google-earth-engine-31-parse-getinfo-results
Sub-skill of google-earth-engine: 3.1 Parse getInfo() Results (+2).
Best use case
google-earth-engine-31-parse-getinfo-results is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of google-earth-engine: 3.1 Parse getInfo() Results (+2).
Teams using google-earth-engine-31-parse-getinfo-results should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/31-parse-getinfo-results/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How google-earth-engine-31-parse-getinfo-results Compares
| Feature / Agent | google-earth-engine-31-parse-getinfo-results | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of google-earth-engine: 3.1 Parse getInfo() Results (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 3.1 Parse getInfo() Results (+2)
## 3.1 Parse getInfo() Results
```python
# Always limit getInfo() to small results — expensive call
result = stats.getInfo()
depth_min = result["elevation_min"]
depth_max = result["elevation_max"]
depth_mean = result["elevation_mean"]
```
## 3.2 Read Exported GeoTIFF
```python
import rasterio
import numpy as np
with rasterio.open("gebco_north_sea_500m.tif") as src:
depth = src.read(1).astype(float)
depth[depth == src.nodata] = np.nan
transform = src.transform
crs = src.crs
print(f"Grid: {depth.shape}, depth range: {np.nanmin(depth):.0f} to {np.nanmax(depth):.0f} m")
```
## 3.3 Time-Series to DataFrame
```python
import pandas as pd
# Reduce collection to time series at a point
point = ee.Geometry.Point([-1.5, 57.0])
ts = era5_ws.map(lambda img: img.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=point,
scale=10000
).set("date", img.date().format("YYYY-MM-dd")))
data = ts.aggregate_array("date").getInfo()
ws_vals = ts.aggregate_array("wind_speed").getInfo()
df = pd.DataFrame({"date": data, "wind_speed_ms": ws_vals})
df["date"] = pd.to_datetime(df["date"])
```
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