applyra-aso

applyra-aso is a skill for Codex from lucholabs/applyra-aso-skill. It costs 97 tokens per session (3,969 once invoked), scanned A, original, MIT.

An App Store Optimization (ASO) workflow for iOS and Android apps. ASO means improving an app's store listing so the right people can find it and choose to install it.

In plain words
What is it for?
Use it for keyword research, rank tracking, competitor-gap analysis, store metadata, localized listings, screenshot planning, conversion experiments, and ASO audits.
Why use it?
It helps replace guesswork about app-store keywords, competitors, listing text, screenshots, and localization with product information and Applyra data.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Claude Code; mentions AGENTS.md; mentions Codex.

Good fit Use it for keyword research, rank tracking, competitor-gap analysis, store metadata, localized listings, screenshot planning, conversion experiments, and ASO audits.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lucholabs/applyra-aso-skill/applyra-aso
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 lucholabs/applyra-aso-skill --skill applyra-aso
Clone the repo
git clone --depth 1 https://github.com/lucholabs/applyra-aso-skill

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 applyra-aso

README.md
[![agentmods](https://agentmods.dev/badge/skills/lucholabs/applyra-aso-skill/applyra-aso/github.svg)](https://agentmods.dev/skills/lucholabs/applyra-aso-skill/applyra-aso)
Your own site
<a href="https://agentmods.dev/skills/lucholabs/applyra-aso-skill/applyra-aso"><img src="https://agentmods.dev/badge/skills/lucholabs/applyra-aso-skill/applyra-aso/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 applyra-aso

Your own site · 80×15
<a href="https://agentmods.dev/skills/lucholabs/applyra-aso-skill/applyra-aso"><img src="https://agentmods.dev/badge/skills/lucholabs/applyra-aso-skill/applyra-aso.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,969 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 $0.00097 $0.03969
Opus 5 $0.00048 $0.01985
Sonnet 5 $0.00019 $0.00794
Haiku 4.5 $0.00010 $0.00397

Measured 11d ago against content hash 786b9ee618a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

applyra-aso 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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/check_setup.sh, scripts/tests/test_validate_metadata.py, scripts/validate_metadata.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

applyra-aso/SKILL.md · 392 lines

How it starts

The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Applyra ASO

Run evidence-led App Store Optimization for Apple App Store and Google Play. Use the repository as product truth, Applyra as the ASO data source, and deterministic validation before proposing or writing metadata.

Portability and path resolution

This skill is portable across Codex, Claude Code, and other Agent Skills-compatible clients. Resolve skill-root as the directory containing this SKILL.md. Every relative reference, script, and asset path in this document is relative to skill-root; never assume the skill is installed under a specific home directory or repository path.

Respond and write reports in the user's language unless the repository establishes another language. Preserve store-facing copy in each target locale.

Core outcome

Produce an ASO system that is:

  1. True to the product — every claim maps to an implemented, available feature.
  2. Market-specific — every keyword decision is tied to store, country, and locale.
  3. Data-backed — Applyra metrics are quoted exactly and never invented.
  4. Conversion-aware — metadata and creatives answer the searcher's intent.
  5. Policy-safe — no competitor trademarks, false rankings, unsupported claims, or misleading assets.
  6. Reproducible — baseline, decisions, diffs, validation, and follow-up measurements are written to disk.
  7. Non-destructive by default — research first; Applyra mutations and store publication require explicit authorization.
  8. Data-minimized — raw private payloads stay local; public reports contain only normalized, redacted evidence.

Read before acting

  1. Read the root AGENTS.md and any nested instructions that apply to files you may change.
  2. Read the relevant reference files for the requested phase:
    • Applyra usage: references/01-applyra-mcp.md
    • Product truth: references/02-app-context.md
    • Keyword research: references/03-keyword-research.md
    • Competitors: references/04-competitors.md
    • Apple metadata: references/05-apple-metadata.md
    • Google Play metadata: references/06-google-play-metadata.md
    • Localization: references/07-localization.md
    • Creatives and experiments: references/08-creatives-conversion.md
    • Measurement: references/09-measurement-experiments.md
    • Repository changes: references/10-repository-integration.md
    • Compliance: references/11-safety-compliance.md
    • Required outputs: references/12-output-contract.md
  3. Run <skill-root>/scripts/check_setup.sh when the Applyra connection, Node version, agent setup, or validator availability is uncertain.

Read the full file on GitHub · 392 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 · 392 lines · 97 tokens per session scan A 786b9ee618a5

Subscribe to this mod's changes

applyra-aso is a skill published in the GitHub repository lucholabs/applyra-aso-skill (2 stars, last pushed 18d ago), licensed MIT. It adds 97 tokens to every session and 3,969 once invoked, about $0.0005 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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