follow up leak sla auditor

Operations lose money in the gaps between stages. Leads rot in CRMs, support tickets sit unassigned, and recruiting pipelines stall because of poor handoffs. This skill identifies exactly where operational leakage occurs and quantifies the financial or service risk of those delays.

What it does

• Lifecycle reconstruction tracks record movement across stages, ownership changes, and activity timestamps to map the true process flow.
• SLA breach detection identifies missed first-touch windows, stale records, and response time violations against explicit or inferred targets.
• Leakage quantification calculates the estimated revenue or service loss using conversion rates and backlog aging multipliers.
• Accountability mapping segments performance by team, rep, or source to separate systemic failures from individual anecdotes.
• Fix prioritization generates a structured cleanup plan covering routing logic, alert thresholds, and hygiene automation.

How it works

1. Source mapping identifies available data from exports, logs, or screenshots and flags missing fields required for a high-confidence audit.
2. Logic definition establishes the governing SLA rules, whether they are explicitly provided or must be inferred from historical patterns.
3. Pattern analysis runs the audit across segments to find broken handoffs or orphaned records that lack a canonical owner.
4. Risk reporting delivers a leak summary with revenue estimates and specific remediation steps.

Frameworks & tools

The skill is designed for environment-agnostic audits using standard CSV/XLSX exports or direct file access. It incorporates workflow logic patterns inspired by n8n, Temporal, and LangGraph.

Why this beats prompting it yourself

Generic prompts often overlook backfilled imports that distort record age or tickets closed automatically without real resolution. This skill uses a rigorous decision gate for data quality and forces an explicit calculation of risk rather than just listing "old" items.

Use cases

• Auditing a sales CRM export to find high-value leads that haven't been touched in 48 hours.
• Analyzing support ticketing data to identify queues where response times exceed service level agreements.
• Reviewing recruiting pipelines to find candidates stalled in interview stages without feedback.
• Estimating the monthly revenue loss caused by slow intake processes in professional services.

Known limitations

Distorted results may occur if data includes backfilled imports or lacks granular activity logs. Multi-team ownership without a clear handoff field can reduce attribution accuracy.