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 OneWave-AI/claude-skills --skill review-response-writergit clone --depth 1 https://github.com/OneWave-AI/claude-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/onewave-ai/claude-skills/review-response-writer)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/review-response-writer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/review-response-writer/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/onewave-ai/claude-skills/review-response-writer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/review-response-writer.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 | $0.00069 | $0.00791 |
| Opus 5 | $0.00034 | $0.00396 |
| Sonnet 5 | $0.00014 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
review-response-writer 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 9d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Response Writer
The response to a bad review is not for the reviewer -- it is for every prospect who reads it for the next five years. Input: the review text (pasted, exported, or fetched from the profile), and on first run a few existing responses or a note on how the owner talks. Output: responses ready to post, plus escalation flags for the ones that need more than words.
Workflow
- Learn the voice. From past responses or the owner's description: warmth level, formality, sign-off style, whether they use names. Small-business responses that sound like the actual owner outperform corporate-polished ones -- keep contractions, keep personality.
- Triage each review:
GLOW(4-5 stars) -- thank specifically (echo the detail they praised, never generic), reinforce the service mentioned (it is searchable text), invite them back.LEGITIMATE COMPLAINT-- acknowledge the specific failure without excuses, state the fix made, take it offline with a real contact, never argue. No coupons in public (it trains complaint-for-discount).UNFAIR/MISTAKEN-- correct the record factually and briefly ("Our records show we honored the quoted price of...") while staying gracious; readers can tell who is being reasonable.SUSPECTED FAKE(no record of the customer, competitor patterns) -- respond once, neutrally ("We have no record of serving you -- contact us and we'll make it right"), and output the platform's removal-request steps with the policy grounds.ESCALATE-- anything mentioning injury, illness, discrimination, legal threats, or an employee by name in an accusation: draft nothing final; flag for the owner and, where serious, counsel, with a holding-pattern response option.
- Draft. Responses under 100 words for positives, under 150 for negatives. Vary structure across a batch -- ten identical-template responses read as a bot and undo the effort.
- Batch report. For a backlog: respond oldest-negative first (unanswered negatives do compounding damage), then recent positives. Note patterns worth fixing upstream -- three reviews mentioning wait times is an operations finding, not a writing task.
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.
- 9d ago First seen · 39 lines · 69 tokens per session scan A 85dd884a62db
review-response-writer is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 791 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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