bash: line 9: file: command not found [error] tool reported failure
python3 - <<'EOF'
import struct
with open('assets/spritesheet_vehicles.png','rb') as f:
data=f.read(33)
# PNG IHDR
w,h=struct.unpack('>II', data[16:24])
print('width',w,'height',h)
EOF
{
"description": "Read PNG IHDR dimensions"
}width 1024 height 512 [stdout] width 1024 height 512
pip install pillow -q 2>/dev/null; python3 - <<'EOF'
from PIL import Image
im = Image.open('assets/spritesheet_vehicles.png').convert('RGBA')
W,H = im.size
px = im.load()
# find bounding box of top-left connected sprite via flood fill from (10,10)
from collections import deque
def alpha(x,y): return px[x,y][3]>10
# Find top-left car: scan first column region. The first car occupies roughly x in [0,~64]
# Let's flood fill the connected component containing the first opaque pixel near top-left.
start=None
for y in range(H):
for x in range(W):
if alpha(x,y):
start=(x,y); break
if start: break
print('first opaque', start)
seen=set([start])
q=deque([start])
minx=miny=10**9; maxx=maxy=-1
while q:
x,y=q.popleft()
minx=min(minx,x);maxx=max(maxx,x);miny=min(miny,y);maxy=max(maxy,y)
for dx in(-1,0,1):
for dy in(-1,0,1):
nx,ny=x+dx,y+dy
if 0<=nx<W and 0<=ny<H and (nx,ny) not in seen and alpha(nx,ny):
seen.add((nx,ny));q.append((nx,ny))
print('bbox x',minx,maxx,'y',miny,maxy,'w',maxx-minx+1,'h',maxy-miny+1)
EOF
{
"description": "Find top-left car sprite bounding box"
}Exit code 1 Traceback (most recent call last): File "<stdin>", line 1, in <module> ModuleNotFoundError: No module named 'PIL' [error] tool reported failure
pip install pillow 2>&1 | tail -2
{
"description": "Install pillow"
}/bin/bash: line 1: pip: command not found [stdout] /bin/bash: line 1: pip: command not found
python3 -m pip install pillow 2>&1 | tail -3; python3 -c "import sys; print(sys.version)"
{
"description": "Install pillow via python -m pip"
}/usr/bin/python3: No module named pip 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0] [stdout] /usr/bin/python3: No module named pip 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]
python3 - <<'EOF'
import zlib, struct
from collections import deque
f=open('assets/spritesheet_vehicles.png','rb').read()
assert f[:8]==b'\x89PNG\r\n\x1a\n'
i=8
W=H=bitd=ct=None
idat=b''
while i<len(f):
ln=struct.unpack('>I',f[i:i+4])[0]
typ=f[i+4:i+8]
data=f[i+8:i+8+ln]
if typ==b'IHDR':
W,H,bitd,ct=struct.unpack('>IIBB',data[:10])
elif typ==b'IDAT':
idat+=data
elif typ==b'IEND':
break
i+=12+ln
print('W',W,'H',H,'bitdepth',bitd,'colortype',ct)
raw=zlib.decompress(idat)
# color type 6 = RGBA, 8bit -> 4 bytes/px
ch=4 if ct==6 else (3 if ct==2 else None)
assert ch, ct
stride=W*ch
# unfilter
out=bytearray()
prev=bytearray(stride)
def paeth(a,b,c):
p=a+b-c;pa=abs(p-a);pb=abs(p-b);pc=abs(p-c)
return a if pa<=pb and pa<=pc else (b if pb<=pc else c)
pos=0
for y in range(H):
ft=raw[pos];pos+=1
line=bytearray(raw[pos:pos+stride]);pos+=stride
if ft==1:
for x in range(ch,stride): line[x]=(line[x]+line[x-ch])&255
elif ft==2:
for x in range(stride): line[x]=(line[x]+prev[x])&255
elif ft==3:
for x in range(stride):
a=line[x-ch] if x>=ch else 0
line[x]=(line[x]+((a+prev[x])>>1))&255
elif ft==4:
for x in range(stride):
a=line[x-ch] if x>=ch else 0
c=prev[x-ch] if x>=ch else 0
line[x]=(line[x]+paeth(a,prev[x],c))&255
out+=line
prev=line
# alpha accessor
def alpha(x,y):
if ch==4:
return out[(y*W+x)*4+3]>10
return True
# save for reuse
import pickle
pickle.dump((W,H,ch,bytes(out)),open('/tmp/img.pkl','wb'))
# top-left connected component
start=None
for y in range(H):
for x in range(W):
if alpha(x,y): start=(x,y);break
if start:break
print('first',start)
seen={start};q=deque([start]);minx=miny=10**9;maxx=maxy=-1
while q:
x,y=q.popleft()
minx=min(minx,x);maxx=max(maxx,x);miny=min(miny,y);maxy=max(maxy,y)
for dx in(-1,0,1):
for dy in(-1,0,1):
nx,ny=x+dx,y+dy
if 0<=nx<W and 0<=ny<H and (nx,ny) not in seen and alpha(nx,ny):
seen.add((nx,ny));q.append((nx,ny))
print('topleft bbox x[%d..%d] y[%d..%d] w=%d h=%d'%(minx,maxx,miny,maxy,maxx-minx+1,maxy-miny+1))
EOF
{
"description": "Decode PNG and find top-left car bbox"
}W 1024 H 512 bitdepth 8 colortype 6 first (22, 0) topleft bbox x[0..689] y[0..511] w=690 h=512 [stdout] W 1024 H 512 bitdepth 8 colortype 6 first (22, 0) topleft bbox x[0..689] y[0..511] w=690 h=512
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
# Examine first-row band. Find row separations first: scan each row's opaque count across x[0..700]
rows=[]
for y in range(H):
c=sum(1 for x in range(0,700) if a(x,y))
rows.append(c)
# print rows where count is low (gaps)
gaps=[y for y in range(H) if rows[y]<20]
print('low-density rows:',gaps)
EOF
{
"description": "Find horizontal row gaps"
}low-density rows: [] [stdout] low-density rows: []
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
# column density in top band y[0..118]
band=range(0,118)
dens=[sum(1 for y in band if a(x,y)) for x in range(0,720)]
# find local minima / low columns
for x in range(0,720):
if dens[x]<8:
print('lowcol',x,dens[x])
EOF
{
"description": "Column density in top band"
}lowcol 675 0 lowcol 676 0 lowcol 677 0 lowcol 678 0 lowcol 679 0 lowcol 680 0 lowcol 681 0 lowcol 682 0 lowcol 683 0 lowcol 684 0 lowcol 685 0 lowcol 686 0 lowcol 687 0 lowcol 688 0 lowcol 689 0 lowcol 690 0 lowcol 691 0 lowcol 692 0 lowcol 693 0 lowcol 694 0 lowcol 695 0 lowcol 696 0 lowcol 697 0 lowcol 698 0 lowcol 699 0 lowcol 700 0 lowcol 701 0 lowcol 702 0 lowcol 703 0 lowcol 704 0 lowcol 705 0 lowcol 706 0 lowcol 707 0 lowcol 708 0 lowcol 709 0 lowcol 710 0 lowcol 711 0 lowcol 712 0 lowcol 713 0 lowcol 714 0 lowcol 715 0 lowcol 716 0 lowcol 717 0 lowcol 718 0 lowcol 719 0 [stdout] lowcol 675 0 lowcol 676 0 lowcol 677 0 lowcol 678 0 lowcol 679 0 lowcol 680 0 lowcol 681 0 lowcol 682 0 lowcol 683 0 lowcol 684 0 lowcol 685 0 lowcol 686 0 lowcol 687 0 lowcol 688 0 lowcol 689 0 lowcol 690 0 lowcol 691 0 lowcol 692 0 lowcol 693 0 lowcol 694 0 lowcol 695 0 lowcol 696 0 lowcol 697 0 lowcol 698 0 lowcol 699 0 lowcol 700 0 lowcol 701 0 lowcol 702 0 lowcol 703 0 lowcol 704 0 lowcol 705 0 lowcol 706 0 lowcol 707 0 lowcol 708 0 lowcol 709 0 lowcol 710 0 lowcol 711 0 lowcol 712 0 lowcol 713 0 lowcol 714 0 lowcol 715 0 lowcol 716 0 lowcol 717 0 lowcol 718 0 lowcol 719 0
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
band=range(0,118)
dens=[sum(1 for y in band if a(x,y)) for x in range(0,680)]
# print as compact rows of 10
for x in range(0,680,1):
pass
# find local minima below threshold relative to neighbors
mins=[]
for x in range(2,678):
if dens[x]<=dens[x-1] and dens[x]<=dens[x+1] and dens[x]<60:
mins.append((x,dens[x]))
print('candidate boundary cols (low density):')
print(mins)
EOF
{
"description": "Find local minima boundary columns"
}candidate boundary cols (low density): [(70, 34), (72, 32), (73, 32), (143, 34), (145, 30), (146, 30), (216, 32), (217, 32), (219, 37), (288, 24), (289, 24), (291, 37), (360, 24), (361, 24), (506, 51), (507, 51), (546, 32), (548, 31), (549, 31), (589, 57), (591, 54), (673, 25), (675, 0), (676, 0), (677, 0)] [stdout] candidate boundary cols (low density): [(70, 34), (72, 32), (73, 32), (143, 34), (145, 30), (146, 30), (216, 32), (217, 32), (219, 37), (288, 24), (289, 24), (291, 37), (360, 24), (361, 24), (506, 51), (507, 51), (546, 32), (548, 31), (549, 31), (589, 57), (591, 54), (673, 25), (675, 0), (676, 0), (677, 0)]
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
# vertical density in left column band x[0..72]
band=range(0,72)
dens=[sum(1 for x in band if a(x,y)) for y in range(0,H)]
mins=[]
for y in range(2,H-2):
if dens[y]<=dens[y-1] and dens[y]<=dens[y+1] and dens[y]<40:
mins.append((y,dens[y]))
print('row boundary candidates:', mins[:30])
EOF
{
"description": "Find row boundaries in left column"
}row boundary candidates: [(130, 26), (131, 26), (133, 27), (250, 27), (251, 27), (381, 26), (382, 26), (384, 27), (501, 0), (502, 0), (503, 0), (504, 0), (505, 0), (506, 0), (507, 0), (508, 0), (509, 0)] [stdout] row boundary candidates: [(130, 26), (131, 26), (133, 27), (250, 27), (251, 27), (381, 26), (382, 26), (384, 27), (501, 0), (502, 0), (503, 0), (504, 0), (505, 0), (506, 0), (507, 0), (508, 0), (509, 0)]
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
# tight bbox within cell x[0..71] y[0..130]
minx=miny=10**9;maxx=maxy=-1
for y in range(0,131):
for x in range(0,72):
if a(x,y):
minx=min(minx,x);maxx=max(maxx,x);miny=min(miny,y);maxy=max(maxy,y)
print('car1 tight bbox x[%d..%d] y[%d..%d] w=%d h=%d'%(minx,maxx,miny,maxy,maxx-minx+1,maxy-miny+1))
EOF
{
"description": "Tight bbox of first car within its cell"
}car1 tight bbox x[0..71] y[0..130] w=72 h=131 [stdout] car1 tight bbox x[0..71] y[0..130] w=72 h=131
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
for y in [40,60,90]:
row=''.join('#' if a(x,y) else '.' for x in range(0,150))
print(y, row)
EOF
{
"description": "Visualize opaque spans across first two cars"
}40 .#####################################################################....#####################################################################....### 60 ..###################################################################......###################################################################......## 90 .####################################################################.....####################################################################.....### [stdout] 40 .#####################################################################....#####################################################################....### 60 ..###################################################################......###################################################################......## 90 .####################################################################.....####################################################################.....###
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
col=''.join('#' if a(35,y) else '.' for y in range(0,260))
print(col)
# spans
y=0;spans=[]
inside=False
for yy in range(0,260):
v=a(35,yy)
if v and not inside: s=yy;inside=True
if not v and inside: spans.append((s,yy-1));inside=False
if inside: spans.append((s,259))
print('vertical opaque spans at x=35:',spans)
EOF
{
"description": "Vertical span of first car center column"
}#################################################################################################################################################################################################################################################################### vertical opaque spans at x=35: [(0, 259)] [stdout] #################################################################################################################################################################################################################################################################### vertical opaque spans at x=35: [(0, 259)]
python3 - <<'EOF'
import pickle
W,H,ch,out=pickle.load(open('/tmp/img.pkl','rb'))
def a(x,y): return out[(y*W+x)*4+3]>10
for y in range(118,140):
row=''.join('#' if a(x,y) else '.' for x in range(0,72))
print(y, row)
EOF
{
"description": "Inspect rows between car1 and car2"
}118 ######################################################################## 119 .####################################################################### 120 .#####################################################################.. 121 .#####################################################################.. 122 ..####################################################################.. 123 ..###################################################################... 124 ...#################################################################.... 125 ....################################################################.... 126 .....#############################################################...... 127 ......###########################################################....... 128 ........#######################################################......... 129 .............#############################################.............. 130 .......................##########################....................... 131 .......................##########################....................... 132 ......................###########################....................... 133 ......................###########################....................... 134 ............###############################################............. 135 .........#####################################################.......... 136 .......#########################################################........ 137 ......###########################################################....... 138 .....#############################################################...... 139 ....###############################################################..... [stdout] 118 ######################################################################## 119 .####################################################################### 120 .#####################################################################.. 121 .#####################################################################.. 122 ..####################################################################.. 123 ..###################################################################... 124 ...#################################################################.... 125 ....################################################################.... 126 .....#############################################################...... 127 ......###########################################################....... 128 ........#######################################################......... 129 .............#############################################.............. 130 .......................##########################....................... 131 .......................##########################....................... 132 ......................###########################....................... 133 ......................###########################....................... 134 ............###############################################............. 135 .........#####################################################.......... 136 .......#########################################################........ 137 ......###########################################################....... 138 .....#############################################################...... 139 ....###############################################################.....
{
"file_path": "/home/user/task/scenes/car.tscn",
"content": "[gd_scene load_steps=3 format=3 uid=\"uid://codexcarsetupscene\"]\n\n[ext_resource type=\"Texture2D\" uid=\"uid://4j3r3nq5e1ub\" path=\"res://assets/spritesheet_vehicles.png\" id=\"1_car\"]\n\n[sub_resource type=\"CapsuleShape2D\" id=\"CapsuleShape2D_car\"]\nradius = 35.0\nheight = 130.0\n\n[node name=\"Car\" type=\"CharacterBody2D\"]\n\n[node name=\"Sprite2D\" type=\"Sprite2D\" parent=\".\"]\nrotation = 1.5707963267948966\ntexture = ExtResource(\"1_car\")\nregion_enabled = true\nregion_rect = Rect2(0, 0, 70, 130)\n\n[node name=\"CollisionShape2D\" type=\"CollisionShape2D\" parent=\".\"]\nrotation = 1.5707963267948966\nshape = SubResource(\"CapsuleShape2D_car\")\n\n[node name=\"Camera2D\" type=\"Camera2D\" parent=\".\"]\n"
}The file /home/user/task/scenes/car.tscn has been updated successfully. (file state is current in your context , no need to Read it back)
No file changes, the agent left the workspace untouched.
Godot Engine v4.4.stable.official.4c311cbee - https://godotengine.org
WARNING: Started the engine as `root`/superuser. This is a security risk, and subsystems like audio may not work correctly.
Set the environment variable `GODOT_SILENCE_ROOT_WARNING` to 1 to silence this warning.
at: setup2 (main/main.cpp:3181)
VALIDATION_FAILED: Wrong car selected , find the correct forward-facing vertical car body in the spritesheetReproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_7d6a2170af56475b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_7d6a2170af56475b · verifier authoritative; classifier explanatory.