dd-l1-vienna-gpkg-manifest

Status: done

Score: 100

Duration: 1:02

Cost: 0.18¢

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"
}

0:06

Let me start by exploring the GPKG file to understand its contents, then write a script to catalog everything.

0:06

Bash ls -lh /work/vienna_planning.gpkg 2>/dev/null || ls -lh /work/

-rw-r--r-- 1 agent agent 512K Jun 18 13:57 /work/vienna_planning.gpkg

0:10

Let me first explore the GPKG to see what layers it contains.

0:10

Bash python3 -c "

python3 -c "
import geopandas as gpd
# List layers
layers = gpd.list_layers('/work/vienna_planning.gpkg')
print(layers)
"

                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

0:19

Good, I can see there are 7 layers. Let me gather info for each one: CRS, feature count, and bounding box.

0:19

Bash python3 -c "

python3 -c "
import geopandas as gpd
import json

layers = ['districts', 'parks', 'waterbodies', 'schools', 'cafes', 'supermarkets', 'cycleway_segments']
results = []

for lyr in layers:
    gdf = gpd.read_file('/work/vienna_planning.gpkg', layer=lyr)
    crs = gdf.crs
    # Convert to EPSG:XXXX string
    if crs is None:
        crs_str = 'None'
    else:
        crs_str = crs.to_authority()
        if crs_str:
            crs_str = f'{crs_str[0]}:{crs_str[1]}'
        else:
            crs_str = str(crs)
    
    geom_type = gdf.geometry.geom_type.iloc[0]
    # For Multi* types, geopandas might give 'MultiPolygon' etc. — keep as is
    feature_count = len(gdf)
    
    # Total bounds across all features
    bbox = [float(gdf.total_bounds[0]), float(gdf.total_bounds[1]),
            float(gdf.total_bounds[2]), float(gdf.total_bounds[3])]
    
    results.append({
        'layer_name': lyr,
        'crs': crs_str,
        'geometry_type': geom_type,
        'feature_count': feature_count,
        'bbox': bbox
    })

print(json.dumps(results, indent=2))
"

[
  {
    "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
    ]
  }
]

0:27

The results look good. All layers share EPSG:31287 (MGI / Austria GK Central). The bbox values are in metric coordinates (6-digit range, as expected for a local Austrian projection). Let me write this out as manifest.json.

0:27

Write /work/manifest.json

content · 51 lines · json

[
  {
    "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]
  }
]

Wrote /work/manifest.json (1489 chars).

0:41

Let me verify the manifest.json is valid JSON and the bbox values look correct by re-reading the file and also sanity-checking the coordinates.

The CRS is EPSG:31287 (MGI / Austria GK Central). For Vienna, coordinates in this CRS should be around:
- Easting: ~620,000 to ~627,000
- Northing: ~480,000 to ~485,000

These look reasonable for Vienna in EPSG:31287. Let me double-check the bbox for cafes - actually the min y from the output was 481107.2540129812 but in my Python output it was 481214.73871311604... Wait, let me check.

Looking back at the Python output:
cafes bbox was: [623796.529863561, 481107.2540129812, 626399.5618121764, 484498.9209005649]

But wait, in the Python script output I see cafes:
"bbox": [
      623796.529863561,
      481107.2540129812,  <-- Wait, the original Python output had 481214.73871311604?
...

Actually looking more carefully at the original Python output:
```
    "cafes": {
      ...
      "bbox": [
        623796.529863561,
        481107.2540129812,
        ...

```

Hmm wait, but looking at the python output lines more carefully, the output from the script was:

```
  {
    "layer_name": "cafes",
    ...
    "bbox": [
      623796.529863561,
      481107.2540129812,
      626399.5618121764,
      484498.9209005649
    ]
  },
```

This seems off - I had written 481107 in the file, but the original Python output showed something around 481200+ for the schools layer. Let me re-run to double check the exact values.

0:41

Bash python3 -c "

python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/vienna_planning.gpkg', layer='cafes')
print('cafes bounds:', list(gdf.total_bounds))
"

cafes bounds: [np.float64(623796.529863561), np.float64(481107.2540129812), np.float64(626399.5618121764), np.float64(484498.9209005649)]

0:51

Everything checks out. Let me verify the manifest.json is valid JSON and has the correct structure.

0:51

Bash python3 -c "

python3 -c "
import json
with open('/work/manifest.json') as f:
    data = json.load(f)
print(f'Records: {len(data)}')
for rec in data:
    assert set(rec.keys()) == {'layer_name', 'crs', 'geometry_type', 'feature_count', 'bbox'}
    assert isinstance(rec['layer_name'], str)
    assert isinstance(rec['crs'], str)
    assert isinstance(rec['geometry_type'], str)
    assert isinstance(rec['feature_count'], int)
    assert isinstance(rec['bbox'], list) and len(rec['bbox']) == 4
    print(f'  {rec[\"layer_name\"]:20s} {rec[\"crs\"]:12s} {rec[\"geometry_type\"]:15s} {rec[\"feature_count\"]:4d}  BBox: {rec[\"bbox\"]}')
print('All good.')
"

Records: 7
  districts            EPSG:31287   Polygon           22  BBox: [623867.3888224852, 481236.5836125494, 626371.3079994549, 484403.7315526943]
  parks                EPSG:31287   MultiPolygon     119  BBox: [623851.990195953, 481200.6674012489, 626620.9438865517, 485539.3479671855]
  waterbodies          EPSG:31287   Polygon           33  BBox: [624288.8521056126, 481236.66320640914, 626747.8611584986, 484132.0212685799]
  schools              EPSG:31287   Point             40  BBox: [624000.002417057, 481214.73871311604, 626259.5421406503, 484178.87865010765]
  cafes                EPSG:31287   Point            392  BBox: [623796.529863561, 481107.2540129812, 626399.5618121764, 484498.9209005649]
  supermarkets         EPSG:31287   Point             87  BBox: [623794.0549950565, 481148.38703723426, 626392.6164786755, 484489.91954147833]
  cycleway_segments    EPSG:31287   LineString       271  BBox: [623787.4932131292, 481134.9325878215, 626619.1631922598, 485245.6624853243]
All good.

0:55

Everything checks out. Let me verify the file exists on disk.

0:55

Bash ls -lh /work/manifest.json

-rw-r--r-- 1 agent agent 1.5K Jun 18 13:58 /work/manifest.json

1:01

Everything checks out. Let me summarize what was produced.

1:01

score: 100

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": "2026-06-18T13:57:32.529923Z",
  "error": null,
  "files": [
    "manifest.json",
    "vienna_planning.gpkg"
  ],
  "last_activity_at": "2026-06-18T13:58:34.375698Z",
  "session_id": "6874fc7264a1470fbfcc090ef53d4583",
  "status": "idle",
  "task_version": 1,
  "usage": {
    "agent_version": "0.3.0",
    "duration_s": 61.612700345009216,
    "estimated_cost_usd": 0.001808952,
    "model": "deepseek/deepseek-v4-flash"
  }
}

dd-l1-vienna-gpkg-manifest Rerun

Map (reference ← swipe → agent)

Gates

Subchecks

dd-l1-vienna-gpkg-manifest