| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1315 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 58.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1315 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "pulsed" | | 1 | "resolved" | | 2 | "lurched" | | 3 | "throbbed" | | 4 | "warmth" | | 5 | "whisper" | | 6 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 159 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 159 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 8 | | totalWords | 1315 | | ratio | 0.006 | | matches | | 0 | "Richmond, standing stones, please Rory, don't tell anyone." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1293 | | uniqueNames | 15 | | maxNameDensity | 1.01 | | worstName | "Eva" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 11 | | Eva | 13 | | Golden | 1 | | Empress | 1 | | Park | 1 | | Southfields | 1 | | Fae | 1 | | Grove | 1 | | Richmond | 3 | | Victorian | 1 | | November | 3 | | Battersea | 1 | | Rise | 1 | | Heartstone | 1 | | Hel | 2 |
| | persons | | | places | | 0 | "Golden" | | 1 | "Park" | | 2 | "Fae" | | 3 | "Grove" | | 4 | "Richmond" | | 5 | "November" | | 6 | "Battersea" | | 7 | "Heartstone" | | 8 | "Hel" |
| | globalScore | 0.997 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like deer prints but too long in t" |
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| 47.91% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.521 | | wordCount | 1315 | | matches | | 0 | "Not bright, but enough" | | 1 | "no light but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 20.55 | | std | 20.7 | | cv | 1.008 | | sampleLengths | | 0 | 9 | | 1 | 31 | | 2 | 4 | | 3 | 27 | | 4 | 103 | | 5 | 11 | | 6 | 41 | | 7 | 25 | | 8 | 46 | | 9 | 7 | | 10 | 7 | | 11 | 2 | | 12 | 8 | | 13 | 67 | | 14 | 3 | | 15 | 54 | | 16 | 10 | | 17 | 4 | | 18 | 18 | | 19 | 19 | | 20 | 4 | | 21 | 34 | | 22 | 11 | | 23 | 19 | | 24 | 3 | | 25 | 4 | | 26 | 60 | | 27 | 5 | | 28 | 32 | | 29 | 12 | | 30 | 14 | | 31 | 7 | | 32 | 1 | | 33 | 54 | | 34 | 34 | | 35 | 4 | | 36 | 35 | | 37 | 14 | | 38 | 2 | | 39 | 15 | | 40 | 13 | | 41 | 9 | | 42 | 65 | | 43 | 14 | | 44 | 2 | | 45 | 5 | | 46 | 8 | | 47 | 16 | | 48 | 8 | | 49 | 67 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 159 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 204 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 164 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 45 | | adverbRatio | 0.03474903474903475 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003861003861003861 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 8.02 | | std | 6.61 | | cv | 0.825 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 15 | | 3 | 4 | | 4 | 4 | | 5 | 5 | | 6 | 11 | | 7 | 7 | | 8 | 5 | | 9 | 23 | | 10 | 2 | | 11 | 6 | | 12 | 34 | | 13 | 13 | | 14 | 8 | | 15 | 4 | | 16 | 8 | | 17 | 6 | | 18 | 5 | | 19 | 5 | | 20 | 18 | | 21 | 18 | | 22 | 10 | | 23 | 2 | | 24 | 4 | | 25 | 6 | | 26 | 3 | | 27 | 21 | | 28 | 15 | | 29 | 10 | | 30 | 4 | | 31 | 2 | | 32 | 1 | | 33 | 7 | | 34 | 2 | | 35 | 8 | | 36 | 14 | | 37 | 7 | | 38 | 22 | | 39 | 14 | | 40 | 5 | | 41 | 5 | | 42 | 3 | | 43 | 23 | | 44 | 7 | | 45 | 8 | | 46 | 16 | | 47 | 4 | | 48 | 1 | | 49 | 5 |
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| 53.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.36585365853658536 | | totalSentences | 164 | | uniqueOpeners | 60 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 136 | | matches | | 0 | "Somewhere behind the stones, something" | | 1 | "Just a tick at the" | | 2 | "Still half full." | | 3 | "Then the wrongness tipped." | | 4 | "Then a voice, familiar and" |
| | ratio | 0.037 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 136 | | matches | | 0 | "She hit the leaf litter" | | 1 | "It rolled, throwing wild shadows" | | 2 | "She scrambled for it." | | 3 | "Her breath plumed and vanished" | | 4 | "She had slipped through a" | | 5 | "She knew Eva wouldn't come" | | 6 | "She had left Southfields at" | | 7 | "Her thumb found the pendant" | | 8 | "She kept it because it" | | 9 | "It was hot now." | | 10 | "It pulsed once, slow, against" | | 11 | "She had brushed past maps" | | 12 | "She counted nine." | | 13 | "Her voice cracked" | | 14 | "She stepped into the circle." | | 15 | "Her trainers sank a little" | | 16 | "She tried to laugh and" | | 17 | "She could see her own" | | 18 | "She could see, beyond that," | | 19 | "Her eyes adjusted slowly." |
| | ratio | 0.25 | |
| 88.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 101 | | totalSentences | 136 | | matches | | 0 | "The brambles snagged Rory's ankle" | | 1 | "She hit the leaf litter" | | 2 | "It rolled, throwing wild shadows" | | 3 | "She scrambled for it." | | 4 | "The grove swallowed sound wrong." | | 5 | "Leaves crunched too loud under" | | 6 | "Her breath plumed and vanished" | | 7 | "The directions had been nonsense." | | 8 | "A text from Eva, nearly" | | 9 | "Eva's phone went straight to" | | 10 | "Rory had knocked off her" | | 11 | "She had slipped through a" | | 12 | "She knew Eva wouldn't come" | | 13 | "Eva hated night walks." | | 14 | "Eva hated parks after dark" | | 15 | "That was the first wrong" | | 16 | "The second was the quiet." | | 17 | "Richmond Park was never quiet." | | 18 | "Here, inside the old oaks," | | 19 | "Rory got her feet under" |
| | ratio | 0.743 | |
| 36.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 136 | | matches | | 0 | "Even at night there was" |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "Rory had knocked off her shift at the Golden Empress, still in the black delivery jacket that smelled of fried noodles, and taken two buses and then walked beca…" | | 1 | "The stones shouldered out of darkness in a rough circle, their backs bearded with lichen that glowed faint green in the phone light." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |