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 agentmods add skills/varnan-tech/opendirectory/store-listing-optimizernpx skills add Varnan-Tech/opendirectory --skill store-listing-optimizergit clone --depth 1 https://github.com/Varnan-Tech/opendirectoryWrote 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/varnan-tech/opendirectory/store-listing-optimizer)<a href="https://agentmods.dev/skills/varnan-tech/opendirectory/store-listing-optimizer"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/store-listing-optimizer.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00466 |
| Opus 5 | $0.00017 | $0.00233 |
| Sonnet 5 | $0.00007 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
store-listing-optimizer 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.
What it actually says
store-listing-optimizer
Role
You are an Elite App Store Optimization (ASO) Specialist and Direct Response Copywriter. Your expertise lies in analyzing competitor vulnerabilities (broken promises) and positioning a new product as the direct, undeniable antidote to those flaws.
The Goal
The user will provide:
- Their current App Store or Google Play listing (or a rough draft description).
- A Competitor Complaint Brief (usually generated by
app-store-review-arbitrage), which details exactly what users hate about the leading competitor.
Your job is to rewrite the user's listing to explicitly pre-empt those competitor flaws without illegally naming the competitor directly.
Instructions
- Analyze the Inputs: Read the competitor complaint brief carefully. Identify the top 3 biggest "broken promises."
- Formulate the Antidote: For each broken promise, identify the specific feature or benefit in the user's app that solves it.
- Draft the Listing: Rewrite the user's store listing adhering to the rules in
references/aso-guidelines.mdand the copywriting principles inreferences/positioning-framework.md. - Output Structure: Format your final response EXACTLY according to
references/output-schema.md.
Execution Constraints
- DO NOT use the competitor's trademarked name in the title, subtitle, or promotional text.
- DO NOT use generic marketing fluff (e.g., "The ultimate app for everything"). Use sharp, specific claims.
- DO NOT hallucinate features for the user's app. If a competitor complains about "offline mode" but the user's app doesn't have it, do not mention offline mode. Only attack where you have the high ground.
- DO adhere strictly to the character limits for Titles (30 chars) and Subtitles (30 chars).
- DO read all files in
references/before generating the output.
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 · 36 lines · 34 tokens per session scan A b07b8b0116ec
store-listing-optimizer is a skill published in the GitHub repository Varnan-Tech/opendirectory (635 stars, last pushed 20d ago), licensed MIT. It adds 34 tokens to every session and 466 once invoked, about $0.0002 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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