All examples for STAR Story Formatter

Transforming a Vague Performance Story

Shows how the skill evaluates a generic story lacking metrics and specificity, then rewrites it with concrete improvements.

Input

What you provide to the skill

I led a project to improve our mobile app performance. The app was slow so I analyzed the code. We made changes and the app got faster. Users were happy with the result.

Output

What the skill generates for you

STAR Story Evaluation: Lacks Metrics and Personal Contribution

Overall Score: 3.8/10

Your story has a logical structure but reads like a status update. You’re missing nearly all metrics, using vague language, and it’s unclear what you personally did versus the team.


Dimension Scores

Impact Metrics: 1/5

What you said: “The app was slow… the app got faster. Users were happy.”
What’s missing: Actual performance metrics (load time, improvement %), scale (users affected), team size, timeframe, business impact.

Role Clarity: 2/5

What you said: “I led… I analyzed… We made changes”
Problem: “Led” is vague. The critical decisions shift to “we” - unclear what YOU decided.

Time Balance: 3/5

Current allocation: S=25%, T=25%, A=25%, R=25%
Problem: Action should be 60%. Currently one sentence: “We made changes.”


Rewritten Story

Situation:
“Our iOS app had a 4.2-second startup time - 2x slower than competitors. We were receiving 50+ App Store complaints weekly and retention had dropped from 65% to 48%.”

Task:
“My goal was to reduce startup time below 2.5 seconds and recover retention to 60% within 8 weeks.”

Action:
“I analyzed 10,000 user sessions using Firebase Performance Monitoring and discovered 65% of startup time was spent loading unnecessary assets. I decided to prioritize three changes: lazy-load secondary screens, compress images, and defer analytics. I created specs with expected impact for each and got buy-in from the eng lead. When our first A/B test showed only 30% improvement, I re-analyzed and found the video player was still pre-loading. I convinced the team to refactor despite the 1-week delay.”

Result:
“Startup time dropped to 2.1 seconds (50% improvement). Within 4 weeks, retention recovered to 62%, complaints dropped 84%, and our rating improved from 4.1 to 4.5.”


Before vs. After

Before: 3.8/10
After (rewritten): 9.0/10

Key improvements:

  • Added 14+ specific metrics throughout
  • Clear “I decided” and “I convinced” for decisions
  • Action section expanded to 60% of content