quality-metrics

The Quality Metrics skill provides a sophisticated framework for AI agents to measure, visualize, and enforce software quality standards throughout the SDLC. Designed for quality engineers and DevOps teams, it moves beyond vanity metrics by focusing on outcome-based data points such as defect escape rates, Mean Time to Detection (MTTD), and DORA metrics. The skill allows agents to define and evaluate complex quality gates that automatically block substandard code commits, pull requests, or deployments based on configurable thresholds for coverage, test pass rates, security vulnerabilities, and performance latency. It is particularly effective when integrated into CI/CD pipelines to ensure that quality thresholds remain consistent as projects scale across multiple environments.

• Automated tracking of key performance indicators (KPIs) including bug escape rate, test effectiveness ratio, and DORA metrics.

• Intelligent quality gate configuration with support for blocking and warning thresholds across commit, PR, and release phases.

• Multi-agent coordination capabilities, integrating with domain-specific agents such as qe-quality-analyzer, qe-test-executor, and qe-coverage-analyzer.

• Trend analysis and predictive modeling for quality metrics over configurable timeframes (e.g., 90-day windows).

• Automated dashboard generation compatible with monitoring platforms like Grafana.

• Risk-informed metrics that correlate test coverage gaps with code importance.

• Use this skill when establishing new quality dashboards or refining existing CI/CD gate policies.

• Input data typically involves build artifacts, test execution logs, and historical defect tracking records.

• Expected outputs include trend analysis reports, gate evaluation results, and auto-generated quality dashboards.

• Constraints: Avoid relying solely on activity-based metrics like total test count; prioritize outcomes-based measurements for better reliability.

• Ensure consistent use of the memory namespace 'aqe/quality-metrics/' to maintain cross-session historical data for trend analysis and alert management.

• Integrate with other skills such as risk-based-testing and shift-right-testing for a holistic view of system stability and reliability.