Rank ADVPL/TLPP functions by callsite count using fan-in analysis.
npx skills add https://github.com/JoniPraia/plugadvpl --skill plugadvpl-hotspots
Please help me install this Agent Skill. Skill: /plugadvpl:hotspots Source: https://github.com/JoniPraia/plugadvpl/tree/main/skills/hotspots Command: npx skills add https://github.com/JoniPraia/plugadvpl --skill plugadvpl-hotspots
It helps you identify which ADVPL/TLPP functions are most frequently used (by counting callers) so refactors can be prioritized where they will impact the most code.
Run /plugadvpl:hotspots to get the top 20 most-called custom functions (refactor priority) for your project.
High-intent search queries and answers about installing and using this skill.
ADVPL fan-in analysis ranks functions by their total call count to identify refactoring priorities. By counting how many callsites invoke each destination function, it highlights which user functions and class methods have the broadest architectural impact across your codebase.
Yes, you can filter ADVPL call graph analysis to exclude native TOTVS routines by default. This ensures results focus on custom code refactoring, with an option to include native catalog calls when you need a comprehensive architectural audit.
The best way to scope impact analysis for TLPP call types is to apply type-scoped filters. This limits the fan-in ranking to specific categories like user functions, methods, execauto, or execblock, providing targeted impact assessment for your architectural review.
You generate a top-N list of frequently called TLPP functions by running a fan-in impact ranking. This produces an ordered list of destination functions based on total call count, along with the number of unique callsites and files where they appear.
Yes, ADVPL impact analysis includes both class methods and user functions in the same ranking. The fan-in calculation aggregates callsites across all these call paths to deliver a unified refactoring priority list based on total invocations.
You should not use fan-in analysis for ADVPL code quality metrics when you need to assess outgoing dependencies rather than incoming calls. Fan-in strictly measures how often a function is called by others, not the complexity or breadth of what it calls.
babdulhakim2Analyze phone numbers to identify carrier, line type, and risk indicators.
ThalesGroupExecute targeted AGILAB tests across unit, smoke, and regression suites.
quangphu1912Detects circular dependencies, architectural violations, and conflicts in module graphs via static code analysis.
icyzhIndex source code repositories into a knowledge graph with interactive web visualization.
