Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add muend/geoai-skills --skill geo-data-engineeringgit clone --depth 1 https://github.com/muend/geoai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/muend/geoai-skills/geo-data-engineering)<a href="https://agentmods.dev/skills/muend/geoai-skills/geo-data-engineering"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/geo-data-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/muend/geoai-skills/geo-data-engineering"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/geo-data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.01346 |
| Opus 5 | $0.00044 | $0.00673 |
| Sonnet 5 | $0.00017 | $0.00269 |
| Haiku 4.5 | $0.00009 | $0.00135 |
Grade A, and why
geo-data-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Data Engineering
Purpose: get spatial data into a clean, validated, analysis-ready state with a repeatable pipeline — the stage where most real-world GIS time is spent and most silent errors are born.
Format selection
| Format | Use for | Avoid because |
|---|---|---|
| GeoParquet | Analysis interchange, big vector, columnar workflows | Not yet readable by some legacy desktop GIS |
| GeoPackage | Desktop GIS exchange, multi-layer projects | Slower than Parquet at scale; SQLite locking |
| FlatGeobuf | Streaming, HTTP range reads | Single layer |
| COG (Cloud-Optimized GeoTIFF) | All raster deliverables | — (make every GeoTIFF a COG) |
| Zarr/NetCDF | Multi-dimensional (time × band × y × x) | Overkill for single rasters |
| Shapefile | Only when a legacy tool demands it | 10-char columns, 2 GB cap, encoding chaos, multi-file fragility |
| CSV + WKT/lon-lat | Simple point exchange | No CRS metadata — document it explicitly |
Acquisition playbook
- OpenStreetMap: small areas →
osmnx; large extracts → Geofabrik PBF +pyrosm/osmium. Respect tag heterogeneity: always inspect tag value distributions before filtering. - Buildings/places at scale: Overture Maps (GeoParquet on S3/Azure, query with DuckDB spatial — often the fastest path).
- Satellite/raster: STAC APIs via
pystac-client+odc-stac— seeremote-sensing-analysis; planetary archives →google-earth-engine. - Boundaries: authoritative national source first; Natural Earth / GADM / geoBoundaries for global work — record which, versions differ materially.
- Record every acquisition: source URL, query parameters, retrieval date,
license. Put it in a
DATA_SOURCES.mdnext to the data.
CRS engineering
- Store in EPSG:4326 or source CRS; analyze in a projected CRS suited to
the extent: local UTM zone (
gdf.estimate_utm_crs()), national grid, or equal-area (EPSG:6933/Mollweide) for cross-region area stats. - Datum shifts matter at sub-meter precision: transformations between datums
need the right transformation grid (
pyproj.network.set_network_enabled(True)when accuracy matters). - Never strip or overwrite a CRS to "fix" misaligned layers — diagnose which layer is wrong with a known landmark instead.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 106 lines · 87 tokens per session scan A f1c004afc155
geo-data-engineering is a skill published in the GitHub repository muend/geoai-skills (15 stars, last pushed 6d ago), licensed MIT. It adds 87 tokens to every session and 1,346 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
detect-objects
Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with GroundedSAM. Requires GPU for best performance.
gdal-api
Use when programming against GDAL/OGR in C, C++, Python, or .NET for raster/vector I/O, coordinate transformation, or custom geospatial algorithms. GDAL API: low-level programming interface for reading/writing 70+ geospatial formats.
read-memories
Search past Claude Code session logs to recover context from previous conversations. Finds past decisions, data paths, CRS info, model configurations, and unresolved work. Works across all projects or scoped to the current one.
download-data
Download NAIP aerial imagery for a bounding box. Specify coordinates as minx,miny,maxx,maxy in WGS84 and optionally a year.
inspect-geo
Inspect any raster or vector geospatial file. Returns CRS, bounds, bands, resolution, dtype, attribute summaries, and band statistics. Supports GeoTIFF, Shapefile, GeoJSON, GeoPackage, GeoParquet, and more.
overture-data
Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box. Returns a GeoDataFrame saved as GeoJSON or GeoPackage.