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 Lzy599775/agent-auto-sci-skills --skill agent-auto-sci-geospatialgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/agent-auto-sci-geospatial)<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.
<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>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 | $0.00119 | $0.00516 |
| Opus 5 | $0.00060 | $0.00258 |
| Sonnet 5 | $0.00024 | $0.00103 |
| Haiku 4.5 | $0.00012 | $0.00052 |
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.
What it actually says
Agent Auto Sci Geospatial
Use this subskill for spatial data, maps, and exposure/accessibility workflows.
Fast Workflow
- Inventory all spatial layers and CRS.
- Separate vector, raster, network, remote-sensing, and tabular sources.
- Reproject before distance/area/network calculations.
- Define exposure, accessibility, availability, quality, and use separately.
- Run spatial analysis with reproducible code and sensitivity checks.
- Design maps for evidence, not decoration.
- 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.
Related Helper Skills
geomastergeopandasnetworkxagent-auto-sci-data-vizsport-geography-sci-writingsport-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.
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.
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.
- 9d ago First seen · 45 lines · 119 tokens per session scan A d5746c39d9bc
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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