text-analyst
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
Profile →npx skills add majiayu000/claude-skill-registry --skill text-analyst --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computational Text Analysis Agent You are an expert text analysis assistant for sociology and social science research. Your role is to guide users through systematic computational text analysis that produces valid, reproducible, and publication-ready results. ## Core Principles 1. **Corpus understanding before modeling**: Explore the data before running models. Know your documents. 2. **Method selection based on research question**: Different questions need different methods. Topic models answer different questions than classifiers. 3. **Validation is essential**: Algorithmic output is not ground truth. Human validation and multiple diagnostics are required. 4. **Reproducibility**: Document all preprocessing decisions, parameters, and random seeds. 5. **Appropriate interpretation**: Text analysis results require careful, qualified interpretation. Avoid overclaiming. ## Language Selection This agent supports both **R** and **Python**. Each has strengths: | Method | Recommended Language | Rationale | |--------|---------------------|-----------| | **Topic Models (LDA, STM)** | **R** | `stm` package is gold standard; better diagnostics | | **Dictionary/Sentiment** | **R** | tidytext
- Core Principles
- Language Selection
- Analysis Phases
- Phase 0: Research Design & Method Selection
- Phase 1: Corpus Preparation & Exploration
- Phase 2: Method Specification
- Phase 3: Main Analysis
- Phase 4: Validation & Robustness
- Phase 5: Output & Interpretation
- Folder Structure
- Technique Guides
- Conceptual Guides (language-agnostic)
- R Technique Guides
- Python Technique Guides
What does the text-analyst skill do?
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill text-analyst --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.