evaluation-reporting-framework

Code quality evaluation combines automated metrics collection from your codebase with multi-dimensional scoring across performance, security, and architecture dimensions. This Skill aggregates data from both automated tools and manual assessments, then generates scored benchmarks and executive summaries in multiple formats to show project health against baselines.

Multi-format reporting converts evaluation results into Markdown, HTML, JSON, and PDF outputs. Each format supports different consumption patterns—JSON for programmatic access, HTML and Markdown for documentation, and PDF for executive distribution—all from a single evaluation dataset.

A/B testing analysis and ROI reporting evaluate comparative performance between variants and calculate return-on-investment outcomes. The Skill applies weighted scoring and benchmarking logic to quantify the business impact and performance differential of changes, then surfaces results in executive summaries alongside technical metrics.

Benchmarking compares current evaluation scores against baseline metrics using weighted composite scoring across multiple dimensions. The Skill ingests baseline data, applies consistent scoring rules, and reports variance, enabling teams to track improvement or degradation over time.

Security, compliance, and team process evaluation extends beyond code metrics to assess security posture, regulatory compliance, and operational maturity. The Skill consolidates data from these domains into unified scoring and reporting, supporting holistic project assessment beyond technical code quality alone.

AI and LLM output evaluation integrates assessment of generated or assisted code and content into the broader evaluation framework. The Skill applies the same multi-dimensional scoring and reporting pipeline to AI-generated artifacts, enabling unified quality assessment across human and machine-generated work.