agent-auto-sci-geospatial

agent-auto-sci-geospatial is a skill for Codex from Lzy599775/agent-auto-sci-skills. It costs 119 tokens per session (516 once invoked), scanned A, original, MIT.

A guide for working with geographic and satellite data, including maps, location-based measurements, and spatial analysis. It covers common tools such as GeoPandas, which handles geographic data in Python.

In plain words
What is it for?
Use it to prepare vector, raster, network, and satellite data; calculate access, heat, and green-space exposure; assess spatial fairness; and create compliant research maps.
Why use it?
It helps prevent errors such as measuring distances in the wrong coordinate system or mixing incompatible map layers. It also encourages reproducible checks and maps that support evidence.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to prepare vector, raster, network, and satellite data; calculate access, heat, and green-space exposure; assess spatial fairness; and create compliant research maps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial
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 Lzy599775/agent-auto-sci-skills --skill agent-auto-sci-geospatial
Clone the repo
git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skills

Made for: Codex.

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 agent-auto-sci-geospatial

README.md
[![agentmods](https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial/github.svg)](https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial)
Your own site
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial/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 agent-auto-sci-geospatial

Your own site · 80×15
<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 516 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 $0.00119 $0.00516
Opus 5 $0.00060 $0.00258
Sonnet 5 $0.00024 $0.00103
Haiku 4.5 $0.00012 $0.00052

Measured 9d ago against content hash d5746c39d9bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

agent-auto-sci-geospatial 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 9d 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/agent-auto-sci-geospatial/SKILL.md · 45 lines

What it actually says

Agent Auto Sci Geospatial

Use this subskill for spatial data, maps, and exposure/accessibility workflows.

Fast Workflow

  1. Inventory all spatial layers and CRS.
  2. Separate vector, raster, network, remote-sensing, and tabular sources.
  3. Reproject before distance/area/network calculations.
  4. Define exposure, accessibility, availability, quality, and use separately.
  5. Run spatial analysis with reproducible code and sensitivity checks.
  6. Design maps for evidence, not decoration.
  7. For China locator maps, check standard map compliance before final figures.

Read references/geospatial_remote_sensing_workflows.md.

For full paper projects, this skill owns spatial data discovery, download planning, CRS/geometry audit, OSM/network accessibility, green/heat exposure indicators, LCZ/remote-sensing preprocessing, spatial equity metrics, map compliance, and handoff to analysis and figure-writing agents. It should produce explicit data paths, assumptions, and sensitivity checks.

For deeper K-Dense-style encapsulation:

  • references/k_dense_geospatial_mapping.md: how GeoPandas, geomaster, raster/vector, remote sensing, and spatial workflow skills are adapted.
  • references/sport_geography_spatial_playbook.md: sport park/facility accessibility, green/heat exposure, equity, LCZ, OSM, and China map-compliance playbook.
  • geomaster
  • geopandas
  • networkx
  • agent-auto-sci-data-viz
  • sport-geography-sci-writing
  • sport-geography-review-bibliometric

Must Not Do

  • Do not calculate distance or area in unprojected geographic CRS.
  • Do not equate proximity with actual use.
  • Do not treat LCZ, NDVI, or park area as interchangeable exposure metrics.
  • Do not use unofficial China locator maps for publication figures.
Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 45 lines · 119 tokens per session scan A d5746c39d9bc

Subscribe to this mod's changes

agent-auto-sci-geospatial is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 3d ago), licensed MIT. It adds 119 tokens to every session and 516 once invoked, about $0.0006 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 2026-08-31.

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