dd-l1-vienna-gpkg-manifest

Status: done

Score: 1.000

Duration: 0:26

Cost: 0.17¢

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"
}

0:02

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

0:02

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

-rw-r--r-- 1 agent agent 512K Jun 18 05:38 /work/vienna_planning.gpkg

0:05

Let me explore the GPKG file to see what layers it contains and gather the required information.

0:05

Bash cd /work && python3 -c "

cd /work && python3 -c "
import sqlite3
conn = sqlite3.connect('vienna_planning.gpkg')
cur = conn.cursor()
# List all tables/ layers
cur.execute(\"SELECT table_name, geometry_column_name, srs_id FROM gpkg_contents WHERE data_type = 'features' ORDER BY table_name\")
rows = cur.fetchall()
for r in rows:
    print(r)
conn.close()
"

Traceback (most recent call last):
  File "<string>", line 6, in <module>
    cur.execute("SELECT table_name, geometry_column_name, srs_id FROM gpkg_contents WHERE data_type = 'features' ORDER BY table_name")
    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
sqlite3.OperationalError: no such column: geometry_column_name

0:09

The gpkg_contents table has slightly different column names - let me check the schema.

0:09

Bash cd /work && python3 -c "

cd /work && python3 -c "
import sqlite3
conn = sqlite3.connect('vienna_planning.gpkg')
cur = conn.cursor()
# Check gpkg_contents schema
cur.execute(\"PRAGMA table_info(gpkg_contents)\")
print('gpkg_contents:', cur.fetchall())
# Also check what tables exist
cur.execute(\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\")
print('Tables:', cur.fetchall())
cur.execute(\"SELECT * FROM gpkg_contents\")
print('Contents:', cur.fetchall())
conn.close()
"

gpkg_contents: [(0, 'table_name', 'TEXT', 1, None, 1), (1, 'data_type', 'TEXT', 1, None, 0), (2, 'identifier', 'TEXT', 0, None, 0), (3, 'description', 'TEXT', 0, "''", 0), (4, 'last_change', 'DATETIME', 1, "strftime('%Y-%m-%dT%H:%M:%fZ','now')", 0), (5, 'min_x', 'DOUBLE', 0, None, 0), (6, 'min_y', 'DOUBLE', 0, None, 0), (7, 'max_x', 'DOUBLE', 0, None, 0), (8, 'max_y', 'DOUBLE', 0, None, 0), (9, 'srs_id', 'INTEGER', 0, None, 0)]
Tables: [('cafes',), ('cycleway_segments',), ('districts',), ('gpkg_contents',), ('gpkg_extensions',), ('gpkg_geometry_columns',), ('gpkg_ogr_contents',), ('gpkg_spatial_ref_sys',), ('gpkg_tile_matrix',), ('gpkg_tile_matrix_set',), ('parks',), ('rtree_cafes_geom',), ('rtree_cafes_geom_node',), ('rtree_cafes_geom_parent',), ('rtree_cafes_geom_rowid',), ('rtree_cycleway_segments_geom',), ('rtree_cycleway_segments_geom_node',), ('rtree_cycleway_segments_geom_parent',), ('rtree_cycleway_segments_geom_rowid',), ('rtree_districts_geom',), ('rtree_districts_geom_node',), ('rtree_districts_geom_parent',), ('rtree_districts_geom_rowid',), ('rtree_parks_geom',), ('rtree_parks_geom_node',), ('rtree_parks_geom_parent',), ('rtree_parks_geom_rowid',), ('rtree_schools_geom',), ('rtree_schools_geom_node',), ('rtree_schools_geom_parent',), ('rtree_schools_geom_rowid',), ('rtree_supermarkets_geom',), ('rtree_supermarkets_geom_node',), ('rtree_supermarkets_geom_parent',), ('rtree_supermarkets_geom_rowid',), ('rtree_waterbodies_geom',), ('rtree_waterbodies_geom_node',), ('rtree_waterbodies_geom_parent',), ('rtree_waterbodies_geom_rowid',), ('schools',), ('sqlite_sequence',), ('supermarkets',), ('waterbodies',)]
Contents: [('districts', 'features', 'districts', '', '2026-05-08T08:00:50.775Z', 623867.3888224852, 481236.5836125494, 626371.3079994549, 484403.7315526943, 31287), ('parks', 'features', 'parks', '', '2026-05-08T08:00:50.793Z', 623851.990195953, 481200.6674012489, 626620.9438865517, 485539.3479671855, 31287), ('waterbodies', 'features', 'waterbodies', '', '2026-05-08T08:00:50.824Z', 624288.8521056126, 481236.6632064091, 626747.8611584986, 484132.0212685799, 31287), ('schools', 'features', 'schools', '', '2026-05-08T08:00:50.855Z', 624000.002417057, 481214.738713116, 626259.5421406503, 484178.8786501077, 31287), ('cafes', 'features', 'cafes', '', '2026-05-08T08:00:50.886Z', 623796.529863561, 481107.2540129812, 626399.5618121764, 484498.9209005649, 31287), ('supermarkets', 'features', 'supermarkets', '', '2026-05-08T08:00:50.917Z', 623794.0549950565, 481148.3870372343, 626392.6164786755, 484489.9195414783, 31287), ('cycleway_segments', 'features', 'cycleway_segments', '', '2026-05-08T08:00:50.945Z', 623787.4932131292, 481134.9325878215, 626619.1631922598, 485245.6624853243, 31287)]

0:12

Now let me get the geometry columns info and CRS info.

0:12

Bash cd /work && python3 -c "

cd /work && python3 -c "
import sqlite3
conn = sqlite3.connect('vienna_planning.gpkg')
cur = conn.cursor()
cur.execute('SELECT * FROM gpkg_geometry_columns')
print('geom_columns:', cur.fetchall())
cur.execute('SELECT * FROM gpkg_spatial_ref_sys')
print('SRS:', cur.fetchall())
conn.close()
"

geom_columns: [('districts', 'geom', 'POLYGON', 31287, 0, 0), ('parks', 'geom', 'MULTIPOLYGON', 31287, 0, 0), ('waterbodies', 'geom', 'POLYGON', 31287, 0, 0), ('schools', 'geom', 'POINT', 31287, 0, 0), ('cafes', 'geom', 'POINT', 31287, 0, 0), ('supermarkets', 'geom', 'POINT', 31287, 0, 0), ('cycleway_segments', 'geom', 'LINESTRING', 31287, 0, 0)]
SRS: [('Undefined Cartesian SRS', -1, 'NONE', -1, 'undefined', 'undefined Cartesian coordinate reference system'), ('Undefined geographic SRS', 0, 'NONE', 0, 'undefined', 'undefined geographic coordinate reference system'), ('WGS 84 geodetic', 4326, 'EPSG', 4326, 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]', 'longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid'), ('MGI / Austria Lambert', 31287, 'EPSG', 31287, 'PROJCS["MGI / Austria Lambert",GEOGCS["MGI",DATUM["Militar-Geographische_Institut",SPHEROID["Bessel 1841",6377397.155,299.1528128,AUTHORITY["EPSG","7004"]],AUTHORITY["EPSG","6312"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4312"]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["latitude_of_origin",47.5],PARAMETER["central_meridian",13.3333333333333],PARAMETER["standard_parallel_1",49],PARAMETER["standard_parallel_2",46],PARAMETER["false_easting",400000],PARAMETER["false_northing",400000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Northing",NORTH],AXIS["Easting",EAST],AUTHORITY["EPSG","31287"]]', None)]

0:18

Let me now get feature counts and precise bbox for each layer. Let me use geopandas to read each layer and get the info.

0:18

Bash cd /work && python3 -c "

cd /work && python3 -c "
import geopandas as gpd
import json

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

for layer in layers:
    gdf = gpd.read_file('vienna_planning.gpkg', layer=layer)
    crs = gdf.crs
    # Get EPSG code
    if crs is not None:
        epsg_code = crs.to_epsg()
        crs_str = f'EPSG:{epsg_code}' if epsg_code else str(crs)
    else:
        crs_str = 'None'
    
    geom_type = gdf.geometry.geom_type.iloc[0] if len(gdf) > 0 else 'Unknown'
    
    count = len(gdf)
    
    # Get bbox in native CRS
    if len(gdf) > 0:
        total_bounds = gdf.total_bounds  # [xmin, ymin, xmax, ymax]
        bbox = [float(total_bounds[0]), float(total_bounds[1]), float(total_bounds[2]), float(total_bounds[3])]
    else:
        bbox = []
    
    results.append({
        'layer_name': layer,
        'crs': crs_str,
        'geometry_type': geom_type,
        'feature_count': count,
        'bbox': bbox
    })
    
    print(f'{layer}: {geom_type}, {crs_str}, count={count}, bbox={bbox}')

with open('manifest.json', 'w') as f:
    json.dump(results, f, indent=2)
print('Written manifest.json')
"

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

0:21

Let me verify the output file:

0:21

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	]

0:26

The manifest is complete and looks correct. Let me verify it's valid JSON.

0:26

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": "2026-06-18T05:38:33.460051Z",
  "error": null,
  "files": [
    "manifest.json",
    "vienna_planning.gpkg"
  ],
  "last_activity_at": "2026-06-18T05:38:59.733597Z",
  "session_id": "f40a08abd40443129a34f113de760535",
  "status": "idle",
  "task_version": 1,
  "usage": {
    "agent_version": "0.3.0",
    "duration_s": 26.055294132005656,
    "estimated_cost_usd": 0.001679142,
    "model": "deepseek/deepseek-v4-flash"
  }
}

dd-l1-vienna-gpkg-manifest Rerun

Map (reference ← swipe → agent)

Gates

Subchecks

dd-l1-vienna-gpkg-manifest