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Reflection 1

A facilitation guide for the team debrief following Run 1. These questions are designed to spark discussion — not every question needs to be covered. Pick the ones that resonate with what you observed during the run.

What You Built

  • Pull up your dashboard. If an operations manager opened this to check conditions across all 9 zones, what would they actually find useful? If a backcountry skier checked it before a trip, would they understand what the forecast means for them? What's still missing from making this a real tool versus a data display?
  • How many zones does your engine cover? Where did you hit friction — was it in the data ingestion, the display, or in making the analysis component produce something meaningful beyond raw data?
  • Did you ship to your live URL continuously throughout the run, or did deployment happen at the end? What was different about the features you deployed early versus late?

What You Practiced

  • You discussed context architecture in Lift 1. How did it hold up during the build? Did path-scoped rules or subdirectory context actually keep conversations focused, or did you end up putting everything in the root file?
  • Which role gap did your team turn into a skill? Did the skill produce consistent output, or did you need to refine it mid-run? How many iterations did it take before the skill encoded your actual judgment — not just the surface-level process?
  • How did delegation contracts work at this velocity? When you wrote acceptance criteria and handed work to AI, did the output match the spec — or did you find yourself re-explaining what "done" looks like?

How You Worked

  • Did your team parallelize during the build, or did you work together on one component at a time? What drove that decision — and would you make the same call next time?
  • If you parallelized: how did you coordinate? Did independent conversations produce work that integrated cleanly, or did you hit merge conflicts — in the code, in the conventions, or in the approach?

Looking Ahead

  • Your platform parses forecasts and maybe generates AI-powered analysis. But here's the question from Lift 1's visibility discussion: when the AI analysis layer generates a briefing or contextual alert, how do you know it accurately reflects what the forecaster identified? The deterministic pipeline is testable — did you parse the right danger rating? But the AI-generated content is non-deterministic. What would it take to evaluate that systematically?