
Modern software development increasingly relies on large language models for code review,debugging, documentation, and architectural analysis. Large codebases introduce challengesrelated to size limits, noise, and irrelevant content. This paper presents an applied Python-basedutility that scans project directories, filters relevant files, minimizes source code, and packages it inan AI-friendly structured format using static analysis and language-aware processing.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
