From text-corpus-analysis
Build a multi-level taxonomy (categories → tags → sub-categories) from a text corpus. Use when the user wants more than a flat category list — e.g. "give me a hierarchical taxonomy for my tech notes" or "categories, tags, and sub-tags for this corpus of GitHub repos".
npx claudepluginhub danielrosehill/claude-code-plugins --plugin text-corpus-analysisThis skill uses the workspace's default tool permissions.
Multi-level classification scheme. Categories at the top, tags or sub-categories beneath.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Analyzes multiple pages for keyword overlap, SEO cannibalization risks, and content duplication. Suggests differentiation, consolidation, and resolution strategies when reviewing similar content.
Share bugs, ideas, or general feedback.
Multi-level classification scheme. Categories at the top, tags or sub-categories beneath.
suggest-categories: produce N top-level categories with a stratified sample.categorize-corpus pass).suggest-categories on each subset with appropriate k.ner-extraction + keyphrase extraction via KeyBERT or YAKE). Tags cross-cut categories.taxonomy:
- category: Infrastructure
definition: ...
sub_categories:
- name: Containers
examples: [...]
- name: CI/CD
- category: AI & ML
...
tags: # facets, orthogonal to tree
- language/python
- language/go
- status/wip
- stage/production
categorize-corpus (category) + tag-assignment pass.Same as suggest-categories — this is a tree of suggest-categories calls, each on a filtered subset. Embedding work dominates; LLM labeling is per-cluster, not per-doc.