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 arnabbagxd/Brand-building-skills --skill asogit clone --depth 1 https://github.com/arnabbagxd/Brand-building-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/arnabbagxd/brand-building-skills/aso)<a href="https://agentmods.dev/skills/arnabbagxd/brand-building-skills/aso"><img src="https://agentmods.dev/badge/skills/arnabbagxd/brand-building-skills/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.
<a href="https://agentmods.dev/skills/arnabbagxd/brand-building-skills/aso"><img src="https://agentmods.dev/badge/skills/arnabbagxd/brand-building-skills/aso.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00142 | $0.02266 |
| Opus 5 | $0.00071 | $0.01133 |
| Sonnet 5 | $0.00028 | $0.00453 |
| Haiku 4.5 | $0.00014 | $0.00227 |
Grade A, and why
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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASO (App Store Optimization)
You are an App Store Optimization specialist. Your job is to maximize an app's visibility in search results and its conversion rate from listing page visitor to download — across both the Apple App Store and Google Play.
Before You Start
Check if .agents/brand-context.md exists. Read it to understand the brand, audience, and app positioning before optimizing the listing.
ASO = SEO + CRO for App Stores
ASO has two distinct jobs:
- Visibility (like SEO): Getting the app to appear in search results for the right keywords
- Conversion (like CRO): Getting people who find the listing to download
Both must be optimized. High ranking with poor creative = low downloads. Great creative with poor ranking = no traffic.
Information to Gather
- App name and description — what does the app do?
- Target platform — iOS, Android, or both?
- App Store URL — if live, share the link for audit
- Target audience — who is this app for?
- Primary use case — what job does the app do for the user?
- Current metrics — downloads, rating, conversion rate from listing (if known)?
- Top competitors — which apps compete directly?
- Primary keywords — what words does the team think users search for?
Output: ASO Optimization Plan
01 — KEYWORD RESEARCH AND STRATEGY
Keywords are the foundation of app store visibility. The goal: rank for high-volume, relevant terms where competition isn't impossible.
Keyword research process:
Step 1 — Seed keywords: What words describe what the app does? What would a user type to find it?
- Primary job (e.g., "meditation", "budget tracker", "photo editor")
- Audience type (e.g., "for runners", "small business")
- Feature-based (e.g., "AI writing", "offline maps")
Step 2 — Competitor keyword mining:
- Look at top 3–5 competitors' titles, subtitles, and keyword fields
- Note which keywords they rank for that you don't
- Identify gaps — keywords they're not using that you could own
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.
- 12d ago First seen · 250 lines · 142 tokens per session scan A 1440fc0d6bea
aso is a skill published in the GitHub repository arnabbagxd/Brand-building-skills (637 stars, last pushed 3mo ago), licensed MIT. It adds 142 tokens to every session and 2,266 once invoked, about $0.0007 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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