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uencoder
PIPELINE Text Processing Beginner 2 steps

Lowercase Text and Count Words

Case normalization ensures consistent word counting. This chain normalizes then counts words.

What it solves

Normalize text case then count words for analysis.

Input → Output

Text Sentence Case input → Word Count output across 2 steps.

Runs in

1–2 minutes · fully local in your browser.

Before you start

  • Text input

The pipeline

  1. 1 Text Sentence Case text-sentence-case

    Text Sentence Case — pure client-side tool. Privacy-first, no server round-trip.

  2. 2 Word Count word-count

    Count characters, words, lines, sentences, paragraphs, byte size, and estimate reading time. Free, private, and 100% client-side.

Open in Toolchain

Runs locally in your browser. The recipe defines the pipeline only — your input never leaves this tab.

▶Run this workflow

Enter your input below and click Run. All processing happens locally in your browser — nothing is uploaded.

Pipeline progress

  1. Text Sentence Casetext-sentence-case
  2. Word Countword-count

Sample input

Hello World Foo

Expected output

Characters: 15
Characters (no spaces): 13
Words: 3
Lines: 1
Sentences: 1
Paragraphs: 1
Bytes (UTF-8): 15
Reading time: ~1 min

This pair is executed against the real tools at build time (tests/unit/recipes.test.ts) — the expected output is verified, not illustrative.

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Tools used

Text Sentence Case
Text Sentence Case — pure client-side tool. Privacy-first, no server round-trip.
TOOL
Word Count
Count characters, words, lines, sentences, paragraphs, byte size, and estimate reading time. Free, private, and 100% client-side.
TOOL