| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 33 | | tagDensity | 0.303 | | leniency | 0.606 | | rawRatio | 0.1 | | effectiveRatio | 0.061 | |
| 92.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1348 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1348 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "stomach" | | 1 | "etched" | | 2 | "quivered" | | 3 | "charm" |
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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 | 0 | | narrationSentences | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 90 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 10 | | totalWords | 1355 | | ratio | 0.007 | | matches | | 0 | "CAMDEN" | | 1 | "CLOSED FOR PRIVATE EVENT" | | 2 | "natural causes" | | 3 | "that" | | 4 | "arranged" | | 5 | "key" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.13% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 892 | | uniqueNames | 7 | | maxNameDensity | 1.46 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Northern | 1 | | Line | 1 | | Tube | 1 | | Patel | 6 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Line" | | 3 | "Patel" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.771 | | windowScore | 0.833 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 2 | | matches | | 0 | "draft that seemed to come from nowhere" | | 1 | "looked like a dozen candles, none of them" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1355 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 89 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 41.06 | | std | 38.82 | | cv | 0.946 | | sampleLengths | | 0 | 110 | | 1 | 56 | | 2 | 3 | | 3 | 37 | | 4 | 2 | | 5 | 85 | | 6 | 6 | | 7 | 63 | | 8 | 77 | | 9 | 1 | | 10 | 3 | | 11 | 134 | | 12 | 15 | | 13 | 9 | | 14 | 4 | | 15 | 7 | | 16 | 72 | | 17 | 87 | | 18 | 32 | | 19 | 4 | | 20 | 4 | | 21 | 98 | | 22 | 9 | | 23 | 79 | | 24 | 7 | | 25 | 43 | | 26 | 19 | | 27 | 99 | | 28 | 9 | | 29 | 71 | | 30 | 36 | | 31 | 3 | | 32 | 71 |
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| 73.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 67 | | matches | | 0 | "been painted" | | 1 | "was dressed" | | 2 | "was arranged" | | 3 | "been taught " | | 4 | "was furred" | | 5 | "was etched" | | 6 | "was polished" | | 7 | "been carved" |
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| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 141 | | matches | | 0 | "were looking" | | 1 | "was not pointing" | | 2 | "was not pointing" |
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| 46.55% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 89 | | ratio | 0.034 | | matches | | 0 | "The station had been a name on a planning document once — a junction on a disused spur of the Northern Line, bricked over in the seventies, forgotten by everyone except the surveyors who still updated its phantom on the Tube map." | | 1 | "Beneath the paint, the ghost of a word — *CAMDEN* — still clung to the wall in faded blue." | | 2 | "Quinn walked the platform before she walked to him, because she had been taught — and had learned the hard way — that the first sweep was the honest one." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 889 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.031496062992125984 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00562429696287964 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 15.22 | | std | 13.54 | | cv | 0.889 | | sampleLengths | | 0 | 27 | | 1 | 22 | | 2 | 42 | | 3 | 19 | | 4 | 29 | | 5 | 15 | | 6 | 12 | | 7 | 3 | | 8 | 33 | | 9 | 4 | | 10 | 2 | | 11 | 24 | | 12 | 19 | | 13 | 9 | | 14 | 2 | | 15 | 10 | | 16 | 7 | | 17 | 7 | | 18 | 7 | | 19 | 6 | | 20 | 3 | | 21 | 13 | | 22 | 20 | | 23 | 27 | | 24 | 17 | | 25 | 3 | | 26 | 2 | | 27 | 8 | | 28 | 32 | | 29 | 1 | | 30 | 14 | | 31 | 1 | | 32 | 3 | | 33 | 15 | | 34 | 30 | | 35 | 5 | | 36 | 12 | | 37 | 23 | | 38 | 29 | | 39 | 20 | | 40 | 5 | | 41 | 10 | | 42 | 5 | | 43 | 4 | | 44 | 4 | | 45 | 7 | | 46 | 48 | | 47 | 24 | | 48 | 7 | | 49 | 19 |
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| 57.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.43820224719101125 | | totalSentences | 89 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 56.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 61 | | matches | | 0 | "It was the look that" | | 1 | "He hesitated, the first time" | | 2 | "He led her down a" | | 3 | "She bent closer, then decided" | | 4 | "She joined him." | | 5 | "Her eyes went to the" | | 6 | "She was dressed in a" | | 7 | "He had not moved from" | | 8 | "She catalogued as she went." | | 9 | "She did not touch it." | | 10 | "She marked its position with" | | 11 | "she said, joining Patel" | | 12 | "Its casing was furred with" | | 13 | "It was not pointing anywhere" | | 14 | "It quivered, faintly, as if" | | 15 | "She let the word settle" | | 16 | "She reached into her pocket" | | 17 | "She had been wearing that" | | 18 | "She had been wearing it" | | 19 | "She put on the glove." |
| | ratio | 0.41 | |
| 25.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 61 | | matches | | 0 | "The lift cage rattled to" | | 1 | "Detective Harlow Quinn stepped out" | | 2 | "The station had been a" | | 3 | "Quinn had never heard of" | | 4 | "DS Patel met her at" | | 5 | "It was the look that" | | 6 | "He hesitated, the first time" | | 7 | "He led her down a" | | 8 | "Someone had scratched a smaller," | | 9 | "The scratchings were not the" | | 10 | "The lines were deliberate, even," | | 11 | "She bent closer, then decided" | | 12 | "The letters read: *CLOSED FOR" | | 13 | "Patel's voice from the platform" | | 14 | "She joined him." | | 15 | "The platform itself was broad" | | 16 | "Her eyes went to the" | | 17 | "The woman lay on her" | | 18 | "She was dressed in a" | | 19 | "That was the wrong word," |
| | ratio | 0.869 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn stepped out onto the abandoned platform, her shoes striking tile that had not felt a commuter in forty years." | | 1 | "She was dressed in a long dark coat and ankle boots, and her hair, dark and lank, was arranged around her head as if someone had taken time to make her presenta…" | | 2 | "And at the spiral's edge, half-hidden under a fallen tile, something that caught the portable light with a greenish wink." | | 3 | "Its casing was furred with a green patina that Quinn recognized as verdigris, the kind that took a generation or more to form." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.121 | | leniency | 0.242 | | rawRatio | 0 | | effectiveRatio | 0 | |