Borrowing it
Nothing to install: this file belongs to djbelieny/nova. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/djbelieny/nova/main/.claude/skills/reviews-testimonials/SKILL.mdgit clone --depth 1 https://github.com/djbelieny/novaWrote 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/djbelieny/nova/reviews-testimonials)<a href="https://agentmods.dev/skills/djbelieny/nova/reviews-testimonials"><img src="https://agentmods.dev/badge/skills/djbelieny/nova/reviews-testimonials/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/djbelieny/nova/reviews-testimonials"><img src="https://agentmods.dev/badge/skills/djbelieny/nova/reviews-testimonials.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 | $0.00066 | $0.02053 |
| Opus 5 | $0.00033 | $0.01026 |
| Sonnet 5 | $0.00013 | $0.00411 |
| Haiku 4.5 | $0.00007 | $0.00205 |
Grade A, and why
reviews-testimonials 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.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reviews & Testimonials Manager
This skill handles end-to-end review and testimonial management — from monitoring and responding to reviews, to collecting testimonials and repurposing them into marketing assets.
When to Use This Skill
- Responding to Google, Yelp, Facebook, or Trustpilot reviews
- Collecting customer testimonials (via email, SMS, or forms)
- Analyzing review sentiment and trends
- Repurposing reviews into marketing content (social posts, website copy, ads)
- Managing online reputation
- Creating review request campaigns
- Building testimonial pages or case studies
- Handling negative reviews and reputation crises
- Generating review response templates
- Monitoring competitor reviews for insights
Review Response Framework
Positive Reviews (4-5 stars)
Goal: Thank them, reinforce the positive, encourage return/referral.
Template:
[Name], this made my day! Really glad [specific thing they mentioned] hit the mark.
[Personal touch related to their experience].
We'd love to see you back — [soft next step: mention upcoming event, new product, or referral program].
— [Your name], [Business name]
Rules:
- Always mention something specific from their review (proves it's not copy-paste)
- Keep it warm and human — match their energy
- Never ask them to leave a review elsewhere in your response (platform violation)
- Respond within 24-48 hours
Negative Reviews (1-2 stars)
Goal: De-escalate publicly, resolve privately, show future readers you care.
Template:
[Name], I'm sorry about [specific issue]. That's not the experience we aim for, and I want to make this right.
I'd like to [specific resolution offer] — could you reach out to [direct contact: email/phone] so we can take care of this personally?
— [Your name], [Title]
Rules:
- NEVER argue, get defensive, or blame the customer publicly
- Acknowledge the specific issue (don't be vague)
- Take the conversation offline with a direct contact method
- Respond within 24 hours (faster = better for reputation)
- After resolving, it's OK to politely ask if they'd consider updating their review
- If the review is fake/spam, flag it on the platform with evidence
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 · 273 lines · 66 tokens per session scan A 336468890300
reviews-testimonials is a skill published in the GitHub repository djbelieny/nova (5 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 2,053 once invoked, about $0.0003 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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