Identify redundant and duplicate content across documentation trees using heading and text similarity.
npx skills add https://github.com/murillodutt/cellm --skill redundancy
Please help me install this Agent Skill. Skill: Redundancy Source: https://github.com/murillodutt/cellm/tree/main/docops/skills/redundancy Command: npx skills add https://github.com/murillodutt/cellm --skill redundancy
Documentation ecosystems grow noisy with duplicated and near-duplicate content across files and sections. This skill identifies structural overlap and exact duplicates to help teams maintain a clean, navigable knowledge base.
Run a scan across the documentation tree to surface duplication opportunities and suggested consolidation paths.
High-intent search queries and answers about installing and using this skill.
To find duplicate content across a documentation tree, run a scan that compares heading structures, topics, and text similarity. This surfaces structural overlap and exact duplicates to help maintain a clean, navigable knowledge base.
Structural similarity detection works by comparing headings using Jaccard similarity to surface near-duplicates. It groups content by topics extracted from titles, frontmatter, and first paragraphs to identify overlapping documentation sections.
Yes, you can consolidate redundant runbooks without modifying source files. The scan provides read-only analysis with severity-driven guidance, suggesting strategies for merging or linking duplicates without altering the original documents.
The best way to streamline large product docs with repeated sections is to scan for duplication opportunities and collapse redundant content into centralized references using the provided consolidation guidance.
Yes, the documentation duplication analysis supports large knowledge bases and runbooks. It identifies structural overlap across large docs repositories where consistency and consolidation are needed.
K-Dense-AIParse FCS files and extract event data as NumPy arrays.
lucasreydmanGenerate and update project CLAUDE.md files with Serena and Sequential Thinking guidance.
dandyeGroup documents by content similarity and topic using NLP vectorization and labeling.
ataschzEmbed text into Gemini vector representations for retrieval and semantic search.
