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 kumaran-is/claude-code-onboarding --skill app-store-optimizationgit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/kumaran-is/claude-code-onboarding/app-store-optimization)<a href="https://agentmods.dev/skills/kumaran-is/claude-code-onboarding/app-store-optimization"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/app-store-optimization/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/kumaran-is/claude-code-onboarding/app-store-optimization"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/app-store-optimization.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 | $0.00130 | $0.01938 |
| Opus 5 | $0.00065 | $0.00969 |
| Sonnet 5 | $0.00026 | $0.00388 |
| Haiku 4.5 | $0.00013 | $0.00194 |
Grade A, and why
app-store-optimization 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 6d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Optimization (ASO)
Iron Law
NO STORE SUBMISSION WITHOUT COMPLETING ASO HEALTH CHECK FIRST — TARGET SCORE ≥ 70/100
Run aso_scorer.py before any first App Store or Play Store submission. A low score wastes review queue time and launch momentum.
When to Use
- Before first App Store or Play Store submission
- Before each major update (new features, new screenshots, new markets)
- When ratings drop — use
review_analyzer.pyto find root causes - When downloads plateau — keyword and competitor audit needed
- Before expanding to new markets — localization ROI assessment
Platform Character Limits (enforced by metadata_optimizer.py)
| Field | Apple App Store | Google Play |
|---|---|---|
| Title | 30 chars | 50 chars |
| Subtitle / Short description | 30 chars (subtitle) | 80 chars |
| Promotional text | 170 chars (editable without update) | — |
| Full description | 4,000 chars | 4,000 chars |
| Keyword field | 100 chars (comma-separated, no spaces, no plurals, no duplicates) | — (extracted from title + description) |
| What's New | 4,000 chars | — |
Workflow
Step 1 — Keyword Research
Use keyword_analyzer.py to:
- Score candidate keywords by volume/competition/relevance
- Find long-tail opportunities (3–4 word phrases, lower competition)
- Identify which competitor keywords have gaps
Output: Ranked keyword list — primary (title/subtitle), secondary (keyword field), long-tail (description)
Step 2 — Metadata Optimization
Use metadata_optimizer.py to:
- Generate platform-specific title within character limit
- Write subtitle (Apple) / short description (Google)
- Craft conversion-focused full description
- Maximize Apple keyword field (100 chars, no wasted characters)
- Validate all character limits before writing
Apple keyword field rules: No spaces after commas, no plurals if singular exists, no words already in title, no competitor names.
Step 3 — Competitor Analysis
Use competitor_analyzer.py to:
- Extract top 10 competitor keyword strategies
- Identify visual asset approaches (icon style, screenshot structure)
- Find keyword gaps — terms they rank for that you don't target
- Spot positioning opportunities
What ships with it
8 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.
- 6d ago First seen · 182 lines · 0 tokens per session scan A ecc365dbe11f
app-store-optimization is a skill published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 130 tokens to every session and 1,938 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 2026-09-03.
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