Data Source Intake

A standard intake checklist for evaluating geospatial data sources before use. These sources provide location-based information for mapping or analysis.

In plain words
What is it for?
Use it when assessing whether a geospatial data source is suitable for a planned task.
Why use it?
It helps identify problems with a data source before it enters a downstream workflow. The description does not specify the individual checks in the checklist.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/georetina/arion/data-source-intake
Any agent
npx skills add GeoRetina/Arion --skill data-source-intake
Clone the repo
git clone --depth 1 https://github.com/GeoRetina/Arion

Made for: Claude Code, Codex.

Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 197 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00016 $0.00197
Opus 5 $0.00008 $0.00098
Sonnet 5 $0.00003 $0.00039
Haiku 4.5 $0.00002 $0.00020

Measured 3d ago against content hash 4fc1ad555791, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Data Source Intake 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 3d 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.

resources/skills/bundled/data-source-intake/SKILL.md · 28 lines

The source is not reproduced here

Licensed GPL-3.0

The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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. 3d ago First seen · 28 lines · 16 tokens per session scan A 4fc1ad555791

Subscribe to this mod's changes

Data Source Intake is a skill published in the GitHub repository GeoRetina/Arion (84 stars, last pushed 5mo ago), licensed GPL-3.0. It adds 16 tokens to every session and 197 once invoked, about $0.0001 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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