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Automation
Skill

/sla-tracking

Use to design measurement, alerting, and reporting for MQL\u2192SQL\

From plugin
gtm-agents
401200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --skill sla-tracking --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/sla-tracking

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use to design measurement, alerting, and reporting for MQL\u2192SQL\

SKILL.md

sla-tracking.SKILL.md
name: sla-tracking
description: "Use to design measurement, alerting, and reporting for MQL\u2192SQL\
  \ SLAs."

SLA Tracking System Skill

When to Use

  • Establishing or revisiting SLA metrics between marketing, SDR, and sales pods.
  • Building dashboards/alerts for pipeline speed and follow-up compliance.
  • Running retrospectives after SLA breaches or pipeline delays.

Framework

1. **Definitions** – clarify timestamps (MQL, SAL, SQL), owner transitions, and acceptance criteria. 2. **Targets** – set response + acceptance SLAs per segment, region, or channel. 3. **Measurement** – configure data pipelines pulling MAP + CRM events, dedupe logic, and exclusions. 4. **Alerting** – thresholds, notification channels, severity levels, and on-call rotation. 5. **Review Cadence** – weekly dashboards, monthly retros, quarterly recalibration.

Templates

  • SLA scorecard (segment → target → actual → variance → owner).
  • Alert playbook with trigger conditions and escalation steps.
  • Retro template capturing root cause, fixes, and follow-up experiments.

Tips

  • Anchor SLAs to revenue impact (pipeline $) to drive accountability.
  • Include qualitative context (reason codes) to separate data gaps vs true SLA misses.
  • Pair with `routing-logic` updates when volume spikes create bottlenecks.

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