dd-l1-vienna-gpkg-manifest

Status: done

Score: 1.000

Duration: 1:20

Cost: 0.16¢

Model: deepseek/deepseek-v4-flash

Map (reference ← swipe → agent)

0:00

Inherited a GPKG (vienna_planning) from a colleague who retired last week. Before I commit to scripting against it I need a one-page manifest so I can see what's in there and decide which layers actually feed next month's councillor briefing. Catalog the contents and give me one record per layer. Output manifest.json — a JSON list of these records. Each record in the list must be a JSON object with exactly five keys: `layer_name` as a string, `crs` as a string in `EPSG:NNNN` form, `geometry_type` as a string matching the layer's native type (e.g. `Point`, `LineString`, `Polygon`, `MultiPolygon`), `feature_count` as an integer, and `bbox` as a list of four numbers `[xmin, ymin, xmax, ymax]` in the layer's native CRS.

0:00

System info

{
  "adapter": "openrouter",
  "model": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}

0:05

Let me start by exploring the GPKG file to understand its contents.

0:05

Bash ls -la /work/vienna_planning* 2>/dev/null || find /work -name "vienna_planning*"...

-rw-r--r-- 1 agent agent 524288 Jun 16 00:49 /work/vienna_planning.gpkg

0:19

Bash cd /work && python3 -c "

cd /work && python3 -c "
import geopandas as gpd
layers = gpd.list_layers('vienna_planning.gpkg')
print(layers)
print('---')
print(layers.columns.tolist())
"

                name geometry_type
0          districts       Polygon
1              parks  MultiPolygon
2        waterbodies       Polygon
3            schools         Point
4              cafes         Point
5       supermarkets         Point
6  cycleway_segments    LineString
---
['name', 'geometry_type']

0:23

Bash cd /work && python3 -c "

cd /work && python3 -c "
import geopandas as gpd
layers = gpd.list_layers('vienna_planning.gpkg')
for name in layers['name']:
    gdf = gpd.read_file('vienna_planning.gpkg', layer=name)
    print(f'Layer: {name}')
    print(f'  CRS: {gdf.crs}')
    print(f'  Geometry type: {gdf.geometry.type.unique()}')
    print(f'  Feature count: {len(gdf)}')
    print(f'  Bounds: {gdf.total_bounds.tolist()}')
    print()
"

Layer: districts
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
  Feature count: 22
  Bounds: [623867.3888224852, 481236.5836125494, 626371.3079994549, 484403.7315526943]

Layer: parks
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
  Feature count: 119
  Bounds: [623851.990195953, 481200.6674012489, 626620.9438865517, 485539.3479671855]

Layer: waterbodies
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
  Feature count: 33
  Bounds: [624288.8521056126, 481236.66320640914, 626747.8611584986, 484132.0212685799]

Layer: schools
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['Point']
Length: 1, dtype: str
  Feature count: 40
  Bounds: [624000.002417057, 481214.73871311604, 626259.5421406503, 484178.87865010765]

Layer: cafes
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['Point']
Length: 1, dtype: str
  Feature count: 392
  Bounds: [623796.529863561, 481107.2540129812, 626399.5618121764, 484498.9209005649]

Layer: supermarkets
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['Point']
Length: 1, dtype: str
  Feature count: 87
  Bounds: [623794.0549950565, 481148.38703723426, 626392.6164786755, 484489.91954147833]

Layer: cycleway_segments
  CRS: EPSG:31287
  Geometry type: <ArrowStringArray>
['LineString']
Length: 1, dtype: str
  Feature count: 271
  Bounds: [623787.4932131292, 481134.9325878215, 626619.1631922598, 485245.6624853243]

0:42

Now I have all the data I need. Let me construct the manifest.json file. Each record needs exactly 5 keys:
- layer_name (string)
- crs (string in EPSG:NNNN form)
- geometry_type (string matching native type)
- feature_count (integer)
- bbox (list of 4 numbers [xmin, ymin, xmax, ymax] in native CRS)

Let me write the script to produce this JSON.

0:42

Write /work/solve.py

content · 37 lines · py

import json
import geopandas as gpd

gpkg_path = '/work/vienna_planning.gpkg'

layers_info = gpd.list_layers(gpkg_path)

records = []
for name in layers_info['name']:
    gdf = gpd.read_file(gpkg_path, layer=name)
    
    # CRS in EPSG:NNNN form
    crs = gdf.crs.to_epsg()
    crs_str = f'EPSG:{crs}' if crs else str(gdf.crs)
    
    # Native geometry type
    geometry_type = layers_info.loc[layers_info['name'] == name, 'geometry_type'].values[0]
    
    # Feature count
    feature_count = len(gdf)
    
    # Bounding box [xmin, ymin, xmax, ymax]
    bbox = gdf.total_bounds.tolist()
    
    records.append({
        'layer_name': name,
        'crs': crs_str,
        'geometry_type': geometry_type,
        'feature_count': feature_count,
        'bbox': bbox
    })

with open('/work/manifest.json', 'w') as f:
    json.dump(records, f, indent=2)

print('manifest.json written successfully')
print(json.dumps(records, indent=2))

Wrote /work/solve.py (940 chars).

0:56

Let me run the script.

0:56

Bash cd /work && python solve.py

manifest.json written successfully
[
  {
    "layer_name": "districts",
    "crs": "EPSG:31287",
    "geometry_type": "Polygon",
    "feature_count": 22,
    "bbox": [
      623867.3888224852,
      481236.5836125494,
      626371.3079994549,
      484403.7315526943
    ]
  },
  {
    "layer_name": "parks",
    "crs": "EPSG:31287",
    "geometry_type": "MultiPolygon",
    "feature_count": 119,
    "bbox": [
      623851.990195953,
      481200.6674012489,
      626620.9438865517,
      485539.3479671855
    ]
  },
  {
    "layer_name": "waterbodies",
    "crs": "EPSG:31287",
    "geometry_type": "Polygon",
    "feature_count": 33,
    "bbox": [
      624288.8521056126,
      481236.66320640914,
      626747.8611584986,
      484132.0212685799
    ]
  },
  {
    "layer_name": "schools",
    "crs": "EPSG:31287",
    "geometry_type": "Point",
    "feature_count": 40,
    "bbox": [
      624000.002417057,
      481214.73871311604,
      626259.5421406503,
      484178.87865010765
    ]
  },
  {
    "layer_name": "cafes",
    "crs": "EPSG:31287",
    "geometry_type": "Point",
    "feature_count": 392,
    "bbox": [
      623796.529863561,
      481107.2540129812,
      626399.5618121764,
      484498.9209005649
    ]
  },
  {
    "layer_name": "supermarkets",
    "crs": "EPSG:31287",
    "geometry_type": "Point",
    "feature_count": 87,
    "bbox": [
      623794.0549950565,
      481148.38703723426,
      626392.6164786755,
      484489.91954147833
    ]
  },
  {
    "layer_name": "cycleway_segments",
    "crs": "EPSG:31287",
    "geometry_type": "LineString",
    "feature_count": 271,
    "bbox": [
      623787.4932131292,
      481134.9325878215,
      626619.1631922598,
      485245.6624853243
    ]
  }
]

1:12

Read /work/manifest.json

     1	[
     2	  {
     3	    "layer_name": "districts",
     4	    "crs": "EPSG:31287",
     5	    "geometry_type": "Polygon",
     6	    "feature_count": 22,
     7	    "bbox": [
     8	      623867.3888224852,
     9	      481236.5836125494,
    10	      626371.3079994549,
    11	      484403.7315526943
    12	    ]
    13	  },
    14	  {
    15	    "layer_name": "parks",
    16	    "crs": "EPSG:31287",
    17	    "geometry_type": "MultiPolygon",
    18	    "feature_count": 119,
    19	    "bbox": [
    20	      623851.990195953,
    21	      481200.6674012489,
    22	      626620.9438865517,
    23	      485539.3479671855
    24	    ]
    25	  },
    26	  {
    27	    "layer_name": "waterbodies",
    28	    "crs": "EPSG:31287",
    29	    "geometry_type": "Polygon",
    30	    "feature_count": 33,
    31	    "bbox": [
    32	      624288.8521056126,
    33	      481236.66320640914,
    34	      626747.8611584986,
    35	      484132.0212685799
    36	    ]
    37	  },
    38	  {
    39	    "layer_name": "schools",
    40	    "crs": "EPSG:31287",
    41	    "geometry_type": "Point",
    42	    "feature_count": 40,
    43	    "bbox": [
    44	      624000.002417057,
    45	      481214.73871311604,
    46	      626259.5421406503,
    47	      484178.87865010765
    48	    ]
    49	  },
    50	  {
    51	    "layer_name": "cafes",
    52	    "crs": "EPSG:31287",
    53	    "geometry_type": "Point",
    54	    "feature_count": 392,
    55	    "bbox": [
    56	      623796.529863561,
    57	      481107.2540129812,
    58	      626399.5618121764,
    59	      484498.9209005649
    60	    ]
    61	  },
    62	  {
    63	    "layer_name": "supermarkets",
    64	    "crs": "EPSG:31287",
    65	    "geometry_type": "Point",
    66	    "feature_count": 87,
    67	    "bbox": [
    68	      623794.0549950565,
    69	      481148.38703723426,
    70	      626392.6164786755,
    71	      484489.91954147833
    72	    ]
    73	  },
    74	  {
    75	    "layer_name": "cycleway_segments",
    76	    "crs": "EPSG:31287",
    77	    "geometry_type": "LineString",
    78	    "feature_count": 271,
    79	    "bbox": [
    80	      623787.4932131292,
    81	      481134.9325878215,
    82	      626619.1631922598,
    83	      485245.6624853243
    84	    ]
    85	  }
    86	]

1:19

score: 1.000

Gates

✓ format_schema_valid

Subchecks

✓ layers_complete (×4) submitted ['cafes', 'cycleway_segments', 'districts', 'parks', 'schools', 'supermarkets', 'waterbodies'] vs expected ['cafes', 'cycleway_segments', 'districts', 'parks', 'schools', 'supermarkets', 'waterbodies']
✓ cafes_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ cafes_geom_type_correct submitted 'Point' vs expected 'Point'
✓ cafes_count_correct submitted 392 vs expected 392
✓ cafes_bbox_correct submitted [623796.529863561, 481107.2540129812, 626399.5618121764, 484498.9209005649] vs expected [623796.53, 481107.25, 626399.56, 484498.92] (max |Δ|=0.004, eps=1.0)
✓ cycleway_segments_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ cycleway_segments_geom_type_correct submitted 'LineString' vs expected 'LineString'
✓ cycleway_segments_count_correct submitted 271 vs expected 271
✓ cycleway_segments_bbox_correct submitted [623787.4932131292, 481134.9325878215, 626619.1631922598, 485245.6624853243] vs expected [623787.49, 481134.93, 626619.16, 485245.66] (max |Δ|=0.003, eps=1.0)
✓ districts_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ districts_geom_type_correct submitted 'Polygon' vs expected 'Polygon'
✓ districts_count_correct submitted 22 vs expected 22
✓ districts_bbox_correct submitted [623867.3888224852, 481236.5836125494, 626371.3079994549, 484403.7315526943] vs expected [623867.39, 481236.58, 626371.31, 484403.73] (max |Δ|=0.004, eps=1.0)
✓ parks_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ parks_geom_type_correct submitted 'MultiPolygon' vs expected 'MultiPolygon'
✓ parks_count_correct submitted 119 vs expected 119
✓ parks_bbox_correct submitted [623851.990195953, 481200.6674012489, 626620.9438865517, 485539.3479671855] vs expected [623851.99, 481200.67, 626620.94, 485539.35] (max |Δ|=0.004, eps=1.0)
✓ schools_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ schools_geom_type_correct submitted 'Point' vs expected 'Point'
✓ schools_count_correct submitted 40 vs expected 40
✓ schools_bbox_correct submitted [624000.002417057, 481214.73871311604, 626259.5421406503, 484178.87865010765] vs expected [624000.0, 481214.74, 626259.54, 484178.88] (max |Δ|=0.002, eps=1.0)
✓ supermarkets_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ supermarkets_geom_type_correct submitted 'Point' vs expected 'Point'
✓ supermarkets_count_correct submitted 87 vs expected 87
✓ supermarkets_bbox_correct submitted [623794.0549950565, 481148.38703723426, 626392.6164786755, 484489.91954147833] vs expected [623794.05, 481148.39, 626392.62, 484489.92] (max |Δ|=0.005, eps=1.0)
✓ waterbodies_crs_correct submitted 'EPSG:31287' vs expected 'EPSG:31287'
✓ waterbodies_geom_type_correct submitted 'Polygon' vs expected 'Polygon'
✓ waterbodies_count_correct submitted 33 vs expected 33
✓ waterbodies_bbox_correct submitted [624288.8521056126, 481236.66320640914, 626747.8611584986, 484132.0212685799] vs expected [624288.85, 481236.66, 626747.86, 484132.02] (max |Δ|=0.003, eps=1.0)

{
  "created_at": "2026-06-16T00:49:16.205916Z",
  "error": null,
  "files": [
    "manifest.json",
    "solve.py",
    "vienna_planning.gpkg"
  ],
  "last_activity_at": "2026-06-16T00:50:35.635613Z",
  "session_id": "24aa29334d754d2d8edd575a5397bd48",
  "status": "idle",
  "task_version": 1,
  "usage": {
    "agent_version": "0.3.0",
    "duration_s": 79.25090557499789,
    "estimated_cost_usd": 0.001563976,
    "model": "deepseek/deepseek-v4-flash"
  }
}

dd-l1-vienna-gpkg-manifest Rerun

Map (reference ← swipe → agent)

Gates

Subchecks

dd-l1-vienna-gpkg-manifest