Quota and capacity modeling methodology with Monte Carlo simulations and P10/P50/P90 scenarios. Use when building quota models, running scenario analysis, calculating pipeline coverage, performing sensitivity analysis, back-testing against historical data, or validating hiring plans.
From opspal-gtm-planningnpx claudepluginhub revpalsfdc/opspal-commercial --plugin opspal-gtm-planningThis skill is limited to using the following tools:
coverage-ratios.mdp10-p50-p90-models.mdramp-patterns.mdseasonality-factors.mdGuides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Details PluginEval's skill quality evaluation: 3 layers (static, LLM judge), 10 dimensions, rubrics, formulas, anti-patterns, badges. Use to interpret scores, improve triggering, calibrate thresholds.
| Metric | Target | Alert |
|---|---|---|
| Scenario sum vs targets | ±2% | >5% |
| Back-test variance | ≤15% | >20% |
| P10-P90 range | P10 ≥ 70% of P50 | <60% |
| Hiring constraints | 100% respected | Any violation |
P10 (Pessimistic): 10th percentile outcome
P50 (Most Likely): 50th percentile (median)
P90 (Optimistic): 90th percentile outcome
Interpretation:
- 50% chance of achieving P50 or better
- 90% confidence interval: P10 to P90
- Risk of missing target: Calculate from distribution
See supporting files:
p10-p50-p90-models.md - Scenario modelingramp-patterns.md - Rep ramp modelingcoverage-ratios.md - Pipeline coverageseasonality-factors.md - Seasonal adjustments