target-identify

Optimization targets are identified by analyzing friction clusters in session events using LLM-based judgment. This process evaluates M2-classified events to produce a ranked list of targets with root cause, frequency, severity, and improvability scores.

Ranking root causes in session analysis involves applying LLM evaluation to friction clusters. The output prioritizes targets by frequency, severity, and improvability, enabling focused workflow optimization and sprint planning.

Yes, LLM evaluation for friction cluster analysis requires the Bun runtime. You must also configure the target-identify script and provide an Anthropic API key with access to a compatible model.

Yes, an Anthropic API key with access to a compatible model is required to run the target-identify script. The LLM uses this access to evaluate session events and generate ranked optimization targets.

LLM-based root cause analysis outputs a prioritized list of optimization targets. Each target includes specific root causes, frequency, severity, and improvability scores to guide post-session reviews and workflow improvements.

LLM evaluation for session analysis requires M2-classified events and a configured Anthropic API key. If your session events are not pre-classified into friction clusters, this approach cannot produce ranked targets.