Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jokull/icelandic-datanpx agentmods add skills/jokull/icelandic-data/lmi-hrlWrote 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/jokull/icelandic-data/lmi-hrl)<a href="https://agentmods.dev/skills/jokull/icelandic-data/lmi-hrl"><img src="https://agentmods.dev/badge/skills/jokull/icelandic-data/lmi-hrl/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/jokull/icelandic-data/lmi-hrl"><img src="https://agentmods.dev/badge/skills/jokull/icelandic-data/lmi-hrl.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 | $0.00040 | $0.01225 |
| Opus 5 | $0.00020 | $0.00613 |
| Sonnet 5 | $0.00008 | $0.00245 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
lmi-hrl scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -o preview.png "https://gis.lmi.is/geoserver/High_Resolution_Layer/wms\ How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LMI High Resolution Layers (Copernicus HRL Iceland 2015)
Pan-European Copernicus Land Monitoring Service "High Resolution Layers" clipped to Iceland and re-served by Landmælingar Íslands. Five thematic binary/percentage rasters, all from the 2015 reference year, all 20 m native resolution, all in EPSG:5325 (ETRS89 / LAEA Iceland).
Catalogue UUID: 58e1ed85-df4d-408d-a34f-d0a60628cb34
Metadata page
OGC services
WMS: https://gis.lmi.is/geoserver/High_Resolution_Layer/wms
WCS: https://gis.lmi.is/geoserver/High_Resolution_Layer/wcs
WMTS: https://gis.lmi.is/geoserver/gwc/service/wmts
The same workspace is mirrored at https://gis.natt.is/geoserver/High_Resolution_Layer/ (Náttúrufræðistofnun) — either host works.
Layers
| Short name | WMS layer / WCS coverage | What | Pixel coding |
|---|---|---|---|
grassland |
Grassland / High_Resolution_Layer__Grassland |
Managed, semi-natural & natural grassy vegetation | 1 grass · 0 non-grass · 254 no-data inside Iceland · 255 outside |
tree_cover |
Tree_Cover_Density / High_Resolution_Layer__Tree_Cover_Density |
Tree canopy density 0-100 % | uint8 percentage; 255 no-data |
imperviousness |
Imperviousness / High_Resolution_Layer__Imperviousness |
Sealed surface 0-100 % | uint8 percentage; 255 no-data |
water_wetness |
Water_and_Wetness / High_Resolution_Layer__Water_and_Wetness |
Permanent / temporary water & wetness | class codes |
dominant_leaf |
Dominant_Leaf_Type / High_Resolution_Layer__Dominant_Leaf_Type |
Broadleaved / coniferous in tree-covered pixels | class codes |
Bounding box (EPSG:5325): 1399300, 53900, 2007200, 609200 (m)
Grid: 30394 × 27764 pixels @ 20 m
Fetching
# Full 20 m GeoTIFF — Grassland is ~865 MB uncompressed
uv run python scripts/lmi_hrl.py fetch grassland
# Downsampled (scaleFactor 0.2 → 100 m, ~33 MB)
uv run python scripts/lmi_hrl.py fetch grassland --scale 0.2
# Quick WMS preview — useful for sanity-checking before a big WCS download
curl -o preview.png "https://gis.lmi.is/geoserver/High_Resolution_Layer/wms\
?service=WMS&version=1.3.0&request=GetMap&layers=Grassland&styles=\
&format=image/png&transparent=true&width=800&height=600\
&crs=EPSG:3057&bbox=200000,300000,800000,700000"
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.
- 9d ago First seen · 84 lines · 40 tokens per session scan A 8583b197810a
lmi-hrl is a skill published in the GitHub repository jokull/icelandic-data (52 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,225 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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