Run the Post-Launch Learning workflow to set up measurement, evaluate results, and capture learnings after a feature ships.
This workflow uses multiple skills in sequence. For each step, read the skill instructions and follow them to create the artifact.
Workflow Steps
Step 1: Instrumentation Spec
Use the measure-instrumentation-spec skill from skills/measure-instrumentation-spec/SKILL.md.
Define event tracking and analytics instrumentation requirements for the shipped feature.
Step 2: Dashboard Requirements
Use the measure-dashboard-requirements skill from skills/measure-dashboard-requirements/SKILL.md.
Specify the analytics dashboard including metrics, visualizations, and data sources.
Step 3: Experiment Results
Use the measure-experiment-results skill from skills/measure-experiment-results/SKILL.md.
Document the results of the feature launch with analysis and recommendations.
Step 4: Retrospective
Use the iterate-retrospective skill from skills/iterate-retrospective/SKILL.md.
Facilitate a team retrospective covering the full feature lifecycle.
Step 5: Lessons Log
Use the iterate-lessons-log skill from skills/iterate-lessons-log/SKILL.md.
Distill retrospective findings into durable lessons for organizational memory.
Output
Create all five artifacts in sequence. Steps 1-2 should happen at or before launch; Steps 3-5 after data accumulates.
Reference the Post-Launch Learning workflow at _workflows/post-launch-learning.md for additional guidance.
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