home / skills / gtmagents / gtm-agents / quota-health
This skill analyzes quota distribution and fairness to help improve planning, alignment, and quota performance across reps and territories.
npx playbooks add skill gtmagents/gtm-agents --skill quota-healthReview the files below or copy the command above to add this skill to your agents.
---
name: quota-health
description: Use to analyze quota distribution, attainment fairness, and productivity
signals.
---
# Quota Health Review Skill
## When to Use
- During annual planning to align quotas with territory potential.
- Mid-year to diagnose underperformance risks or over-assignment.
- Before launching compensation plan changes or SPIFs.
## Framework
1. **Data Collection** – pull attainment, pipeline, coverage, and territory metrics by rep/segment.
2. **Fairness Analysis** – compute attainment distribution, Gini coefficient, and coverage ratios.
3. **Signal Review** – identify chronic over/under assignments, ramp issues, or constrainted territories.
4. **Recommendation Engine** – suggest quota rebalancing, enablement, or hiring adjustments.
5. **Governance** – log proposed changes, approval needs, and communication strategy.
## Templates
- Quota health dashboard (rep-level KPIs, quartiles, recommendations).
- CRO briefing outline with risks/opportunities.
- Quota adjustment request form with approvals.
## Tips
- Pair with `territory-optimization` outputs to ensure coverage and quota stay in sync.
- Normalize attainment for ramping reps to avoid skewed fairness scores.
- Keep change logs auditable for finance and legal.
---
This skill analyzes quota distribution, attainment fairness, and productivity signals to surface imbalances and actionable fixes. It combines rep- and territory-level metrics with statistical fairness measures and signal detection to recommend quota rebalancing, enablement, or hiring changes. The output is governance-ready and suitable for planning or corrective actions.
The skill ingests attainment, pipeline, coverage, and territory data at the rep and segment level. It computes distribution metrics (quartiles, Gini coefficient, coverage ratios) and normalizes for ramping reps to avoid skew. It then flags chronic over- or under-assignment, ramp issues, and constrained territories, and generates prioritized recommendations plus communication and approval notes.
What inputs does the skill need?
Attainment history, current pipeline, coverage metrics, territory assignments, and ramp flags for reps.
How does it handle new or ramping reps?
It normalizes attainment for ramping reps and applies separate fairness checks so their presence doesn’t skew distribution metrics.