forensic-skills

Use when planning architecture refactoring, understanding cross-module dependencies, discovering hidden dependencies, finding shotgun surgery patterns, or identifying files that change together - reveals temporal coupling and architectural violations using git history analysis

Use when monitoring code quality over time, measuring refactoring impact, tracking if complexity is improving or worsening, or validating technical debt work - tracks complexity metrics across git history identifying improving, stable, or deteriorating files

Use when investigating merge conflicts, reducing communication overhead, detecting modules with high coordination complexity, or identifying files edited by many contributors and cross-team - reveals coordination bottlenecks and team communication issues

Use when justifying technical debt to executives, calculating the cost of quality issues, translating tech metrics to business language, or planning quality budgets - uses research-backed formulas (2-3x defects, productivity multipliers) to convert code problems into dollars and ROI

Use when planning refactoring priorities, investigating recurring bugs, identifying which files cause the most bugs, or determining problem areas to fix - identifies high-risk files by combining git change frequency with code complexity using research-backed formula (4-9x defect rates)

Use when assessing team resilience, planning for developer departures, calculating bus/truck factor, identifying knowledge silos, or evaluating organizational risk - maps code ownership from git history and identifies single points of failure using research-backed thresholds (>80% ownership = silo)

Use when developer is leaving or new hire onboarding, assessing team resilience, planning for developer departures, calculating bus/truck factor, identifying knowledge silos, or evaluating organizational risk - identifies knowledge gaps and transition risks

Use when team reorganization planning, identifying coordination inefficiencies, validating Conway's Law, assessing team-architecture fit, or planning service splits - analyzes alignment between code module boundaries and team structures revealing organizational bottlenecks

Use when planning refactoring sprints, prioritizing technical debt backlog, justifying refactoring investment to executives, or creating data-driven roadmaps - calculates return on investment using effort-impact matrices and research-backed formulas

Use when investigating test suite issues, reducing CI/CD time, identifying brittle tests, finding test duplication, or analyzing test maintenance burden - reveals test code quality problems through git history analysis

Use when understanding velocity issues, measuring quality improvement efforts, tracking interrupt work, or quantifying technical debt impact - monitors trends in unplanned work (bugs, hotfixes) and correlates with code hotspots