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 Varnan-Tech/opendirectory --skill app-store-review-arbitragegit 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/app-store-review-arbitrage)<a href="https://agentmods.dev/skills/varnan-tech/opendirectory/app-store-review-arbitrage"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/app-store-review-arbitrage/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/varnan-tech/opendirectory/app-store-review-arbitrage"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/app-store-review-arbitrage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00036 | $0.02786 |
| Opus 5 | $0.00018 | $0.01393 |
| Sonnet 5 | $0.00007 | $0.00557 |
| Haiku 4.5 | $0.00004 | $0.00279 |
Grade A, and why
app-store-review-arbitrage 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 10d 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
app-store-review-arbitrage
Convert a competitor's App Store or Google Play URL into a one-session GTM brief: ranked complaint clusters, a broken promise map, landing page headlines, and ad copy directions — all sourced from verbatim reviews.
Critical Rules (read before Step 1)
These rules apply throughout all steps. Violating any of them fails Self-QA (Step 6).
- Every quote must be verbatim. No paraphrase, no grammar correction, no cleaning. Exact reviewer words only.
- No fabricated statistics. Do not write "40% faster" or "2× more reliable" unless a reviewer explicitly used similar language. The Self-QA step checks for uncited percentages.
- Cluster names must use reviewer language. Study the anti-pattern table in Step 3.
- Every headline and ad copy direction must cite its source cluster. Format:
[cluster: "cluster-name"]. - Section 2 is always present in the output — even when degraded. Never skip or omit it.
- No banned words in any generated copy: powerful, robust, seamless, innovative, game-changing, streamline, leverage, revolutionize, transform.
Step 1 — Parse Input and Detect Platform
Accept a natural language prompt containing one app URL. Extract the URL.
Platform detection:
apps.apple.com→ App Storeplay.google.com/store/apps/details?id=→ Google Play- Any other URL → stop and respond: "Please provide a direct App Store or Google Play URL. I can't analyse review data from other sources."
ID extraction (do this before calling the script):
| Platform | What to extract | How |
|---|---|---|
| App Store | Numeric app_id |
Digits after /id in the URL |
| App Store | country |
2-letter code after apps.apple.com/ (e.g., us, gb) |
| Google Play | package_name |
Value of id= query parameter |
Persist the extracted values — you will need them for the output filename in Step 7.
If product_context was provided in the user's prompt (what their own product does), store it — used to personalise copy in Step 5.
What ships with it
10 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.
- 10d ago First seen · 262 lines · 36 tokens per session scan A 51fbe10fe8b7
app-store-review-arbitrage is a skill published in the GitHub repository Varnan-Tech/opendirectory (637 stars, last pushed 24d ago), licensed MIT. It adds 36 tokens to every session and 2,786 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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