陌讯skills聚合平台

support-operations

📁 ncklrs/startup-os-skills 📅 Jan 27, 2026
14
总安装量
5
周安装量
#24119
全站排名
安装命令
npx skills add https://github.com/ncklrs/startup-os-skills --skill support-operations

Agent 安装分布

claude-code 5
opencode 4
codex 2
github-copilot 2
antigravity 2
gemini-cli 2

Skill 文档

Support Operations

Strategic support operations expertise for customer-facing teams — from ticket management and SLA design to escalation workflows and self-service optimization.

Philosophy

Great support isn’t about closing tickets fast. It’s about solving customer problems permanently while building scalable systems.

The best support operations teams:

  1. Prevent before they support — Self-service and proactive help reduce ticket volume
  2. Measure what drives loyalty — Resolution quality beats response speed
  3. Escalate with context — Every handoff preserves customer history
  4. Feed insights upstream — Support data drives product and success improvements

How This Skill Works

When invoked, apply the guidelines in rules/ organized by:

  • ticket-* — Ticket management, prioritization, queue optimization
  • sla-* — SLA design, compliance monitoring, escalation triggers
  • tier-* — Support tier structure, skill-based routing, specialization
  • knowledge-* — Knowledge base strategy, self-service, deflection
  • metrics-* — CSAT, FRT, TTR, FCR, quality scoring
  • escalation-* — Severity definitions, escalation paths, incident management
  • tooling-* — Support stack optimization, integrations, automation
  • feedback-* — Support-to-CS handoffs, product feedback loops, voice of customer

Core Frameworks

The Support Operations Hierarchy

Level Focus Metrics Owner
Tickets Individual resolution Handle time, CSAT Agents
Queue Flow optimization Wait time, backlog Team leads
Channel Channel effectiveness Deflection, containment Managers
Operations System performance Cost per ticket, NPS Directors
Strategy Business impact Retention, expansion VP/C-level

The Support Tier Model

┌─────────────────────────────────────────────────────────────────┐
│                         TIER 3 (L3)                              │
│  Engineering escalation, code-level issues, custom development  │
│  Target: <5% of tickets | SLA: Best effort                      │
├─────────────────────────────────────────────────────────────────┤
│                         TIER 2 (L2)                              │
│  Technical specialists, complex troubleshooting, integrations   │
│  Target: 15-25% of tickets | SLA: 4-8 hours                     │
├─────────────────────────────────────────────────────────────────┤
│                         TIER 1 (L1)                              │
│  First response, common issues, documentation guidance          │
│  Target: 60-80% resolution | SLA: 15-60 minutes                 │
├─────────────────────────────────────────────────────────────────┤
│                      SELF-SERVICE (L0)                           │
│  Knowledge base, chatbots, community forums, in-app help        │
│  Target: 30-50% deflection | SLA: Instant                       │
└─────────────────────────────────────────────────────────────────┘

Ticket Priority Matrix

Priority Business Impact Response SLA Resolution SLA Examples
P1 Critical Complete outage, data loss 15 min 4 hours System down, security breach
P2 High Major feature broken 1 hour 8 hours Key workflow blocked
P3 Medium Feature impaired 4 hours 24 hours Partial functionality
P4 Low Minor issue, cosmetic 8 hours 72 hours UI bug, minor inconvenience
P5 Request Feature request, how-to 24 hours 5 days Enhancement, training

Support Metrics Framework

Metric Definition Target Warning
CSAT Customer satisfaction score 90%+ <85%
FRT First response time <1 hour >4 hours
TTR Time to resolution <24 hours >72 hours
FCR First contact resolution 70%+ <50%
NPS Net promoter score 30+ <10
Ticket Volume Tickets per 100 customers 5-15 >25
Deflection Rate Self-service success 30-50% <20%
Escalation Rate Tickets escalated 10-20% >30%
Reopen Rate Tickets reopened <5% >10%
Agent Utilization Productive time 70-80% <60% or >90%

The Ticket Lifecycle

┌─────────────────────────────────────────────────────────────────┐
│                                                                  │
│  NEW → TRIAGED → ASSIGNED → IN PROGRESS → PENDING → RESOLVED   │
│                                    │          │                  │
│                                    ▼          ▼                  │
│                              ESCALATED    WAITING                │
│                                    │     (Customer)              │
│                                    ▼                             │
│                              ENGINEERING                         │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Channel Strategy Matrix

Channel Best For Cost Scalability Personal
Self-service Common issues Lowest Highest Lowest
Chatbot Quick questions Low High Low
Live chat Real-time help Medium Medium Medium
Email/Ticket Complex issues Medium Medium Medium
Phone Urgent/sensitive High Low High
Video Technical demos High Low Highest

Severity Levels

Severity Definition Escalation Path Communication
SEV1 System-wide outage Immediate to engineering + exec Status page, proactive email
SEV2 Major feature broken 1 hour to L3 Affected users notified
SEV3 Feature degraded 4 hours to L2 Standard ticket updates
SEV4 Minor impact Normal queue Standard ticket updates

Key Formulas

Cost Per Ticket

Cost Per Ticket = (Total Support Cost) / (Total Tickets Handled)
Target: $5-25 depending on complexity

Support Capacity Planning

Required Agents = (Ticket Volume × Handle Time) / (Available Hours × Utilization Rate)

Example:
(500 tickets × 20 min) / (8 hours × 60 min × 0.75) = 28 agents

Self-Service ROI

Savings = (Deflected Tickets × Cost Per Ticket) - Self-Service Investment

Anti-Patterns

  • Speed over quality — Fast wrong answers create repeat contacts
  • Ticket tennis — Multiple handoffs without resolution
  • Knowledge hoarding — Solutions in heads, not documentation
  • Metric gaming — Closing tickets prematurely to hit targets
  • Escalation avoidance — L1 struggling when L2 is needed
  • Channel forcing — Making customers switch channels unnecessarily
  • Copy-paste responses — Generic answers that don’t address the issue
  • Invisible backlog — Tickets aging without visibility
  • No feedback loop — Support insights never reach product
  • Over-automation — Bots handling issues that need humans
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