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-deep-learninggit 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-deep-learning)<a href="https://agentmods.dev/skills/muend/geoai-skills/geo-deep-learning"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/geo-deep-learning/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-deep-learning"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/geo-deep-learning.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 | $0.00104 | $0.01699 |
| Opus 5 | $0.00052 | $0.00849 |
| Sonnet 5 | $0.00021 | $0.00340 |
| Haiku 4.5 | $0.00010 | $0.00170 |
Grade A, and why
geo-deep-learning 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Deep Learning
Purpose: deep learning on Earth observation with the two failure modes that dominate this field designed out from the start: spatial leakage (inflated metrics from nearby train/test pixels) and georeferencing loss (predictions that no longer align with the map).
Characterise the label set before naming an architecture
Architecture advice given without knowing the label set is guesswork. Before recommending U-Net versus a foundation model versus a non-deep baseline, state or ask for:
- Label count and labelled area — polygons alone say nothing; 40 polygons covering 2 ha and 40 covering 2 000 km² are different problems.
- Geographic spread — are the labels clustered in one scene, one season and one sensor, or distributed across the deployment domain? Clustered labels cap what any model can generalise to, and they decide whether a geographically independent validation split is even constructible.
- Class balance and minority-class pixel fraction, so loss and sampling choices are grounded rather than assumed.
- Deployment geography — where predictions will be made, relative to where the labels are.
Do not answer "fine-tune a large model or use a simpler approach" before these are known. When the user has not supplied them, ask and give the provisional recommendation conditioned on the answers ("if the 40 polygons sit in one scene, then …; if they span the region, then …"), never a single unconditional recommendation.
Problem framing first
| Task | Head/architecture default | Metric |
|---|---|---|
| Pixel-wise classes (land cover) | U-Net / DeepLabv3+ (pretrained encoder) | mIoU, per-class IoU |
| Binary extraction (buildings, water, roads) | U-Net + Dice/CE hybrid | IoU, F1; boundary F1 for roads |
| Object detection (vehicles, ships, trees) | YOLO-family / Faster R-CNN, rotated boxes if oriented | mAP@50 |
| Scene classification | Fine-tuned CNN/ViT | F1 (macro) |
| Regression (height, biomass, density) | U-Net with regression head | RMSE/MAE + spatial residual map |
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 · 146 lines · 104 tokens per session scan A 5d71a76337dc
geo-deep-learning is a skill published in the GitHub repository muend/geoai-skills (17 stars, last pushed 7d ago), licensed MIT. It adds 104 tokens to every session and 1,699 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
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.
process-raster
Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.
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.
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.
agent-auto-sci-ai-ml
A research workflow for building and checking machine-learning and artificial-intelligence models, including models that work with maps, time-based data, text, or images.