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 anhnguyen0905/codex-mcp --skill asogit clone --depth 1 https://github.com/anhnguyen0905/codex-mcpWrote 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/anhnguyen0905/codex-mcp/aso)<a href="https://agentmods.dev/skills/anhnguyen0905/codex-mcp/aso"><img src="https://agentmods.dev/badge/skills/anhnguyen0905/codex-mcp/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/anhnguyen0905/codex-mcp/aso"><img src="https://agentmods.dev/badge/skills/anhnguyen0905/codex-mcp/aso.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 | $0.00090 | $0.01211 |
| Opus 5 | $0.00045 | $0.00606 |
| Sonnet 5 | $0.00018 | $0.00242 |
| Haiku 4.5 | $0.00009 | $0.00121 |
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 9d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASO (store listing discoverability and conversion)
The two jobs, kept separate
installs = impressions × store-page CVR (impression → install)
impressions ≈ search/browse/referral traffic won by relevance and ranking
Metadata mostly moves impressions — which queries you can appear for at all. Creatives mostly move CVR — whether that traffic converts. Diagnose which half moved first: installs falling with CVR flat is a traffic problem, installs falling with impressions flat is a page problem. Paid traffic lands on the same page, so a CVR win compounds across all UA spend.
Metadata fields and what each affects
- Apple App Store: app name, subtitle and the character-limited keyword field are indexed for search; the description is not, so it is purely a conversion asset. No repetition across fields, no wasted comma spacing, no plural duplicates. IAP and developer names also carry indexing weight.
- Google Play: title, short description and long description are all indexed, so the long description carries real search weight — but users read it, so stuffing costs conversion; natural repetition of the priority term beats a keyword list.
- Both: title and the first line of subtitle/short description are the highest-leverage text — what a browsing user reads before deciding.
Keyword research: relevance before volume
Build the term set from real query sources — store autosuggest, competitor listings, search-term reports from paid app campaigns, support tickets, category vocabulary. Triage each term on relevance to what the app actually does, estimated volume, and difficulty (who ranks now, how entrenched). Prefer ranking well for a moderate-volume term you genuinely satisfy over placing low on a head term: irrelevant traffic depresses CVR and the ranking signals that follow it. Track rank and installs per term — rank alone hides worthless traffic.
Creatives are the real conversion lever
The icon appears in every impression, so it gates result-list CTR as well as page CVR and must read at thumbnail size. Screenshots: the first one or two are all most users see — lead with the single strongest value message, text legible small, promise consistent with the product or you buy churn instead of retention. A preview/promo video can lower CVR as easily as raise it depending on its first seconds; test it, never assume.
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.
- 9d ago First seen · 93 lines · 90 tokens per session scan A e4b7e6119dae
aso is a skill published in the GitHub repository anhnguyen0905/codex-mcp (3 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 1,211 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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