\"cs-sop\"
SkillMediacs-sop helps your AI design customer service operations from the ground up. Once added, your AI can put together a complete support setup, from team tiers and response templates to escalation and complaint handling, so customers get consistent, quality answers.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding the skill, tell your AI what you need, such as setting up a support team, creating service standards, or improving response quality, and it will draft the operations for you.
Then ask your AI: use the \"cs-sop\" skill
What your AI can do with it
- Design tiered support structures with L1, L2, and L3 levels
- Create response templates for customer conversations
- Define service level agreements (SLAs)
- Write clear escalation procedures
- Set up processes for handling customer complaints
- Improve the quality of customer responses
What this skill tells your AI
The instructions your AI receives, as published by charlieviettq/awesome-agent-skill in .claude/skills/cs-sop/SKILL.md and read by ahel’s review.
Framework
IRON LAW: Tier the Support, Not the Customer
Every customer deserves quality service. But not every issue needs a
senior specialist. Route by ISSUE COMPLEXITY, not by customer "importance."
L1 handles 70-80% of volume (simple, repeatable)
L2 handles 15-20% (requires expertise)
L3 handles 5% (requires engineering or management)
Three-Tier Support Model
| Tier | Handles | Skills Required | Resolution Target |
|---|---|---|---|
| L1 (Basic) | FAQ, order status, password reset, simple returns | Script-following, product basics, empathy | < 5 minutes, first-contact resolution |
| L2 (Specialist) | Technical issues, billing disputes, complex returns, product defects | Deep product knowledge, judgment, negotiation | < 24 hours |
| L3 (Expert) | System bugs, legal/compliance, executive escalations, crisis | Engineering, legal, or management involvement | < 72 hours, case-by-case |
Case Categorization
| Category | Examples | Priority | SLA (First Response) |
|---|---|---|---|
| Critical | Service outage, security breach, safety issue | P1 | < 15 minutes |
| High | Payment failure, account locked, order error | P2 | < 1 hour |
| Medium | Product question, feature request, general complaint | P3 | < 4 hours |
| Low | Feedback, suggestion, general inquiry | P4 | < 24 hours |
Complaint Handling: LAST Framework
- Listen: Let the customer express fully without interrupting
- Apologize: Acknowledge their frustration sincerely ("I'm sorry this happened")
- Solve: Offer a concrete solution or next step
- Thank: Thank them for bringing it to your attention
Escalation Rules
| Trigger | Escalate To | Timeline |
|---|---|---|
| L1 can't resolve in 15 min | L2 | Immediate warm handoff |
| Customer requests supervisor | L2 or Team Lead | Within 5 minutes |
| Issue involves refund > NT$X | L2 (approval authority) | Same interaction |
| Legal threat or media mention | L3 + Legal + PR | Immediate |
| Repeat contact (3+ on same issue) | L2 + investigation | After 3rd contact |
Response Template Structure
[Greeting] Hi {name}, thank you for contacting us.
[Acknowledge] I understand you're experiencing {issue}.
[Action] Here's what I've done / Here's what we'll do:
1. {specific action}
2. {timeline}
[Next steps] {what the customer should expect / do next}
[Close] Is there anything else I can help you with?
Output Format
# Customer Service SOP: {Business}
## Support Tiers
| Tier | Scope | Team Size | Tools |
|------|-------|----------|-------|
| L1 | {scope} | {N people} | {tools} |
| L2 | {scope} | {N} | {tools} |
| L3 | {scope} | {N} | {tools} |
## SLA Targets
| Priority | First Response | Resolution | Escalation |
|----------|--------------|-----------|-----------|
| P1 | {time} | {time} | {to whom} |
| P2 | ... | ... | ... |
## Top 10 Contact Reasons
| # | Reason | Volume % | Resolution | Template? |
|---|--------|---------|-----------|----------|
| 1 | {reason} | {%} | L1/L2 | Y/N |
## Escalation Flowchart
{Decision tree for when to escalate}
## Quality Metrics
| Metric | Target |
|--------|--------|
| First Contact Resolution | > 70% |
| CSAT | > 4.2/5 |
| Avg Response Time | < {X} hours |
| Escalation Rate | < 20% |
Gotchas
- SLAs must be MEASURABLE: "Respond quickly" is not an SLA. "First response within 1 hour for P2 tickets" is. If you can't measure it, you can't manage it.
- Warm handoff > cold transfer: When escalating, the L1 agent should brief L2 before transferring. Forcing the customer to repeat their story destroys satisfaction.
- Empower L1 with resolution authority: If L1 must escalate every refund, 70% of volume goes to L2 unnecessarily. Give L1 authority for refunds under a threshold (e.g., NT$500).
- Templates are starting points, not scripts: Robotic copy-paste responses feel worse than no response. Agents should personalize templates to the specific situation.
- Taiwan CS expectations: Taiwan customers expect fast LINE response (within minutes during business hours), polite and apologetic tone, and willingness to go the extra mile. The bar for "good service" is high.
References
- For CSAT/NPS survey design, see the cs-analytics skill
- For chatbot-human handoff design, see the cs-chatbot-design skill
Signals
- GitHub stars
- 26
- Forks
- 9
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
cs-sop- Source
- github.com/charlieviettq/awesome-agent-skill