bookforge-modularity-health-evaluatorAssess code modularity health using quantitative metrics — cohesion (LCOM), coupling (afferent/efferent), abstractness, instability, distance from main seque...
Install via ClawdBot CLI:
clawdbot install quochungto/bookforge-modularity-health-evaluatorGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
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https://github.com/bookforge-ai/bookforge-skills/tree/main/books/fundamentals-ofAudited Apr 18, 2026 · audit v1.0
Generated May 7, 2026
A large transaction processing class in a banking monolith has become a maintenance nightmare with hundreds of methods. Use the skill to calculate LCOM and afferent/efferent coupling, revealing excessive responsibilities and high coupling, then guide refactoring to split the class into cohesive modules.
An e-commerce company's shared utility package is imported by nearly every module, causing cascading failures during updates. The skill measures abstractness, instability, and distance from the main sequence to determine the module is in the 'zone of pain', prompting extraction of autonomous microservices.
A healthcare system is migrating from a monolith to microservices but unsure where to draw boundaries. Apply connascence taxonomy analysis on modules handling patient records and billing to identify high-connascence clusters that should stay together, and low-connascence interfaces for separation.
A legacy CRM has a low abstractness, high instability package that frequently breaks. Use LCOM and Ca/Ce metrics to pinpoint classes with low cohesion, then recommend how to increase abstractness through interfaces, stabilizing the module and improving maintainability.
A real-time data pipeline has modules with cyclic dependencies, causing unpredictable latency spikes. The skill identifies connascence of position and algorithm violations, guiding the team to remove cycles and enforce hierarchical dependencies for better performance.
Offer modularity health assessments as a service to development teams, providing a quantified scorecard of coupling, cohesion, and connascence, along with actionable refactoring plans.
Package the modularity metric analysis into a plugin for IDEs like VS Code or IntelliJ, offering a freemium model with basic metrics and a subscription for advanced connascence and dependency graph analysis.
Integrate the analysis into CI/CD pipelines (e.g., GitLab CI, Jenkins) to automatically detect modularity regression on every pull request, sold as a SaaS add-on for DevOps teams.
💬 Integration Tip
Leverage existing static analysis tools (e.g., SonarQube, JDepend) to extract raw dependency and method-level data, then feed into this skill for specialized modularity metrics. Ensure the team understands the metrics' semantics (e.g., LCOM, distance from main sequence) for actionable insights.
Scored May 7, 2026
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