Agent Skills: normalizing-text-for-measurement

Strip, parse, and segment a document before something else measures it — normalizers that fuse two blocks into one word because a close-token emits no separator, the two metric classes failing on different residue — character-based ones (reading time, token budgets, size limits) inflated by leftover syntax, word-based ones (word count, readability) corrupted by boundaries the strip never inserted — sibling consumers that skip the normalizer entirely, parser presets whose rule set silently reclassifies a construct, sections grouped by a non-unique heading key that collapse and double-count into an ancestor, and deliberate lossiness that the next reader mistakes for a bug. Use when writing or reviewing a markdown/HTML stripper, a plain-text extractor, a section or paragraph splitter, a reading-time/word-count/readability/token-count function, a dedup key or checksum built from prose, or a search-index preprocessing step.