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 aAAaqwq/AGI-Super-Team --skill after-salesgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/after-sales)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/after-sales"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/after-sales/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/aaaaqwq/agi-super-team/after-sales"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/after-sales.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 | $0.00019 | $0.07969 |
| Opus 5 | $0.00010 | $0.03985 |
| Sonnet 5 | $0.00004 | $0.01594 |
| Haiku 4.5 | $0.00002 | $0.00797 |
Grade A, and why
after-sales 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 11d 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 — 799 lines — stays where its author put it; the contents beside it link to each section on GitHub.
After-Sales (售后服务)
Overview
After-sales is everything that happens after a customer makes a purchase—onboarding, support, follow-up, and relationship management. Most creators focus 90% of energy on acquisition (getting new customers) and 10% on retention (keeping existing customers). This is backwards: acquiring a new customer costs 5-7x more than retaining an existing one, and existing customers spend 67% more on average than new customers. The core principle: the sale is just the beginning, not the end. Exceptional after-sales transforms one-time buyers into loyal customers who purchase repeatedly, refer others, and become advocates for your brand. On Xiaohongshu, where trust and word-of-mouth drive growth, after-sales is your competitive advantage. A customer who has a great experience tells 3-5 people. A customer who has a bad experience tells 10-20 people (and leaves negative reviews). After-sales isn't just "customer service"—it's revenue protection, brand building, and competitive moat all in one.
Key insight: Businesses with exceptional after-sales see 2-3x higher customer lifetime value (CLV) and 5-10x more referrals than average businesses. Why? Because after-sales creates differentiation in crowded markets. Products can be copied, prices can be matched, but exceptional customer experience cannot. On Xiaohongshu, where followers watch how you treat customers, after-sales is public marketing. How you handle questions, issues, and support signals to potential customers whether they can trust you. Exceptional after-sales = social proof = easier sales = higher conversion = more revenue. Most creators treat customers as transactions: sell, move on. Smart creators treat customers as relationships: sale, nurture, grow. The goal isn't just satisfaction—it's delight. Satisfied customers buy again. Delighted customers become advocates who fuel your growth for free.
When to Use
Use when:
- Selling products or services (physical or digital)
- Building subscription or membership business
- Launching new offer (plan after-sales before launch)
- Experiencing customer churn or low repeat purchase rate
- Receiving customer complaints or negative feedback
- Wanting to increase customer lifetime value
- Building referral system or advocacy program
- Scaling business (need systems to maintain quality)
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.
- 11d ago First seen · 799 lines · 19 tokens per session scan A 95a9925e279c
after-sales is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 7,969 once invoked, about $0.0001 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 2026-08-30.
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