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 Vortx-AI/emem --skill emem-recall-polygongit clone --depth 1 https://github.com/Vortx-AI/ememWrote 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/vortx-ai/emem/emem-recall-polygon)<a href="https://agentmods.dev/skills/vortx-ai/emem/emem-recall-polygon"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-recall-polygon/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/vortx-ai/emem/emem-recall-polygon"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-recall-polygon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Data Exfiltration · line 35 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 69 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00099 | $0.01446 |
| Opus 5 | $0.00049 | $0.00723 |
| Sonnet 5 | $0.00020 | $0.00289 |
| Haiku 4.5 | $0.00010 | $0.00145 |
Grade A, and why
emem-recall-polygon 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Bash(curl:*) Bash(jq:*) Read How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
emem-recall-polygon
This skill calls /v1/recall_polygon to fetch facts inside an
arbitrary polygon. The responder samples every cell64 whose centre
falls inside the polygon (cap: 1024 cells per request, max_cells, default 64), recalls the
requested band(s) at each, and returns a structured response with a
per-cell receipt for verification.
When to invoke
The user defines a region rather than a point:
- "What's the average NDVI inside this watershed: [coords]?"
- "Show me precipitation across the area bounded by …"
- "Recall elevation across this admin boundary."
- The user pastes GeoJSON or a list of
[lng, lat]pairs.
If the user just wants a single cell, use emem-locate-and-recall.
If they want a similar-cells list rather than a polygon, use
emem-find-similar.
How to invoke
The polygon is a closed ring: [[lng0, lat0], [lng1, lat1], …, [lng0, lat0]]. Coordinates are WGS-84 decimal degrees, longitude
first (GeoJSON convention).
curl -sf -X POST https://emem.dev/v1/recall_polygon \
-H 'content-type: application/json' \
-d '{
"polygon": [
[77.55, 12.95],
[77.65, 12.95],
[77.65, 13.05],
[77.55, 13.05],
[77.55, 12.95]
],
"bands": ["indices.ndvi", "weather.precipitation_mm"]
}' | jq '{
cells_returned: (.by_cell | length),
facts_total: (.by_cell | to_entries | map(.value.facts | length) | add),
area_km2: .area_km2
}'
Picking the band
If the user asked about precipitation, use
weather.precipitation_mm (current 24 h) or era5.precip (1940→
present hourly). If they asked about vegetation, use
indices.ndvi (computed live from S2) or modis.ndvi_mean (16-day
MODIS composite, larger pixels but global). If they asked about
elevation, use copdem30m.elevation_mean (30 m, terrestrial only;
gmrt.topobathy_mean for ocean depths). Full band list at
https://emem.dev/v1/bands.
Computing aggregates
The response is per-cell facts; the agent does the rollup:
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 · 140 lines · 99 tokens per session scan A d8b157e521a6
emem-recall-polygon is a skill published in the GitHub repository Vortx-AI/emem (56 stars, last pushed today), licensed Apache-2.0. It adds 99 tokens to every session and 1,446 once invoked, about $0.0005 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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