Method and limitations

1. Read UTF-8 input (an optional UTF-8 BOM is accepted).

2. Apply Unicode case folding and normalize curly apostrophes.

3. Extract letter sequences, including internal apostrophes.

4. Count all words with collections.Counter.

5. Sort by descending frequency, then alphabetically for ties.

6. Save a JSON record; combine records into an HTML report.

What is a word?

Git git produces two occurrences of git. Код код produces two occurrences of код. Numbers and underscores separate words. The tokenizer is educational: it does not perform linguistic lemmatization.

Raw Markdown, not rendered prose

URLs, code snippets, image descriptions and Markdown metadata are included. Common words such as the are not filtered. Do not interpret this analysis as a reading-quality score or a semantic comparison between frameworks.

Repeatability

The source snapshots are committed with SHA-256 checksums and upstream commit references. The analyzer performs no downloads. Identical input bytes and parameters under the same Python environment produce identical JSON.

The report includes every *.json in its input folder. Use a dedicated result folder for each dataset; old results remain until deliberately removed. The report’s total counts word occurrences across files, not unique vocabulary across the whole collection. Each bar chart has its own scale.

Errors

Missing files, invalid UTF-8 and non-positive --top values produce a nonzero exit code. A report with no input JSON files is an error. An empty document is valid and has zero words.