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Yoshikemolo
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devflow-planning

Analyze Jira velocity and capacity to forecast sprint outcomes.

5|3|Updated Jan 21, 2026
Search Tags:#agile#sprint planning#forecasting#scrum#velocity#capacity
One-click install
npx skills add https://github.com/Yoshikemolo/mcp-jira-devflow --skill devflow-planning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: devflow-planning
Source: https://github.com/Yoshikemolo/mcp-jira-devflow/tree/main/skills/devflow-planning
Command: npx skills add https://github.com/Yoshikemolo/mcp-jira-devflow --skill devflow-planning

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines agile sprint planning by providing AI-driven insights into velocity, capacity, and potential risks, reducing manual analysis and improving predictability.

Core Features & Use Cases

  • Sprint Planning: Analyze historical velocity and team capacity to recommend optimal sprint loads.
  • Forecasting: Predict sprint success probability and identify potential spillover risks.
  • Agile Recommendations: Offers guidance on Scrum best practices, GitFlow integration, and team health.
  • Use Case: A Scrum Master can use this Skill to quickly determine the ideal number of story points to commit to for the next sprint, based on the team's past performance and current availability.

Quick Start

Use the devflow-planning skill to plan the next sprint by analyzing velocity and calculating capacity.

Frequently Asked Questions about devflow-planning

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

FAQPage Schema
How do I calculate sprint capacity and velocity for my agile team?▼

Forecast sprint success and identify spillover risks by applying AI-driven insights to historical velocity and capacity data. This sprint forecasting approach analyzes team availability and past performance to predict outcomes and potential delays.

Can I use Jira historical data for sprint forecasting and risk analysis?▼

Yes, Jira integration is required to provide historical data and issue tracking for sprint forecasting. The Skill analyzes Jira records to calculate team capacity, evaluate velocity, and predict sprint success probability.

What is the best way to determine optimal story point commitments for a sprint?▼

The best way to determine optimal story point commitments is by analyzing historical velocity and current team capacity to recommend ideal sprint loads. This AI-driven approach evaluates past performance and availability to optimize sprint commitments.

Does agile sprint planning work without manual velocity analysis?▼

Agile sprint planning can operate without manual velocity analysis by using AI-driven insights to automate capacity calculations and velocity evaluation. This reduces manual analysis, streamlines workflows, and improves predictability for Scrum teams.

When should a Scrum Master use AI for sprint load recommendations?▼

A Scrum Master should use AI for sprint load recommendations when needing to quickly determine ideal story point commitments based on past performance and current availability. It is ideal for reducing manual analysis and improving sprint predictability.

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