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 google-earth-enginegit 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/google-earth-engine)<a href="https://agentmods.dev/skills/muend/geoai-skills/google-earth-engine"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/google-earth-engine/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/google-earth-engine"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/google-earth-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00101 | $0.02367 |
| Opus 5 | $0.00051 | $0.01184 |
| Sonnet 5 | $0.00020 | $0.00473 |
| Haiku 4.5 | $0.00010 | $0.00237 |
Grade A, and why
google-earth-engine 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 11d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Earth Engine
Purpose: use GEE's server-side model correctly. The recurring failure
modes are client/server confusion (calling .getInfo() in loops,
Python if on server objects), unbounded computation (timeouts from
unscaled reductions), and silent default scales (statistics computed
at the wrong resolution).
Should this run here at all? — Earth Engine versus local
Answer this before writing any ee. code. The decision turns on six things, and
you cannot make it without them, so establish them first — asking alongside a
provisional recommendation, never instead of one:
- Archive extent and duration — area, and how many years at what revisit. This is what makes server-side worth its constraints; a single scene does not.
- Algorithm expressibility — can the work be written as masks, reducers and band math? Anything needing arbitrary per-pixel iteration, a custom solver, or a Python library GEE does not host belongs local.
- Data locality and sensitivity — restricted or offline data cannot be uploaded, and that ends the discussion regardless of scale.
- Interactive limits versus batch — see Quotas and etiquette.
Anything beyond a ~5 minute interactive request has to be designed as a batch
export from the start, not retrofitted when
getInfotimes out. - Export volume — what actually comes back: a few reduced statistics, or full-resolution per-pixel stacks you will store and reprocess locally.
- Reproducibility cost — the real price of moving server-side. The catalog version can shift under you and the computation leaves no local trace, so choosing GEE obliges you to ship the provenance record. State this cost when you recommend GEE; a recommendation that omits it is incomplete.
Recommend Earth Engine only when 1 and 2 favour it and 3 permits it. When the
answer is genuinely balanced, say so and name the deciding question rather than
defaulting to the platform this skill is about. xee and STAC + stackstac /
odc-stac are the middle paths worth naming: catalog access with local compute.
What ships with it
2 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.
- 11d ago First seen · 200 lines · 101 tokens per session scan A bb323b728637
google-earth-engine is a skill published in the GitHub repository muend/geoai-skills (15 stars, last pushed 7d ago), licensed MIT. It adds 101 tokens to every session and 2,367 once invoked, about $0.0005 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
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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.
process-raster
Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.
search-stac
Search and download satellite imagery from Microsoft Planetary Computer. Browse available collections, search by bbox and time range, list assets, and download specific items.
gdal
Use when processing geospatial raster/vector data via command line — format conversion (Shapefile to GeoJSON), reprojection, DEM analysis, NDVI calculation, mosaicking. GDAL/OGR CLI: the industry standard for batch geospatial data processing with 50+ command-line tools (ogr2ogr, gdalwarp, gdaltranslate, gdalcalc).