priority-analyzer

Feature prioritization using RICE scoring calculates Reach × Impact × Confidence divided by Effort to rank development items. The analyzer automates this computation by integrating data from BRIEF.md, SPEC.md, and CONTEXT.json files to generate evidence-based priority rankings.

The RICE framework for development roadmap planning evaluates features by multiplying Reach, Impact, and Confidence, then dividing by Effort. It provides a quantitative method to sequence product features based on potential value and implementation feasibility.

Confidence scores for feature ranking are derived from the completeness and quality of documentation and research. The analyzer evaluates evidence within BRIEF.md, SPEC.md, and CONTEXT.json to automatically assign confidence values to each prioritized feature.

Competitor data integration into feature prioritization is supported using the competitor-registry.json file. The analyzer incorporates this market and competitor data to refine RICE priority rankings and adjust feature sequencing.

Ranking backlog features for a sprint is best achieved by applying the RICE model across multiple project data sources. The analyzer processes gap-candidates.json alongside project documentation to output a detailed, ranked list of features for the next development sprint.

RICE prioritization works without complete project documentation, but incomplete BRIEF.md or SPEC.md files will lower the evidence-based confidence scores. Missing context directly reduces the calculated confidence multiplier, yielding more conservative feature rankings.