thrivve-mc-how-many

Forecast completed stories by a target date using Monte Carlo simulation on daily throughput data.

1|Updated Oct 27, 2025

Search Tags:#planning #agile #simulation #forecasting #throughput #data-analytics #monte-carlo

Ready to run

npx skills add https://github.com/Thrivve-Partners/Claude-Skills-Forecasting --skill thrivve-mc-how-many

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: thrivve-mc-how-many
Source: https://github.com/Thrivve-Partners/Claude-Skills-Forecasting/tree/main/thrivve-mc-how-many
Command: npx skills add https://github.com/Thrivve-Partners/Claude-Skills-Forecasting --skill thrivve-mc-how-many

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Forecasts how many stories or tasks will be completed by a future date using Monte Carlo simulation based on historical daily throughput data, enabling probabilistic planning and capacity sizing.

Core Features & Use Cases

Monte Carlo forecasting over a user-defined horizon using historical throughput data.
Handles input: at least 10 days of throughput data, optional start date, target date, and confidence level.
Provides percentile-based forecasts (P50, P85, P95) and readable summaries for planning.
Use case: you want to know how many stories you can expect by a deadline given recent throughput.

Quick Start

Run a Monte Carlo forecast using your historical throughput to estimate how many stories will be completed by a target date at a chosen confidence level.

Frequently Asked Questions about thrivve-mc-how-many

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I forecast how many Agile stories will be completed by a target date?▼

To forecast Agile story completion by a target date, run a Monte Carlo simulation using your historical daily throughput data. The skill calculates probabilistic outcomes at specified confidence levels like P50, P85, or P95 to estimate how many stories will be done.

What is Monte Carlo simulation for Agile throughput forecasting?▼

Monte Carlo simulation for Agile throughput forecasting is a method that runs multiple iterations across a planning horizon using historical daily throughput data. It produces probabilistic percentile forecasts, such as P50 or P85, to predict how many tasks will be completed by a future date.

How much historical throughput data do I need for a Monte Carlo forecast?▼

You need at least 10 days of historical daily throughput data to run a Monte Carlo forecast. The simulation uses this history to project future completion rates, and an XMR-style variation check validates the stability of your throughput data before generating the forecast.

Can I set a specific confidence level for my Monte Carlo story forecast?▼

Yes, you can set an optional confidence level for your Monte Carlo story forecast. The skill uses this input to generate percentile-based forecasts, allowing you to choose thresholds like P50, P85, or P95 to match your planning risk tolerance.

Does the Monte Carlo forecast validate the stability of my throughput data?▼

Yes, the Monte Carlo forecast validates data stability using an XMR-style variation check on your historical throughput. This ensures the daily completion rates are consistent enough to produce reliable probabilistic forecasts for your Agile planning horizon.

What is the best way to use Monte Carlo simulation for capacity sizing?▼

The best way to use Monte Carlo simulation for capacity sizing is to input your historical daily throughput, a start date, and a target date. The simulation outputs percentile forecasts that help you set realistic expectations for how many stories your team can deliver within that timeframe.