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text-corpus-analysis

Name: text-corpus-analysis
Author: danielrosehill

By danielrosehill

Skills for analyzing large text corpora — topic modeling, NER, categorization, taxonomy derivation, word frequency, synonym clustering, parametric and correlation analysis. Supports classical NLP, local LLMs, and cloud LLMs (OpenRouter) with explicit cost-awareness for large runs.

npx claudepluginhub danielrosehill/claude-code-plugins --plugin text-corpus-analysis

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Skills14

categorize-corpus

/categorize-corpus

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.

choose-approach

/choose-approach

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.

correlation-analysis

/correlation-analysis

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.

define-taxonomy

/define-taxonomy

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".

ner-extraction

/ner-extraction

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.

The plugin manifest points to a different repository than the source indexed by ClaudePluginHub.

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Version0.1.0

Stars0

MaintenanceGood

LicenseMIT

Last CommitApr 23, 2026

AddedApr 23, 2026

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danielrosehill11

README

Text-Corpus-Analysis

Claude Code plugin: reusable task definitions for text corpus and topic analysis. Covers categorization, taxonomy development, topic modeling, NER, trend/correlation analysis, and synonym clustering across corpora ranging from a handful of notes to tens of thousands.

Design principles

Three execution lanes per task: classical NLP (cheap, local, deterministic), local LLM (free at inference time, slower, bounded quality), cloud LLM via OpenRouter (best quality, metered cost).
Cost-awareness is first-class. For any skill that can blow up on a 10k-document corpus, the skill must estimate token/$ cost before running and offer a cheaper fallback.
Sampling and stratification are preferred over whole-corpus passes when deriving categories/taxonomies — you rarely need to read every document to find the themes.
Hybrid pipelines beat pure-LLM ones. Use classical NLP to narrow the space (candidate extraction, deduping, clustering), then use an LLM only where judgment is required (labeling, disambiguation).

Skills

Skill	Purpose
`choose-approach`	Decide NLP vs local-LLM vs cloud-LLM for a given task + corpus size. Estimates cost.
`topic-analysis`	Topic clusters and their evolution over time.
`ner-extraction`	Named entity recognition — people, places, orgs.
`trend-analysis`	Temporal trends across topics, entities, or keywords.
`categorize-corpus`	Assign each document to one of N user-defined categories.
`suggest-categories`	Derive N categories from a corpus's dominant themes.
`define-taxonomy`	Build a multi-level category → tag → sub-category taxonomy.
`word-frequency`	Word/token counts, stopword-filtered.
`synonym-cluster`	Find variant spellings / transcription variants of the same concept.
`parametric-analysis`	Summary statistics (avg word length, sentences/doc, etc.).
`correlation-analysis`	Correlate metadata (timestamps, tags) with content features.
`setup-local-llm`	Audit/install a local LLM suitable for corpus work (Ollama).
`setup-openrouter`	Configure OpenRouter access for cloud LLM calls.
`recommend-tools`	Catalog of external libraries/plugins for text corpus work.

Typical workflow

choose-approach — pick the execution lane and estimate cost.
word-frequency / ner-extraction — cheap classical pass to surface candidates.
suggest-categories or define-taxonomy — derive structure from a stratified sample.
categorize-corpus — apply the structure to the whole corpus.
trend-analysis / correlation-analysis — analyze the categorized corpus over time.

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README