harness-score-analyzer

Analyzing QA score trends involves evaluating multi-round score histories to categorize performance as IMPROVING, STAGNANT, or DECLINING, which then guides subsequent actions. This process computes core statistics to offer actionable predictive guidance for your next steps.

Deciding whether to refine or pivot your strategy depends on evaluating delta thresholds within your score history. When scores categorize as STAGNANT, analyzing these historical deltas recommends either a REFINE or PIVOT strategy to improve performance.

Yes, you can compute lightweight statistics and a basic forecast from a basic score history sample. Providing a simple series like 7.5, 8.0, 7.8 calculates the mean, max, min, and a predictive forecast for the next iteration.

Detecting if multi-round QA cycles are improving or declining requires categorizing the score history trajectory. The analyzer evaluates the sequence of scores to determine if the trend is IMPROVING, STAGNANT, or DECLINING over time.

Yes, this analyzer works without external dependencies for multi-round QA cycles. It uses a frontmatter configuration to compute core statistics, categorize trends, and offer simple predictive guidance directly based on the provided score data.