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 opengeos/geoai-skills --skill process-rastergit clone --depth 1 https://github.com/opengeos/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/opengeos/geoai-skills/process-raster)<a href="https://agentmods.dev/skills/opengeos/geoai-skills/process-raster"><img src="https://agentmods.dev/badge/skills/opengeos/geoai-skills/process-raster/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/opengeos/geoai-skills/process-raster"><img src="https://agentmods.dev/badge/skills/opengeos/geoai-skills/process-raster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00031 | $0.01292 |
| Opus 5 | $0.00015 | $0.00646 |
| Sonnet 5 | $0.00006 | $0.00258 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
process-raster 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 10d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are helping the user process geospatial raster data using geoai.
Input: $@
Follow these steps in order.
Step 1 -- Determine the operation
Parse $@ to identify the requested operation:
| Operation | Triggers | Required inputs |
|---|---|---|
clip |
"clip", "crop", "subset", --bbox present |
input raster + bbox |
stack |
"stack", "combine bands" | list of input rasters |
mosaic |
"mosaic", "merge" | input directory or list of rasters |
raster-to-vector |
"to vector", "vectorize", "polygonize" | input raster |
vector-to-raster |
"to raster", "rasterize", "burn" | input vector + pixel size |
If the operation is unclear from the input, ask the user to specify.
Step 2 -- Resolve input file(s)
For single-file operations (clip, raster-to-vector, vector-to-raster):
find "$PWD" -name "INPUT_FILENAME" -not -path '*/.git/*' 2>/dev/null
For multi-file operations (stack, mosaic), if a directory is given:
find "INPUT_DIR" -name "*.tif" -o -name "*.tiff" 2>/dev/null | sort
If the user recently inspected or downloaded a file and did not specify an input, check the state file for context:
STATE_DIR=""
test -f .geoai-skills/state.json && STATE_DIR=".geoai-skills"
PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
PROJECT_ID="$(echo "$PROJECT_ROOT" | tr '/' '-')"
test -f "$HOME/.geoai-skills/$PROJECT_ID/state.json" && STATE_DIR="$HOME/.geoai-skills/$PROJECT_ID"
If state exists, read the last inspected or downloaded file:
python3 -c "
import json
with open('STATE_DIR/state.json') as f:
state = json.load(f)
if 'last_inspected' in state:
print(f'Last inspected: {state[\"last_inspected\"][\"path\"]}')
if 'downloaded_files' in state:
for f in state['downloaded_files']:
print(f'Downloaded: {f}')
"
Step 3 -- Execute the operation
Clip by bounding box
python3 -c "
import geoai
result = geoai.clip_raster_by_bbox(
input_raster='INPUT_PATH',
output_raster='OUTPUT_PATH',
bbox=[MINX, MINY, MAXX, MAXY],
)
print(f'Clipped raster saved to: {result}')
info = geoai.get_raster_info(result)
for k, v in info.items():
print(f'{k}: {v}')
"
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.
- 10d ago First seen · 185 lines · 31 tokens per session scan A a1f6c29ebfe0
process-raster is a skill published in the GitHub repository opengeos/geoai-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,292 once invoked, about $0.0002 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 2026-08-30.
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