text-corpus-analysis

Assign each document in a corpus to one of N user-defined categories. Use when the user has a fixed taxonomy (e.g. 10-20 labels) and wants every note/document routed into exactly one (or top-k) of them. Supports zero-shot classifiers, local LLMs, and cloud LLMs with cost-aware batching.

Decide whether a corpus analysis task should use classical NLP, a local LLM, or a cloud LLM (OpenRouter) given corpus size, task complexity, and cost tolerance. Use first, before any other skill in this plugin, especially when the corpus is large (thousands+ of documents) or when an LLM pass could get expensive.

Correlate metadata (timestamps, tags, source, author) with content features (topics, entities, length, sentiment) to surface non-obvious patterns. Use when the user asks "does X correlate with Y in my corpus" or wants to discover relationships between when/where/who and what.

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".

Extract named entities (people, places, organizations, dates, products) from a text corpus. Use when the user wants to know "who and where is mentioned" or needs a list of entities for downstream indexing, linking, or trend analysis.

Compute summary statistics over a text corpus — average word length, words/doc, sentences/doc, lexical diversity, readability scores, token length distributions. Use when the user wants a quantitative description of the corpus shape rather than its content.

Recommend well-maintained external libraries and tools for text corpus analysis beyond what this plugin ships — classical NLP, topic modeling, corpus indexing, aspect-based sentiment, multilingual analysis. Use when a task calls for something this plugin doesn't do natively, or when the user wants a survey of what's out there.

Audit what local LLM runtimes are installed (Ollama, llama.cpp, vLLM, LM Studio) and suggest/install a model suitable for corpus analysis tasks — classification, labeling, summarization. Use when a skill in this plugin wants a local-LLM lane but nothing is available, or when the user wants to move a cloud workload local for cost/privacy.

Configure OpenRouter as the cloud-LLM backend for skills in this plugin. Use when a skill needs cloud LLM access and the user wants pay-as-you-go routing across Claude, GPT, Gemini, DeepSeek, Llama, Qwen without managing multiple provider keys.

Derive N categories from the dominant themes of a corpus — the user says "give me 10 categories for these 1000 notes" or "propose 20 labels that would cover most of this data". Produces a proposed category list with definitions, coverage estimates, and example documents.

Identify tokens or phrases that refer to the same concept but appear in different forms — transcription variants from voice notes, spelling variants, acronyms vs expansions, aliases. Use on any voice-note or STT-derived corpus before frequency/NER/topic work, or to deduplicate entity lists.

Identify topic clusters in a text corpus and track how those topics evolve over time. Use when the user has a body of notes, voice notes, articles, or documents and wants to know "what is this corpus mostly about" or "how have my interests shifted". Supports classical topic modeling (LDA/BERTopic) and LLM-assisted labeling.

Identify temporal trends across a text corpus — rising/falling topics, entities, or keywords over time. Use after topic-analysis or ner-extraction when the user wants "what am I talking about more / less than before" or "when did X first show up".

Count word/token occurrences across a corpus with stopword filtering, stemming/lemmatization options, and n-gram support. Use when the user wants a simple frequency export — "how often does X come up", "top 100 words in my notes", "bigram frequencies".