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    "name": "experiment-tracker",
    "description": "PROACTIVELY use this agent when experiments are started, modified, or when results need analysis. This agent specializes in tracking A/B tests, feature experiments, and iterative improvements within the 6-day development cycle. Should be triggered automatically when experimental code paths or feature flags are introduced. Examples:\\n\\n<example>\\nContext: When implementing feature flags or A/B test variants\\nuser: \"Add a feature flag to test the new onboarding flow\"\\nassistant: \"I've implemented the feature flag for the onboarding flow A/B test. Let me use the experiment-tracker agent to properly document this experiment and set up tracking.\"\\n<commentary>\\nEvery feature flag represents an experiment that needs proper tracking and success metrics.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: After deploying experimental features\\nuser: \"The new viral sharing feature is now live for 10% of users\"\\nassistant: \"Great! The viral sharing feature is deployed. I'll use the experiment-tracker agent to monitor its performance and document initial results.\"\\n<commentary>\\nLive experiments need immediate tracking setup to capture valuable data from the start.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: When reaching experiment milestones\\nuser: \"It's been a week since we launched the TikTok integration test\"\\nassistant: \"Perfect timing for analysis. Let me use the experiment-tracker agent to compile the week-one results and determine if we should expand the test.\"\\n<commentary>\\nRegular experiment checkpoints ensure data-driven decisions about feature rollouts.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Before making product decisions\\nuser: \"Should we keep the AI avatar feature or remove it?\"\\nassistant: \"Let me analyze the experiment data to inform this decision. I'll use the experiment-tracker agent to review all metrics and user feedback for the AI avatar feature test.\"\\n<commentary>\\nProduct decisions should be backed by experiment data, not gut feelings.\\n</commentary>\\n</example>",
    "version": "1.0.0"
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