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 ntaffzii/Skill-Agents --skill thai-customer-servicegit clone --depth 1 https://github.com/ntaffzii/Skill-AgentsWrote 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/ntaffzii/skill-agents/thai-customer-service)<a href="https://agentmods.dev/skills/ntaffzii/skill-agents/thai-customer-service"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/thai-customer-service/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/ntaffzii/skill-agents/thai-customer-service"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/thai-customer-service.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 | $0.00129 | $0.01062 |
| Opus 5 | $0.00064 | $0.00531 |
| Sonnet 5 | $0.00026 | $0.00212 |
| Haiku 4.5 | $0.00013 | $0.00106 |
Grade A, and why
thai-customer-service 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 12d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thai Customer Service Replies
Overview
Using the same apology intensity for a 1-day shipping delay and a wrong/damaged item both under- and over-shoots — Thai customer-service register has a graded apology ladder, and picking the wrong rung reads as either dismissive or oddly dramatic. This skill maps severity to the right phrase and gives channel-specific tone guidance, since a LINE OA reply and a Shopee platform-mediated reply don't read the same even for an identical situation.
When to use
- Reply ลูกค้าที่ร้องเรียน/บ่น/ขอเงินคืน บน LINE OA, Facebook Page, Shopee, Lazada, IG DM, TikTok Shop
- เขียนข้อความแจ้งสถานะ (delay, order update, restock)
- ร่าง auto-reply / FAQ response ที่ยังต้องฟังดูเป็นมนุษย์ ไม่ใช่บอทแข็งๆ
- Choosing the right apology intensity for a given severity of complaint
When NOT to use
- Internal communication to a colleague or vendor — that's a different register, not the customer-facing apology ladder
- Formal written complaint response requiring legal review (e.g. involving compensation liability) — draft here for tone, but have anything with real financial/legal exposure reviewed
Core knowledge
Apology ladder — match intensity to severity, don't default to the strongest phrase every time (it reads as insincere when overused) or the mildest (reads as dismissive for a real failure):
| Phrase | Severity | Example trigger |
|---|---|---|
| ขออภัย(ค่ะ/ครับ)ในความไม่สะดวก | Mild | Minor delay, small inconvenience, a question that took a while to answer |
| ขออภัยเป็นอย่างยิ่ง | Moderate | Order delay beyond promised window, out-of-stock after order confirmed |
| ขอโทษอย่างสูง | High | Wrong item sent, damaged item, billing error, clearly the seller's fault |
| ขอแสดงความเสียใจอย่างยิ่ง | Severe | Major failure, safety/health concern, repeated failure after prior complaint |
Pair the apology with a concrete next step every time — an apology with no resolution offered reads as empty regardless of which rung is used.
Channel tone differences:
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.
- 12d ago First seen · 61 lines · 129 tokens per session scan A 7a94a6fdd7d5
thai-customer-service is a skill published in the GitHub repository ntaffzii/Skill-Agents (4 stars, last pushed 3d ago), licensed MIT. It adds 129 tokens to every session and 1,062 once invoked, about $0.0006 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 2026-08-31.
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