sprint-optimizer

Generates three-case sprint plans with weighted scoring and iterative refinement rounds.

Updated Apr 5, 2026
One-click install
npx skills add https://github.com/Simon-YHKim/eject-button --skill sprint-optimizer-simon-yhkim
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sprint-optimizer
Source: https://github.com/Simon-YHKim/eject-button/tree/main/.claude/skills/sprint-optimizer
Command: npx skills add https://github.com/Simon-YHKim/eject-button --skill sprint-optimizer-simon-yhkim

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Decision-making during sprint planning often stalls on a single option or endless debate. This Skill structures every decision into three comparable cases (Conservative, Balanced, Aggressive), recommends a Best case using weighted criteria, and runs a bounded iteration loop so plans improve without infinite rework. ## Core Features & Use Cases - 3-Case Analysis: Produces Conservative, Balanced, and Aggressive options with risk and reward profiles for any decision. - Weighted Best-Case Recommendation: Scores options on Impact (40%), Feasibility (30%), Speed (20%), and Risk (10%), with user-adjustable weights. - Bounded Iterative Refinement: Runs a user-agreed number of improvement rounds (1, 3, or 5+) incorporating feedback each round, preventing endless loops. - Use Case: A product manager asks to compare three launch strategies; the Skill outputs a comparison table, recommends the Balanced case, then refines it over three feedback rounds into a final sprint report. ## Quick Start Ask the assistant to run sprint mode and compare three options for your next product decision, then pick the recommended case and agree on an iteration depth.

Frequently Asked Questions about sprint-optimizer

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

FAQPage Schema
How do I compare multiple sprint planning options?

Request sprint mode and the Skill generates three cases—Conservative, Balanced, and Aggressive—each with description, expected outcome, time estimate, and risk. It then recommends a Best case using weighted scoring criteria.

What criteria does sprint-optimizer use to recommend a best case?

It scores options on Impact (40%), Feasibility (30%), Speed (20%), and Risk (10%). These weights are adjustable, so you can reprioritize criteria like speed over impact for time-sensitive decisions.

How does the iterative refinement loop work?

After you pick a case, you agree on an iteration depth of 1, 3, or 5+ rounds. Each round incorporates your feedback, addresses edge cases, and refines the plan, ending with a final validated version.

Can the refinement loop run indefinitely?

No. The iteration depth must be explicitly agreed with the user before refinement starts. This bounded design prevents infinite loops and keeps each sprint cycle time-boxed.

What output does a sprint planning session produce?

Each session produces a markdown Sprint Report containing a three-case comparison table, the recommendation with reasoning, and an iteration log documenting each refinement round and its changes.