nlp-toolkit-guide
NLP analysis with perplexity scoring, burstiness, and entropy metrics
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill nlp-toolkit-guide --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.
# NLP Toolkit Guide ## Overview Natural Language Processing research requires a diverse set of analytical tools beyond standard model training. Text quality assessment, AI-generated text detection, linguistic feature extraction, and corpus analysis all depend on well-understood metrics: perplexity, burstiness, entropy, and their variants. This guide provides practical implementations of these core NLP metrics alongside patterns for tokenization, embedding analysis, and text feature engineering. The focus is on metrics used in active research areas -- AI text detection (perplexity + burstiness classifiers), information-theoretic analysis of corpora, and linguistic diversity measurement. These tools are framework-agnostic where possible, but leverage Hugging Face Transformers for language model operations and standard Python scientific computing libraries for statistical analysis. ## Perplexity Scoring Perplexity measures how well a language model predicts a text. Lower perplexity means the text is more predictable to the model -- a key signal in AI text detection, model evaluation, and domain adaptation. ```python import torch import numpy as np from transformers import AutoModelFor
- Overview
- Perplexity Scoring
- Burstiness Analysis
- Entropy and Information-Theoretic Metrics
- AI Text Detection Pipeline
- Tokenization Patterns
- Best Practices
- References
What does the nlp-toolkit-guide skill do?
NLP analysis with perplexity scoring, burstiness, and entropy metrics
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill nlp-toolkit-guide --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.