v0.8.0

A new closing step on refactor and a new evidence section, hence the minor bump. No entry changes; no configuration to migrate. The visible difference: refactor output comes back leaner, and the fiction review's Voice fit: line moves to the end of the report.

• Added: two closing tests on every refactor, stated once in style-pass.md §4. The deletion test: strike each word or phrase you added; if the sentence still parses and says the same thing, it was filler, delete it. The reversion test: put back what you replaced; if the old wording was sound and said the same in fewer words, keep it. Repair fails both tests and stays (the article a broken sentence needs, the subject of a split run-on, the verb that replaces a nominalization). §4's restorations are allowed only where the same edit removed filler and the passage does not end longer than it began. The evidence is Shan, Lee and Hao 2026 (arXiv 2608.27855v1): across 1,000 human seed documents in five domains, three editor models and 31 prompts, machine editing lowered lexical density sharply (d = −3.10) while barely moving diversity, the reverse of the generation footprint. Measured on English; the extension to other languages is a Sepia inference, and the tests themselves are untested as an intervention. Short professional routes now load that paragraph on refactor (#232)
• Added: research/detectors.md and a Detectors section in research/sources.md: how the strongest commercial detector scores (Pangram's own technical reports, kept separate from measured third-party studies), where third parties found it weak, and which circulating claims trace to no source. Used for one operative sentence only, in the §7 punctuation row: Pangram's 2025 pipeline lower-cased and unidecode-normalized input, which collapses glyph variants toward ASCII without erasing them; the whitelist row still rests on the contradictory measurements. SKILL.md gains one sentence: sepia writes for expert human readers and is tuned to pass no automated detector (#232)
• Changed: the fiction review prints Voice fit: as the last line, after the findings, quoted evidence, and plan it is computed from, so an early count cannot steer them. Wahi 2026 (arXiv 2609.02246, an engineering report) measured the effect on a code-quality judge: rationale before score raised agreement with human experts from 42.6% to 51.9% with no rubric change (#231)
• Changed: README prose in all three languages, each Chinese file written from the English and kept to its own locale's usage. The English text is the maintainer's, with the caveat about what was verified stated once instead of four times, the 95.5% → 93.9% figure scoped to StoryScope's LAMP-edited condition, and the voice-skills section stating external skills and the built-in profile separately; Shan et al. joins the Sources line. Sponsorship: .github/FUNDING.yml and a Support section (#233, #234)

Not in this release: any content-word share, count, or threshold. The first draft of #232 had the executor compute and report one; eight Codex rounds and an architecture review showed a corpus-level effect size cannot be turned into a per-passage test, so the share, its definition and the Content words: line were removed and only the two tests shipped.

Observed, one specimen, not measured evidence: the same lightly AI-polished opinion paragraph refactored through v0.7.0 and through this release, then measured with a fixed English stoplist. Source paragraph content-word share 0.471; v0.7.0 output 0.505; this release 0.645. The new output is shorter with every point kept and applies the deletion test by name in its defect list. Transcripts and the stoplist are in #232.