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.
git clone --depth 1 https://github.com/Amey-Thakur/AI-SKILLSWrote 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/commands/amey-thakur/ai-skills/review-data-privacy)<a href="https://agentmods.dev/commands/amey-thakur/ai-skills/review-data-privacy"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/review-data-privacy/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/commands/amey-thakur/ai-skills/review-data-privacy"><img src="https://agentmods.dev/badge/commands/amey-thakur/ai-skills/review-data-privacy.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 | $0.00024 | $0.00256 |
| Opus 5 | $0.00012 | $0.00128 |
| Sonnet 5 | $0.00005 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
review-data-privacy 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
You were invoked as a slash command. The user's input:
$ARGUMENTS
Use that input to fill this prompt's variables (take the main content, topic, or task from it; ask only if a required value is missing and not supplied), then follow the prompt exactly.
Review privacy for:
{feature}
Context: {context}
Use privacy-by-design, data-minimization, right-to-erasure, and vendor-data-processing.
Produce:
- Every field collected and the purpose each serves.
- Fields that could be removed, derived, or aggregated instead.
- Where the data flows: stores, logs, analytics, and vendors.
- Retention per store, and whether deletion reaches all of them.
- Cross-border transfer implications.
- Consent requirements, if any.
- Findings ranked by risk to the person.
Rules: challenge every field against a stated purpose. Trace data into logs and analytics, which are where collection is unplanned. Verify deletion reaches derived copies. This is an engineering review, not legal advice; flag anything needing counsel.
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 · 38 lines · 24 tokens per session scan A 97304e5b94ad
review-data-privacy is a command published in the GitHub repository Amey-Thakur/AI-SKILLS (7 stars, last pushed 6d ago), licensed MIT. It adds 24 tokens to every session and 256 once invoked, about $0.0001 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 2026-09-06.
Other commands, from other repositories
preflight
Audit a build against App Review rejection triggers by app type before submitting.
privacy-check
Scan for required-reason API usage and verify PrivacyInfo.xcprivacy completeness before submission.
rejection-recovery
Diagnose an App Review rejection, draft a Resolution Center response, and plan the fix.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.