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 terrain-hydrologygit 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/terrain-hydrology)<a href="https://agentmods.dev/skills/muend/geoai-skills/terrain-hydrology"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/terrain-hydrology/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/terrain-hydrology"><img src="https://agentmods.dev/badge/skills/muend/geoai-skills/terrain-hydrology.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 | $0.00090 | $0.01677 |
| Opus 5 | $0.00045 | $0.00839 |
| Sonnet 5 | $0.00018 | $0.00335 |
| Haiku 4.5 | $0.00009 | $0.00168 |
Grade A, and why
terrain-hydrology 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 12d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terrain & Hydrology
Purpose: terrain products whose numbers are physically meaningful. The two recurring failure modes: unit mismatch (degree coordinates with meter elevations silently corrupts every derivative) and unconditioned DEMs (flow routed into spurious pits produces fragmented, fictional streams).
DEM hygiene first
| Check | Rule |
|---|---|
| Surface type | DTM (bare earth) for hydrology/slope; DSM (with canopy/buildings) for viewshed/solar. Using a DSM for watersheds routes rivers over treetops. |
| Source | Copernicus GLO-30 > SRTM for most global work; national LiDAR DTMs when available (see point-cloud-lidar to make your own). Record source + acquisition date. |
| Nodata | Identify the nodata value (-9999, -32768, 3.4e38) and mask it — never let it enter statistics or fill algorithms as "very deep hole". |
| Voids | Fill data voids (interpolation from edges) BEFORE hydrological conditioning; document filled areas. |
| CRS + units | Reproject to a projected CRS so horizontal units = vertical units (meters). Slope from a 4326 DEM without z-factor correction is the classic silent error. If staying geographic, apply a latitude-dependent z-factor — better: don't. |
Derivatives
import whitebox
wbt = whitebox.WhiteboxTools()
wbt.slope("dem.tif", "slope_deg.tif", units="degrees")
wbt.aspect("dem.tif", "aspect_deg.tif")
wbt.plan_curvature("dem.tif", "plan_curv.tif")
- Slope: state units (degrees vs percent — 45° = 100%); Horn's method (3×3) is the standard; steeper terrain → consider resolution effects (slope flattens as cell size grows — report cell size with every slope statistic).
- Aspect: circular variable — never average it arithmetically; use vector (sin/cos) averaging; flat cells have undefined aspect (mask, don't zero).
- Curvature: plan (flow convergence) vs profile (flow acceleration) — pick per question.
- Hillshade is for cartography (see
cartography-geoviz), never analysis input. - Ruggedness/position: TRI, TPI (radius-dependent — report the radius), geomorphons for landform classification.
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.
- 12d ago First seen · 138 lines · 90 tokens per session scan A 391051e89b61
terrain-hydrology is a skill published in the GitHub repository muend/geoai-skills (18 stars, last pushed 8d ago), licensed MIT. It adds 90 tokens to every session and 1,677 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.
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