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 motion-team/creative-strategy-skills --skill review-auditgit clone --depth 1 https://github.com/motion-team/creative-strategy-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/skills/motion-team/creative-strategy-skills/review-audit)<a href="https://agentmods.dev/skills/motion-team/creative-strategy-skills/review-audit"><img src="https://agentmods.dev/badge/skills/motion-team/creative-strategy-skills/review-audit/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/motion-team/creative-strategy-skills/review-audit"><img src="https://agentmods.dev/badge/skills/motion-team/creative-strategy-skills/review-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- 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 | $0.00128 | $0.01768 |
| Opus 5 | $0.00064 | $0.00884 |
| Sonnet 5 | $0.00026 | $0.00354 |
| Haiku 4.5 | $0.00013 | $0.00177 |
Grade A, and why
review-audit 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Audit
This system mines positive customer reviews to extract the insights that make ad copy actually work — the real language, real pain, real moments, and real transformations that customers experienced. The output feeds directly into creative strategy and hook writing.
The goal is not to summarize reviews. The goal is to find the raw material for ads.
What You Need Before Starting
Reviews can be provided in any format:
- Pasted directly into the chat
- CSV or spreadsheet upload
- Copy/pasted from a document
If multiple products are present in the review set, identify them before beginning. All output is separated by product.
If the format is unclear or product attribution is ambiguous, ask before proceeding.
Step 1: Group by Product
If the brand sells multiple products, sort all reviews by product first. Every subsequent step runs separately per product.
If all reviews are for a single product, skip grouping and proceed.
Step 2: Score Review Quality (1–5)
Before analysis, score every review for quality. This determines what gets analyzed and what gets discarded.
| Score | What it looks like |
|---|---|
| 1 | Garbage — gibberish, swear words, 2–3 meaningless words, zero signal ("great product", "love it", "👍") |
| 2 | Low signal — very short, vague, no specific detail or emotion |
| 3 | Moderate — mentions the product, some specificity, but no vivid detail or emotional depth |
| 4 | High quality — specific, describes a real experience, references a before/after or a feeling |
| 5 | Gold — long, emotional, vivid, paragraph-level detail; the customer was so moved they wrote an essay about it |
Score 5 reviews are the priority. They contain the most usable language and the deepest insight.
Step 3: Filter
Discard all reviews scored 1. Do not include them in analysis.
Analyze scores 2–5, with emphasis on 4s and 5s. Low-scoring reviews (2–3) can contribute to pattern identification but should not be the source of pulled quotes.
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 · 206 lines · 128 tokens per session scan A 1dcdbd94c3bc
review-audit is a skill published in the GitHub repository motion-team/creative-strategy-skills (165 stars, last pushed 3mo ago), licensed MIT. It adds 128 tokens to every session and 1,768 once invoked, about $0.0006 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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