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capacity-modeling skill

/plugins/sales-operations/skills/capacity-modeling

This skill models capacity planning by forecasting bookings, headcount, ramp, and productivity to align hiring with revenue goals.

npx playbooks add skill gtmagents/gtm-agents --skill capacity-modeling

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: capacity-modeling
description: Use to model bookings targets vs headcount, ramp, and productivity assumptions.
---

# Capacity Modeling Playbook Skill

## When to Use
- During annual/quarterly planning to align hiring with revenue goals.
- Stress testing bookings plans with upside/downside scenarios.
- Preparing CRO/finance readouts on coverage gaps and productivity.

## Framework
1. **Assumption Library** – document ramp curves, win rates, ACV, hours per deal, attrition.
2. **Scenario Engine** – plug assumptions into base/upside/downside tables.
3. **Gap Analysis** – highlight where capacity misses coverage vs target.
4. **Mitigation Paths** – propose levers (hiring, enablement, pricing, partner attach).
5. **Communication Kit** – summarize risks/opportunities for leadership decisions.

## Templates
- Capacity workbook tabs (inputs, scenarios, summary).
- CRO briefing deck outline.
- Hiring + enablement recommendation log.

## Tips
- Keep assumptions versioned to avoid stale models.
- Tie recommendations to time-to-productivity and pipeline coverage KPIs.
- Pair with `quota-health` to see downstream impact of headcount changes.

---

Overview

This skill models bookings targets against headcount, ramp curves, and productivity assumptions to identify coverage gaps and hiring needs. It produces scenario-based capacity plans and recommended mitigation levers. The output is designed for CROs, finance, and revenue ops to support planning and decision-making.

How this skill works

You provide an assumption library (ramp profiles, win rates, ACV, hours per deal, attrition) and baseline bookings targets. The engine runs base, upside, and downside scenarios to calculate required quota-carrying headcount, time-to-productivity effects, and pipeline coverage. It highlights capacity shortfalls and proposes mitigation paths such as hiring timing, enablement interventions, or pricing/partner levers. The skill also generates concise summaries and briefing outlines for leadership reviews.

When to use it

  • Annual or quarterly planning to align hiring with revenue goals
  • Stress testing bookings plans under upside and downside scenarios
  • Preparing CRO or finance readouts on coverage gaps and timing risks
  • Evaluating headcount trade-offs vs productivity investments
  • Prioritizing hiring queues and enablement rollouts for upcoming quarters

Best practices

  • Version and timestamp all assumption sets to avoid stale models
  • Tie model outputs to KPIs: pipeline coverage, time-to-productivity, quota attainment
  • Run multiple ramp curve variants to capture new hire variability
  • Document mitigation levers with expected impact and lead time
  • Keep input tabs separate from scenario and summary tabs for auditability

Example use cases

  • Estimate required quota-carrying hires to hit next quarter bookings target given current pipeline
  • Compare outcomes of accelerating hiring vs increasing enablement spend on ramp time
  • Create base/upside/downside tables for board or CRO briefings
  • Identify timing gaps where hires won’t reach productivity before peak demand
  • Link capacity outputs to quota-health to assess downstream quota attainment risk

FAQ

What core inputs are required?

At minimum: bookings targets, current headcount, ramp curves, ACV, win rates, attrition, and average hours per deal.

How do I handle uncertainty in ramp assumptions?

Run conservative, median, and aggressive ramp curves and present the range; version each assumption set and note historical ramp performance.

Can this model recommend specific mitigation levers?

Yes. It quantifies impact of hiring timing, faster ramp via enablement, pricing changes, and partner attach, and associates each lever with expected lead time.