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 cosmicstack-labs/mercury-agent-skills --skill review-respondergit clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-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/cosmicstack-labs/mercury-agent-skills/review-responder)<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/review-responder"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/review-responder/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/cosmicstack-labs/mercury-agent-skills/review-responder"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/review-responder.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 | $0.00043 | $0.01831 |
| Opus 5 | $0.00022 | $0.00915 |
| Sonnet 5 | $0.00009 | $0.00366 |
| Haiku 4.5 | $0.00004 | $0.00183 |
Grade A, and why
review-responder 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 8d 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 Responder
Core Principles
Online reviews are the digital word-of-mouth that makes or breaks a shop or restaurant. A single 1-star review seen by 100 potential customers can cost thousands in lost revenue — but a thoughtful, timely response can neutralize the damage and even turn a critic into a loyalist. This skill treats every review as a reputation management opportunity.
The Four Review Response Rules
- Speed matters. Respond within 24 hours (ideally < 4 hours for negative reviews). A fast response signals you care.
- Personalize, don't templatize. Generic "Thank you for your feedback" responses feel dismissive. Reference specifics.
- Take negative conversations offline. Public arguments never end well. Address, apologize, and invite a private conversation.
- Amplify the positive. A great review is marketing content. Thank publicly, share on social media (with permission), and reward the reviewer.
Skill Workflow
Step 1 — Connect Review Sources
Ask the user which platforms they use. Common sources:
- Google Business Profile (most important — appears in Search & Maps)
- Yelp (critical for restaurants in the US)
- TripAdvisor (key for tourist-facing businesses)
- Facebook Reviews
- DoorDash / UberEats (for delivery feedback)
- OpenTable (for reservations)
For each platform, collect:
- Rating (1-5)
- Review text
- Date of review
- Reviewer name
- Any business reply already posted
Step 2 — Triage by Sentiment & Urgency
Classify each review:
| Category | Rating | Sentiment | Response Priority |
|---|---|---|---|
| 🔴 Crisis | 1 star | Angry, accuses of health/safety issue, public figure | Immediate (< 1 hr) |
| 🟠 Critical | 1-2 stars | Significant complaint, specific issue, or repeat detractor | Same day |
| 🟡 Mixed | 3 stars | Balanced feedback — some praise, some criticism | Within 24 hrs |
| 🟢 Positive | 4-5 stars | Happy customer, specific praise | Within 48 hrs |
| ⚪ Neutral | Any | Factual, no emotion (e.g., "They exist") | Low priority |
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.
- 8d ago First seen · 206 lines · 43 tokens per session scan A d5adc209809b
review-responder is a skill published in the GitHub repository cosmicstack-labs/mercury-agent-skills (471 stars, last pushed 17d ago), licensed MIT. It adds 43 tokens to every session and 1,831 once invoked, about $0.0002 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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