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 MartinPuli/createAnApp --skill app-compliance-auditorgit clone --depth 1 https://github.com/MartinPuli/createAnAppWrote 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/martinpuli/createanapp/app-compliance-auditor)<a href="https://agentmods.dev/skills/martinpuli/createanapp/app-compliance-auditor"><img src="https://agentmods.dev/badge/skills/martinpuli/createanapp/app-compliance-auditor/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/martinpuli/createanapp/app-compliance-auditor"><img src="https://agentmods.dev/badge/skills/martinpuli/createanapp/app-compliance-auditor.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 | $0.00084 | $0.00971 |
| Opus 5 | $0.00042 | $0.00485 |
| Sonnet 5 | $0.00017 | $0.00194 |
| Haiku 4.5 | $0.00008 | $0.00097 |
Grade A, and why
app-compliance-auditor 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Compliance Auditor
Produce a fact-based risk register and remediation plan. Do not present the result as legal advice or universal compliance; identify jurisdiction and counsel/professional handoffs.
Establish scope and facts
Inspect the exact source, binary/archive, dependencies, privacy manifest, entitlements, permissions, network traffic/endpoints, backend, website forms/cookies, App Store labels, policies, paywall, emails, ads, social assets, and review metadata.
Create one data map for app, SDKs, backend, vendors, and website. “Processed on device” and “collected by the developer or third party” are different; verify actual transmission.
Reconcile privacy
For every data type, map purpose, collection, sharing, tracking, linkage, storage, retention, deletion, security, permission, processor, and disclosure. Require agreement among source/binary behavior, manifest, consent UI, website, privacy policy, App Store privacy answers, and vendor contracts.
Adding analytics, ads, attribution, crash reporting, session replay, pixels, social SDKs, cloud sync, or AI APIs invalidates prior “data not collected” conclusions until re-audited.
Read references/trigger-map.md whenever the product adds a material data, AI, account, upload, sharing, content, territory, or regulated-domain capability.
Read references/conditional-requirements.md to map product triggers to Apple-facing controls before assigning a release verdict.
Audit AI truth
- State whether AI is a product feature, internal development aid, or marketing-production aid.
- Disclose material AI interactions where omission would mislead users.
- Do not call synthetic people, voices, customer work, testimonials, reviews, experts, or outcomes real.
- Obtain likeness/voice rights and label synthetic demonstrations appropriately.
- Do not send user content to an AI provider without a defined feature, lawful basis/consent where required, updated policy/labels, vendor review, retention controls, and deletion path.
- Substantiate performance/safety claims independently of generated copy.
What ships with it
4 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.
- 9d ago First seen · 61 lines · 84 tokens per session scan A 41e9202eeed4
app-compliance-auditor is a skill published in the GitHub repository MartinPuli/createAnApp (14 stars, last pushed 20d ago), licensed MIT. It adds 84 tokens to every session and 971 once invoked, about $0.0004 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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