overture-data

overture-data is a skill for Claude Code from opengeos/geoai-skills. It costs 42 tokens per session (1,003 once invoked), scanned A, original, MIT.

A tool for downloading Overture Maps data for a rectangular geographic area. Overture Maps is an open map dataset containing features such as buildings, roads, places, land, and water, and the result is saved as a GeoJSON or GeoPackage file.

In plain words
What is it for?
Use it to retrieve a selected feature type, such as buildings, roads, places, or water, by supplying four coordinates around the area of interest. The downloaded data can then be used in geographic analysis or mapping software.
Why use it?
It avoids manually finding, filtering, and exporting map features for a chosen area. It also checks that the area coordinates form a valid bounding box.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: positional $N argument.

Part of the geoai-skills plugin — 8 skills shipped together

Good fit Use it to retrieve a selected feature type, such as buildings, roads, places, or water, by supplying four coordinates around the area of interest. The downloaded data can then be used in geographic analysis or mapping software.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/opengeos/geoai-skills/overture-data
Install

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.

Any agent
npx skills add opengeos/geoai-skills --skill overture-data
Clone the repo
git clone --depth 1 https://github.com/opengeos/geoai-skills

Made for: Claude Code.

Or install geoai-skills, the plugin that ships this one along with the rest of its 8 skills.

Wrote 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.

agentmods badge for overture-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/opengeos/geoai-skills/overture-data/github.svg)](https://agentmods.dev/skills/opengeos/geoai-skills/overture-data)
Your own site
<a href="https://agentmods.dev/skills/opengeos/geoai-skills/overture-data"><img src="https://agentmods.dev/badge/skills/opengeos/geoai-skills/overture-data/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.

agentmods 80×15 button for overture-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/opengeos/geoai-skills/overture-data"><img src="https://agentmods.dev/badge/skills/opengeos/geoai-skills/overture-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,003 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01003
Opus 5 $0.00021 $0.00502
Sonnet 5 $0.00008 $0.00201
Haiku 4.5 $0.00004 $0.00100

Measured 11d ago against content hash ca80b4f1ec74, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

overture-data 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 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.

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.

skills/overture-data/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are helping the user download Overture Maps data using geoai.

Input: $@

Follow these steps in order.

Step 1 -- Parse arguments

Extract:

  • $0 or the first positional argument as the Overture data type
  • --bbox minx,miny,maxx,maxy as the bounding box (required)
  • --output FILE as the output file path (optional, default: ./<data_type>_overture.gpkg)

Valid Overture data types: address, building, building_part, division, division_area, division_boundary, place, segment, connector, infrastructure, land, land_cover, land_use, water

If the data type is not recognized, print the list of valid types and ask the user to pick one.

If the user provided natural language (e.g. "get buildings in downtown Nashville"), extract the data type and either infer or ask for the bounding box.

Step 2 -- Validate the bounding box

Confirm the bounding box has 4 numeric values:

  • minx < maxx and miny < maxy
  • Values within WGS84 range

If validation fails, report the issue and ask for corrected coordinates.

Step 3 -- Download the data

For building data specifically

python3 -c "
import geoai

gdf = geoai.download_overture_buildings(
    bbox=(MINX, MINY, MAXX, MAXY),
    output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"

For all other data types

python3 -c "
import geoai

gdf = geoai.get_overture_data(
    overture_type='DATA_TYPE',
    bbox=(MINX, MINY, MAXX, MAXY),
    output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"

Replace DATA_TYPE, MINX, MINY, MAXX, MAXY, and OUTPUT_PATH with actual values.

Step 4 -- Update state

Read the full file on GitHub · 131 lines

Changes

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.

  1. 11d ago First seen · 131 lines · 42 tokens per session scan A ca80b4f1ec74

Subscribe to this mod's changes

overture-data is a skill published in the GitHub repository opengeos/geoai-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,003 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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