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tools-only (@tools-only) published this skill. Their other Agent Skills are listed on their SkillMD profile.

897 Feature Text Canonicalizer F606824a by tools-only

Name: 897 Feature Text Canonicalizer F606824a
Author: tools-only

File contents

Requirements & Problem Understanding

Problem Statement

What problem are we solving?

PDF and EPUB files often represent the same text using different character sequences (e.g., ligatures like "ﬁ" instead of "fi").
Text extraction from PDF often includes soft hyphens or end-of-line hyphens ("word- \n wrap") that break word comparison.
To perform accurate validation (completeness and order checks), we need a "canonical" version of the text that is identical regardless of the source file's quirks.

Goals & Objectives

What do we want to achieve?

Unicode Normalization: Use NFKC to unify character representations.
Ligature Resolution: Decompose ligatures (e.g., "ﬂ" -> "fl", "æ" -> "ae").
Hyphenation Cleanup: Remove end-of-line hyphens and unify various hyphen characters.
Whitespace Handling: While text_segmenter handles basic whitespace, the canonicalizer should ensure consistent encoding.
Idempotency: Running the canonicalizer multiple times should not change the output after the first run.

User Stories & Use Cases

How will users interact with the solution?

As a developer, I want to pass "extact-from-pdf" text and get "clean-compare-ready" text.
As a validator, I want to be sure that canonicalize(pdf_text) == canonicalize(epub_text) if the words are the same.
As a system, I want two modes: conservative (safe) and aggressive (for heavy OCR/PDF artifacts).

Success Criteria

How will we know when we're done?

pdf_to_epub/validation/text_canonicalizer.py is implemented.
Ligatures are correctly expanded.
End-of-line hyphens are removed.
100% test coverage including complex Unicode edge cases.

Constraints & Assumptions

What limitations do we need to work within?

Must be fast enough for large texts.
Should not remove meaningful punctuation (unless specified in aggressive mode).
Assumption: Input is UTF-8 encoded string.

Questions & Open Items

What do we still need to clarify?

Do we need to handle specific OCR artifacts like "rn" instead of "m"? (Decision: Keep it for 'aggressive' mode only).
Should we lowercase by default? (Decision: No, let the specific checker decide on case-sensitivity).

---name: 897-feature-text-canonicalizer-f606824adescription: Define requirements for text normalization to ensure consistent comparison between PDF and EPUB.---# Requirements & Problem Understanding## Problem Statement**What problem are we solving?**- PDF and EPUB files often represent the same text using different character sequences (e.g., ligatures like "ﬁ" instead of "fi").- Text extraction from PDF often includes soft hyphens or end-of-line hyphens ("word- \n wrap") that break word comparison.- To perform accurate validation (completeness and order checks), we need a "canonical" version of the text that is identical regardless of the source file's quirks.## Goals & Objectives**What do we want to achieve?**- **Unicode Normalization:** Use NFKC to unify character representations.- **Ligature Resolution:** Decompose ligatures (e.g., "ﬂ" -> "fl", "æ" -> "ae").- **Hyphenation Cleanup:** Remove end-of-line hyphens and unify various hyphen characters.- **Whitespace Handling:** While `text_segmenter` handles basic whitespace, the canonicalizer should ensure consistent encoding.- **Idempotency:** Running the canonicalizer multiple times should not change the output after the first run.## User Stories & Use Cases**How will users interact with the solution?**- As a developer, I want to pass "extact-from-pdf" text and get "clean-compare-ready" text.- As a validator, I want to be sure that `canonicalize(pdf_text) == canonicalize(epub_text)` if the words are the same.- As a system, I want two modes: `conservative` (safe) and `aggressive` (for heavy OCR/PDF artifacts).## Success Criteria**How will we know when we're done?**- `pdf_to_epub/validation/text_canonicalizer.py` is implemented.- Ligatures are correctly expanded.- End-of-line hyphens are removed.- 100% test coverage including complex Unicode edge cases.## Constraints & Assumptions**What limitations do we need to work within?**- Must be fast enough for large texts.- Should not remove meaningful punctuation (unless specified in aggressive mode).- Assumption: Input is UTF-8 encoded string.## Questions & Open Items**What do we still need to clarify?**- Do we need to handle specific OCR artifacts like "rn" instead of "m"? (Decision: Keep it for 'aggressive' mode only).- Should we lowercase by default? (Decision: No, let the specific checker decide on case-sensitivity).

Frequently asked questions

897 Feature Text Canonicalizer F606824a