场景 1：客服与工单自动化 / Customer Support & Ticket Automation
中文：把多通道用户问题统一进一个处理流，先自动分类与建议回复，再决定是否升级人工。
EN: Unify multi-channel support messages into one flow: auto-classify, suggest responses, and escalate to human agents when needed.
KPI（中文 / EN）
- 一次解决率 FCR / First Contact Resolution
- 平均首次响应时间 FRT / First Response Time
- 人工接管率 Human Handoff Rate
- 工单升级率 Escalation Rate
模板入口 / Template
support_flow:
ingest: <CHANNEL_EVENT>
classify: [billing, technical, refund, other]
respond:
faq_hit: auto_reply
faq_miss: draft_reply + human_review
ticket:
create_when: [high_risk, negative_sentiment, repeated_issue]
audit:
metrics: [fcr, frt, handoff_rate, escalation_rate]文章标题池 / Article Ideas (ZH + EN)
- 《把客服消息统一成一个事件流：OpenClaw 实战》 / "How to Normalize Support Messages into One Event Stream with OpenClaw"
- 《客服自动化的边界：哪些场景必须人工接管》 / "Automation Boundaries: When Human Handoff Is Non-Negotiable"
- 《从 FRT 到 FCR：客服 KPI 的可观测设计》 / "From FRT to FCR: Designing Observable Support KPIs"
场景 2：社群运营与增长 / Community Operations & Growth
中文：围绕新成员引导、规则提醒、FAQ 回答与风险词处理，降低运营重复劳动。
EN: Automate onboarding, rule reminders, FAQ handling, and risky-content routing to reduce repetitive community operations work.
KPI（中文 / EN）
- 7 日留存 D7 Retention
- 问题命中率 FAQ Hit Rate
- 人工介入次数 Manual Intervention Count
- 活跃用户比例 Active Member Ratio
模板入口 / Template
community_flow:
onboarding:
trigger: member_joined
action: send_welcome + starter_faq
moderation:
detect: [spam, abuse, policy_violation]
route: [auto_warn, manual_review]
faq:
match: semantic_search(<FAQ_INDEX>)
fallback: human_mention
analytics:
metrics: [d7_retention, faq_hit_rate, intervention_count]文章标题池 / Article Ideas (ZH + EN)
- 《社群自动化不是机器人刷屏：先做欢迎流与 FAQ》 / "Community Automation Starts with Onboarding and FAQ, Not Spam Bots"
- 《把社群规则变成可执行路由：误判率如何控制》 / "Turning Community Rules into Executable Routing with Lower False Positives"
- 《用 D7 留存复盘社群运营流程》 / "Using D7 Retention to Review Community Workflow Quality"
场景 3：告警值班与故障协同 / On-call Alerting & Incident Collaboration
中文：把监控告警先聚合再路由，减少告警风暴；让确认、升级、关闭形成闭环。
EN: Aggregate and route alerts before notifying on-call staff, reducing noise while preserving incident closure workflows.
KPI（中文 / EN）
- 平均确认时间 MTTA / Mean Time to Acknowledge
- 平均恢复时间 MTTR / Mean Time to Resolve
- 每小时告警数 Alerts per Hour
- 误报率 False Positive Rate
模板入口 / Template
incident_flow:
ingest: <MONITORING_ALERT>
dedupe:
key: [service, fingerprint, severity]
window: <DEDUP_WINDOW_MINUTES>
route:
p1: page_primary_oncall
p2: notify_channel
close_loop:
ack: write_back_status
resolved: close_ticket + postmortem_stub
metrics:
track: [mtta, mttr, alerts_per_hour, false_positive_rate]文章标题池 / Article Ideas (ZH + EN)
- 《告警先聚合再通知：值班团队如何降噪》 / "Aggregate Before Notify: Noise Reduction for On-call Teams"
- 《MTTA/MTTR 不只是运维指标，也是协作质量指标》 / "MTTA/MTTR as Collaboration Quality Metrics, Not Just Ops Metrics"
- 《从告警到复盘：故障闭环的最小模板》 / "From Alert to Postmortem: A Minimal Incident Closure Template"