optimizing-geo

optimizing-geo is a skill for Claude Code, Codex from ofcskn/mobile-automation-plugin. It costs 117 tokens per session (619 once invoked), scanned A, original, MIT.

A process for helping an app appear in answers from AI assistants such as ChatGPT, Claude, Gemini, and Perplexity.

In plain words
What is it for?
Use it to create a consistent app description, generate JSON-LD schema, prepare launch copy, and plan actions to improve AI visibility.
Why use it?
It organizes the factual descriptions, structured page data, and public references that AI systems may use when recommending apps.

Skill for Claude CodeCodex

Part of the automobileapp plugin — 11 skills, 17 commands, 9 agents shipped together

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/ofcskn/mobile-automation-plugin/optimizing-geo
Any agent
npx skills add ofcskn/mobile-automation-plugin --skill optimizing-geo
Clone the repo
git clone --depth 1 https://github.com/ofcskn/mobile-automation-plugin

Made for: Claude Code, Codex.

Or install automobileapp, the plugin that ships this one along with the rest of its 11 skills, 17 commands, 9 agents.

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 optimizing-geo

README.md
[![agentmods](https://agentmods.dev/badge/skills/ofcskn/mobile-automation-plugin/optimizing-geo.svg)](https://agentmods.dev/skills/ofcskn/mobile-automation-plugin/optimizing-geo)
Your own site
<a href="https://agentmods.dev/skills/ofcskn/mobile-automation-plugin/optimizing-geo"><img src="https://agentmods.dev/badge/skills/ofcskn/mobile-automation-plugin/optimizing-geo.svg" alt="Measured on agentmods" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 619 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00117 $0.00619
Opus 5 $0.00059 $0.00309
Sonnet 5 $0.00023 $0.00124
Haiku 4.5 $0.00012 $0.00062

Measured 4d ago against content hash 0d7fbb8304e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

optimizing-geo 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 4d 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/optimizing-geo/SKILL.md · 64 lines

What it actually says

Optimizing GEO (AI Discoverability)

When to use

Request Action
"GEO optimization" Run geo-optimizer.lens.md
"schema markup" Generate JSON-LD from lens output
"ProductHunt launch" Use lens tagline + description
"entity anchor" Extract from lens output, save to config
"AI visibility" Run lens + follow 30-day action plan

What GEO does

AI tools cite apps that have:

  1. Structured, factual descriptions that AI can extract precisely
  2. Consistent entity name across ALL web surfaces
  3. Coverage on authoritative tech sites and review platforms
  4. Schema markup on the app's landing page
  5. Authentic community presence (Reddit, ProductHunt, reviews)

Entity anchor rule

The entity anchor sentence is the canonical one-sentence definition:

"[App Name] is a [category] app for iOS and Android designed for [user persona],
featuring [key differentiator]."

Use this EXACT wording everywhere. Never change it for 6+ months. Save to .msd/config/{appId}.config.jsongeo.entityAnchor.

Process

  1. Complete ASO optimization first (skills/optimizing-aso-seo)
  2. Load lenses/geo-optimizer.lens.md
  3. Fill parameters: app name, category, ASO description, user persona, differentiator, store URLs
  4. Review entity anchor sentence (most important output — locked in for 6 months)
  5. Save JSON-LD schema to assets/{appId}/schema.json
  6. Save entity anchor to .msd/config/{appId}.config.jsongeo.entityAnchor
  7. Follow the 30-day GEO action plan from lens output

30-day action plan summary

Day Action
1 Add JSON-LD schema to landing page
2 Launch on ProductHunt with GEO description
7 Submit to AppAdvice, 9to5Mac, AndroidPolice
14 Post authentic use case in relevant subreddits
21 Build in public thread with factual feature list
30 First AI visibility audit (test all 4 AI tools)
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. 4d ago First seen · 64 lines · 117 tokens per session scan A 0d7fbb8304e0

Subscribe to this mod's changes

optimizing-geo is a skill published in the GitHub repository ofcskn/mobile-automation-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 117 tokens to every session and 619 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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