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 acaprino/daodan --skill reply-to-customer-reviewgit clone --depth 1 https://github.com/acaprino/daodanWrote 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/acaprino/daodan/reply-to-customer-review)<a href="https://agentmods.dev/skills/acaprino/daodan/reply-to-customer-review"><img src="https://agentmods.dev/badge/skills/acaprino/daodan/reply-to-customer-review/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/acaprino/daodan/reply-to-customer-review"><img src="https://agentmods.dev/badge/skills/acaprino/daodan/reply-to-customer-review.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 | $0.00071 | $0.01719 |
| Opus 5.5 | $0.00028 | $0.00688 |
| Sonnet 5.5 | $0.00014 | $0.00344 |
| Haiku 4.5 | $0.00007 | $0.00172 |
Grade A, and why
reply-to-customer-review 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 16d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-reply-method — 88% identical, 65 lines differ
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reply to Customer Review
Generate a professional, empathetic response to a customer review. Analyze the review, craft an adaptive response, and provide operational suggestions.
Input
The user pastes a customer review (or invokes via /digital-marketing:reply-to-customer-review). Optional parameters:
- --brand "Name" -- business name to use in the response
- --tone formal|friendly|casual -- override tone (default: professional-empathetic)
- --lang XX -- force output language (default: same as review)
- --sector hospitality|ecommerce|auto -- force sector (default: auto-detect)
If invoked without arguments, prompt the user to paste a review.
Process
Execute all four steps inline. Do NOT spawn subagents.
Step 1: Analysis
Analyze the review and determine:
Language -- identify the review language. For mixed-language reviews, identify the dominant language and note secondary languages.
Sentiment -- classify as one of:
- POSITIVE -- satisfied customer, praise, recommendation
- NEUTRAL -- factual, neither praise nor complaint
- NEGATIVE -- dissatisfaction, complaint, criticism
- MIXED -- contains both positive and negative elements
Key Points -- extract the specific topics mentioned (e.g., cleanliness, shipping speed, product quality, staff attitude, app stability, price, location).
Severity (negative/mixed reviews only) -- assess as one of:
- UNFOUNDED -- no real issue, emotional venting, unrealistic expectations
- MINOR -- real issue but limited impact, easy to address
- MAJOR -- serious issue requiring immediate attention, systemic problem
- ABUSIVE -- contains threats, profanity, personal attacks. Flag for platform reporting. Recommend not responding publicly or generate a minimal professional response. Never mirror hostility.
Sector -- auto-detect from vocabulary and context:
- HOSPITALITY -- mentions stay, check-in, room, host, property, booking, location, amenities, noise, breakfast
- ECOMMERCE -- mentions shipping, delivery, return, refund, product, app, crash, bug, update, order, package
- GENERIC -- does not clearly match either sector
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.
- 16d ago Changed e9bfee67284d
- 1mo ago First seen · 147 lines · 71 tokens per session scan A bedf0080d476
reply-to-customer-review is a skill published in the GitHub repository acaprino/daodan (9 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 1,719 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 2026-09-05.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…