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-urbangit 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-urban)<a href="https://agentmods.dev/skills/vortx-ai/emem/emem-urban"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-urban/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-urban"><img src="https://agentmods.dev/badge/skills/vortx-ai/emem/emem-urban.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 | $0.00127 | $0.01915 |
| Opus 5.5 | $0.00051 | $0.00766 |
| Sonnet 5.5 | $0.00025 | $0.00383 |
| Haiku 4.5 | $0.00013 | $0.00192 |
Grade A, and why
emem-urban 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 28d 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.
curl -sf -X POST https://emem.dev/v1/recall -H 'content-type: application/json' \ How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
emem-urban
Network use. The commands in this skill send and receive JSON (and, where a step says so, an image or a raster file) to
https://emem.devonly, the service the plugin's MCP server connects to. Nothing they download is executed. The files underscripts/read local files and make no network calls.
emem answers per cell. A cell is about 10 m across, much smaller than most urban sources, so the first question for every urban number is what it was measured over. This skill gives the answer per band and shows how to go from cells to an area without overstating coverage.
The bands
| Band | Unit | Measured over | Source | Watch for |
|---|---|---|---|---|
overture.buildings.count |
count | the cell's own bbox: buildings whose centroid falls inside | Overture buildings, release named in derivation.args |
0 can mean unmapped; coverage is uneven outside NA and EU |
overture.places.count |
count | points inside the cell bbox | Overture places | POIs, not people or floor area |
overture.transportation.road_length_m |
m | carriageway segments (class motorway through service) clipped to the cell bbox; footways, paths, rail and water are not counted | Overture transportation | a 10 m cell off the street reads 0 |
overture.transportation.road_bearing_deg |
deg_axial, 0 to 180 from north | the nearest carriageway segment within 50 m; distance, class and the row groups read in derivation.args |
Overture transportation | an Absence when no carriageway is within 50 m; a footway or plaza edge does not count |
population |
people_per_km2 | one 1 km pixel, sampled at the cell | WorldPop UNadj 1 km, 2020 | the pixel's density, not people in the cell |
indices.ndbi |
ratio, -1 to 1 | one 10 m Sentinel-2 pixel, (B11 - B08) / (B11 + B08) | Sentinel-2 L2A, scene id in args | read surface_class: a cloud-shadow pixel is not built-up evidence |
esa_worldcover.lc_2021 |
class | one 10 m pixel | ESA WorldCover v200, 2021 | 50 is built-up |
surface_water.recurrence |
percent | one 30 m pixel | JRC Global Surface Water 1984 to 2021 | 60 N to 60 S only |
nightlights.dmsp_ols_avg_dn |
DN 0 to 63 | DMSP-OLS stable lights | DMSP-OLS V4, 1992 to 2013 | saturates at 63 in any city core; no VIIRS |
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.
- 28d ago Changed · +4 lines febd6568b8d9
- 29d ago First seen · 100 lines · 127 tokens per session scan A 9efc9c1339a3
emem-urban is a skill published in the GitHub repository Vortx-AI/emem (76 stars, last pushed today), licensed Apache-2.0. It adds 127 tokens to every session and 1,915 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-29.
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