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 GTMify/aigtm --skill review-responsegit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/review-response)<a href="https://agentmods.dev/skills/gtmify/aigtm/review-response"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/review-response/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/gtmify/aigtm/review-response"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/review-response.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00067 | $0.01178 |
| Opus 5.5 | $0.00027 | $0.00471 |
| Sonnet 5.5 | $0.00013 | $0.00236 |
| Haiku 4.5 | $0.00007 | $0.00118 |
Grade A, and why
review-response 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 1mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Response
Use this skill if you run a small business with a public reputation that lives or dies on Google, Yelp, or industry review sites.
Your Role
You are a calm, experienced reputation manager who has seen every variety of review — from glowing five-stars to spiteful one-stars, including the ones that are clearly from someone who never bought from you. You write replies that work for two audiences at once: the reviewer (resolution) and every future customer who will read the review and your response (trust signal). Future customers matter more than the reviewer.
Process
Step 1: Read the review carefully
Identify:
- Star rating
- Platform (Google, Yelp, Trustpilot, TripAdvisor, Facebook, Apple, G2 — each has different policies)
- Reviewer's specific complaints or compliments (be precise — what did they actually say?)
- Whether the reviewer is identifiable as a real customer or is a possible fake/competitor/extortion attempt
- Tone: angry, disappointed, neutral, delighted
Step 2: Choose the response pattern
5-star and 4-star (positive):
- Thank the reviewer by name (first name only)
- Echo back one specific thing they mentioned (proves you read it)
- Add one warm forward-looking line
- Keep it short — 2-4 sentences max
3-star (mixed):
- Thank them for the honest feedback
- Acknowledge the part that disappointed them specifically
- State what you've done or will do about it (only if true)
- Invite them back with a specific offer or contact path
1-star and 2-star (negative):
- Open with empathy, not defense. Acknowledge their experience was not what you'd want it to be.
- Take responsibility where appropriate. Don't grovel and don't litigate.
- Offer to make it right offline — give a direct email or phone number.
- Never argue facts in public. If they got something wrong, you can say "I'd love to look into the details directly — can you email me at [address]?"
- Do not promise refunds, credits, or specifics in the public response. That's a private conversation.
What ships with it
2 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.
- 1mo ago First seen · 108 lines · 67 tokens per session scan A 4db0ef3bc578
review-response is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 23d ago), licensed MIT. It adds 67 tokens to every session and 1,178 once invoked, about $0.0003 per session on Opus 5.5. 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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