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 agentmods add skills/mekxh/golem/remote-sensingnpx skills add MEKXH/golem --skill remote-sensinggit clone --depth 1 https://github.com/MEKXH/golemWrote 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/mekxh/golem/remote-sensing)<a href="https://agentmods.dev/skills/mekxh/golem/remote-sensing"><img src="https://agentmods.dev/badge/skills/mekxh/golem/remote-sensing.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00281 |
| Opus 5 | $0.00012 | $0.00140 |
| Sonnet 5 | $0.00005 | $0.00056 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
remote-sensing 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 5d 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.
What it actually says
Remote Sensing Workflow
Use this skill when the task involves satellite imagery, raster products, vegetation indices, terrain, or image preprocessing.
Step 1: Discover Candidate Imagery
If the user does not already provide raster files, discover likely datasets first with geo_data_catalog.
Step 2: Inspect and Verify CRS
Start with geo_info and geo_crs_detect.
Step 3: Reuse Learned Pipelines First
Before creating a new raster workflow, inspect whether pipelines/geo/ already captures a similar scene-prep or raster-analysis sequence.
Step 4: Use GDAL for Raster Processing
Typical operations go through geo_process.
Step 5: Convert Deliverables
Use geo_format_convert when the result needs a simpler output format.
Step 6: Fabricate a Reusable Raster Tool
If a raster workflow is clearly reusable and not covered by learned pipelines or built-in tools, fabricate a workspace geo tool under tools/geo/.
Conventions
- Prefer learned pipelines before fabricating a new raster tool.
- Prefer GeoTIFF for raster outputs unless the user asks for a web-ready preview.
- Keep all generated outputs inside the workspace.
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.
- 5d ago First seen · 33 lines · 23 tokens per session scan A c2dc72fc0589
remote-sensing is a skill published in the GitHub repository MEKXH/golem (199 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 281 once invoked, about $0.0001 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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