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/process-raster

Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.

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geoai-skills
308 skills
Install
$ npx -y skills add opengeos/geoai-skills --skill process-raster --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/process-raster

Context preview

The summary Claude sees to decide when to auto-load this skill.

Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.

SKILL.md

process-raster.SKILL.md
name: process-raster
description: >
  Process raster data: clip by bounding box, stack multiple bands, mosaic
  GeoTIFFs, or convert between raster and vector formats.
argument-hint: <operation> <input> [options]
allowed-tools: Bash

You are helping the user process geospatial raster data using geoai.

Input: `$@`

Follow these steps in order.

Step 1 -- Determine the operation

Parse `$@` to identify the requested operation:

| Operation | Triggers | Required inputs | |---|---|---| | `clip` | "clip", "crop", "subset", `--bbox` present | input raster + bbox | | `stack` | "stack", "combine bands" | list of input rasters | | `mosaic` | "mosaic", "merge" | input directory or list of rasters | | `raster-to-vector` | "to vector", "vectorize", "polygonize" | input raster | | `vector-to-raster` | "to raster", "rasterize", "burn" | input vector + pixel size |

If the operation is unclear from the input, ask the user to specify.

Step 2 -- Resolve input file(s)

For single-file operations (`clip`, `raster-to-vector`, `vector-to-raster`):

find "$PWD" -name "INPUT_FILENAME" -not -path '*/.git/*' 2>/dev/null

For multi-file operations (`stack`, `mosaic`), if a directory is given:

find "INPUT_DIR" -name "*.tif" -o -name "*.tiff" 2>/dev/null | sort

If the user recently inspected or downloaded a file and did not specify an input, check the state file for context:

STATE_DIR=""
test -f .geoai-skills/state.json && STATE_DIR=".geoai-skills"
PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
PROJECT_ID="$(echo "$PROJECT_ROOT" | tr '/' '-')"
test -f "$HOME/.geoai-skills/$PROJECT_ID/state.json" && STATE_DIR="$HOME/.geoai-skills/$PROJECT_ID"

If state exists, read the last inspected or downloaded file:

python3 -c "
import json
with open('STATE_DIR/state.json') as f:
    state = json.load(f)
if 'last_inspected' in state:
    print(f'Last inspected: {state[\"last_inspected\"][\"path\"]}')
if 'downloaded_files' in state:
    for f in state['downloaded_files']:
        print(f'Downloaded: {f}')
"

Step 3 -- Execute the operation

Clip by bounding box

python3 -c "
import geoai

result = geoai.clip_raster_by_bbox(
    input_raster='INPUT_PATH',
    output_raster='OUTPUT_PATH',
    bbox=[MINX, MINY, MAXX, MAXY],
)
print(f'Clipped raster saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Default output: `./clipped_<original_name>.tif`

Stack bands

python3 -c "
import geoai

result = geoai.stack_bands(
    input_files=['FILE1', 'FILE2', 'FILE3'],
    output_file='OUTPUT_PATH',
)
print(f'Stacked raster saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Mosaic GeoTIFFs

python3 -c "
import geoai

result = geoai.mosaic_geotiffs(
    input_dir='INPUT_DIR',
    output_file='OUTPUT_PATH',
)
print(f'Mosaic saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Raster to vector

python3 -c "
import geoai

gdf = geoai.raster_to_vector(
    raster_path='INPUT_PATH',
    output_path='OUTPUT_PATH',
)
print(f'Vectorized: {len(gdf)} features')
print(f'Saved to: OUTPUT_PATH')
print(f'Columns: {list(gdf.columns)}')
"

Default output: `./<original_name>.gpkg`

Vector to raster

python3 -c "
import geoai

result = geoai.vector_to_raster(
    vector_path='INPUT_PATH',
    output_path='OUTPUT_PATH',
    pixel_size=PIXEL_SIZE,
)
print(f'Rasterized: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
    print(f'{k}: {v}')
"

Default pixel size: 1.0 (or infer from context). Default output: `./<original_name>.tif`

Replace all placeholder values with actual paths and parameters before running.

Step 4 -- Update state

If a state directory exists, update it with the output file path using the same state resolution pattern as Step 2.

Step 5 -- Report and suggest

Report:

  • Operation performed
  • Input and output file paths
  • Key properties of the output (dimensions, CRS, band count, feature count)

Then suggest: *"Use `/geoai-skills:inspect-geo` to examine the result in detail."*

Error handling

  • **`import geoai` fails** -> delegate to `/geoai-skills:install-geoai`.
  • **File not found** -> use `find` to locate, suggest corrected path.
  • **CRS mismatch** (for stack/mosaic) -> report the issue and suggest reprojecting first.
  • **Insufficient disk space** -> report the error.
  • **Memory error** (very large rasters) -> suggest processing in tiles or using a smaller extent.
Read more
Ships withgeoai-skills

A Claude Code plugin that adds GeoAI-powered skills for geospatial data exploration, satellite imagery download, AI-based object detection, and session memory. Built on the GeoAI Python library.

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MIT
License
1mo ago
Last commit
5mo ago
Created

Repo: opengeos/geoai-skills

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