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 change-detectiongit 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/change-detection)<a href="https://agentmods.dev/skills/muend/geoai-skills/change-detection"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/change-detection/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/change-detection"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/change-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00158 | $0.01739 |
| Opus 5 | $0.00079 | $0.00870 |
| Sonnet 5 | $0.00032 | $0.00348 |
| Haiku 4.5 | $0.00016 | $0.00174 |
Grade A, and why
change-detection 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Change Detection & Spatio-temporal Analysis
Purpose: separate real surface change from the four great impostors — misregistration, radiometric drift, phenology, and classification error. Every method below exists to control one of them; skipping the controls produces confident maps of nothing.
Preconditions (where change detection is won or lost)
Preconditions 1 and 2 are checked here but established elsewhere. When either fails, the owning skill leads and this skill resumes once comparable observations exist. Precondition 3 is this skill's own problem and is never a reason to route away.
- Co-registration: sub-pixel alignment between dates (AROSICS or
manual tie-points). Half a pixel of shift creates edge-shaped phantom
change everywhere. Verify: flicker-compare crisp features. For elevation
surfaces or point clouds, vertical datum agreement, co-registration and
the vertical-accuracy budget belong to
point-cloud-lidar— a datum offset is not subsidence. - Radiometric consistency: same processing level (surface
reflectance), same sensor, same processing baseline. If any of the three
differ, this is a harmonization problem, not a thresholding one: hand it
to
remote-sensing-analysis(HLS for Landsat↔Sentinel-2, relative normalization with PIFs,BOA_ADD_OFFSETacross the Sentinel-2 2022 baseline change). - Same season / phenological stage for bi-temporal work — a May vs September pair "detects" summer. If season can't be matched, use composites or time-series methods instead.
- Cloud/shadow masks intersected across dates; analyze only mutually valid pixels and report that coverage %.
Method selection
| Situation | Method |
|---|---|
| Two dates, continuous "how much" | Index differencing (ΔNDVI, ΔNBR...) with statistical thresholding |
| Two dates, categorical "from-what-to-what" | Post-classification comparison (only with strong classifiers) |
| Two dates, multivariate robust | Change vector analysis (CVA); MAD/iMAD for sensor-robust detection |
| Dense stack, gradual + abrupt | Trend + break analysis (BFAST/LandTrendr/CCDC family; at archive scale → google-earth-engine) |
| Structure change (buildings) | DL bi-temporal segmentation (siamese U-Net) → geo-deep-learning |
| SAR pairs (clouds, disasters) | Log-ratio of calibrated backscatter + speckle handling |
| Vector vintages (parcels, buildings) | Geometry+attribute diff with tolerance (below) |
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 · 144 lines · 158 tokens per session scan A fd0a6a28083e
change-detection is a skill published in the GitHub repository muend/geoai-skills (17 stars, last pushed 7d ago), licensed MIT. It adds 158 tokens to every session and 1,739 once invoked, about $0.0008 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.
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).