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 ComeOnOliver/skillshub --skill axiom-app-store-diaggit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote 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/comeonoliver/skillshub/axiom-app-store-diag)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/axiom-app-store-diag"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-app-store-diag/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/comeonoliver/skillshub/axiom-app-store-diag"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-app-store-diag.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 | $0.00038 | $0.10765 |
| Opus 5 | $0.00019 | $0.05382 |
| Sonnet 5 | $0.00008 | $0.02153 |
| Haiku 4.5 | $0.00004 | $0.01077 |
Grade A, and why
axiom-app-store-diag 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 8d 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 — 1,204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Rejection Diagnostics
Overview
Systematic App Store rejection diagnosis and remediation. 9 diagnostic patterns covering the most common rejection categories including technical, metadata, privacy, business, subjective, and safety violations.
Core principle Most App Store rejections fall into well-known categories. Reading the rejection message carefully and mapping to the correct guideline prevents the #1 mistake: fixing the wrong thing and getting rejected again for the same reason.
Most developers waste 1-2 weeks on rejection cycles because they skim the rejection message, assume the cause, and "fix" something that wasn't the problem. This skill provides systematic diagnosis from rejection message to targeted fix.
Red Flags — Suspect Submission Issue
If you see ANY of these, suspect a submission issue and use this skill:
-
Rejection message cites a specific guideline number
-
"Binary Rejected" without clear guideline (technical gate failure)
-
Same app rejected multiple times for different reasons
-
"Metadata Rejected" (no code change needed)
-
Rejection mentions "privacy" or "data collection"
-
Rejection mentions "login" or "authentication"
-
Reviewer asks for demo account or more information
-
❌ FORBIDDEN "The reviewer is wrong, let's just resubmit"
- Re-read the rejection. App Review is right 95% of the time.
- Resubmitting without changes wastes 3-7 days per cycle.
- If you genuinely disagree, use the appeal process (Pattern 7).
Mandatory First Steps
ALWAYS do these BEFORE changing any code:
- Read the FULL rejection message — Don't skim. Copy the exact text. Note every guideline number cited.
- Identify rejection type:
- "App Rejected" → Guideline violation, code/content fix needed
- "Metadata Rejected" → ASC metadata issue, no build needed
- "Binary Rejected" → Technical gate (SDK, manifest, encryption)
- "Removed from Sale" → Post-approval enforcement
- Check the specific guideline — Look up the exact number in app-store-ref
- Screenshot the rejection — Save for team communication and appeal reference
- Check App Review messages in ASC — Sometimes they ask for information, not reject
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.
- 8d ago First seen · 1,204 lines · 38 tokens per session scan E 4d62b7ef3a9c
axiom-app-store-diag is a skill published in the GitHub repository ComeOnOliver/skillshub (63 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 10,765 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 2026-09-03.
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