| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.405 | | leniency | 0.811 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1267 | | totalAiIsmAdverbs | 2 | | 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) | |
| 96.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1267 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 76 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1275 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.35% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 954 | | uniqueNames | 9 | | maxNameDensity | 1.15 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Morris | 1 | | Quinn | 11 | | Tube | 1 | | Christopher | 1 | | Vauxhall | 1 | | Herrera | 4 |
| | persons | | 0 | "Morris" | | 1 | "Quinn" | | 2 | "Christopher" | | 3 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Vauxhall" |
| | globalScore | 0.923 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like fishing lead" |
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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 | 1275 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 25 | | std | 20.67 | | cv | 0.827 | | sampleLengths | | 0 | 32 | | 1 | 54 | | 2 | 13 | | 3 | 49 | | 4 | 4 | | 5 | 2 | | 6 | 52 | | 7 | 15 | | 8 | 52 | | 9 | 18 | | 10 | 19 | | 11 | 1 | | 12 | 32 | | 13 | 5 | | 14 | 27 | | 15 | 30 | | 16 | 34 | | 17 | 9 | | 18 | 48 | | 19 | 11 | | 20 | 49 | | 21 | 34 | | 22 | 41 | | 23 | 8 | | 24 | 7 | | 25 | 69 | | 26 | 4 | | 27 | 52 | | 28 | 50 | | 29 | 15 | | 30 | 4 | | 31 | 5 | | 32 | 72 | | 33 | 4 | | 34 | 6 | | 35 | 2 | | 36 | 33 | | 37 | 8 | | 38 | 63 | | 39 | 7 | | 40 | 19 | | 41 | 7 | | 42 | 30 | | 43 | 27 | | 44 | 4 | | 45 | 23 | | 46 | 8 | | 47 | 30 | | 48 | 69 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 157 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 97 | | ratio | 0.093 | | matches | | 0 | "The rain had been falling since six, and by half eleven Camden High Street ran like a gutter in a slaughterhouse — everything sliding downhill, glossy and orange under the sodium lamps." | | 1 | "He turned left at the lock, past the shuttered stalls, and that was when he made her — a glance at a puddle, at the reflection of a woman in a wet wool coat who did not belong to that street at that hour." | | 2 | "\"I know you're alone.\" He looked back for half a second — warm brown eyes, wide, and not with fear of her." | | 3 | "Control, this is DS Quinn, I'm in foot pursuit—" | | 4 | "Beyond was a construction yard, then a stairwell going down — a proper one, tiled, cream and oxblood, the tiles of a Tube station that had closed before her mother was born." | | 5 | "She could hear her own heart and, behind the felt, a sound she couldn't organise into anything — a low churn like a market crowd played at the wrong speed, and a bell, and something that might have been laughter and might have been a bird." | | 6 | "\"Three years ago my partner came out of Camden at four in the morning with her wrists opened and her shoes on the wrong feet.\" Quinn's voice stayed level; she'd practised." | | 7 | "The thing had lived in her wallet for three years, and before that in a manila envelope stamped PERSONAL EFFECTS — DS E." | | 8 | "Not from wind — there was no wind — but as if something on the other side had leaned against it and thought better of coming through." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 952 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.018907563025210083 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0031512605042016808 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 13.14 | | std | 11.89 | | cv | 0.905 | | sampleLengths | | 0 | 32 | | 1 | 6 | | 2 | 18 | | 3 | 30 | | 4 | 6 | | 5 | 7 | | 6 | 44 | | 7 | 5 | | 8 | 3 | | 9 | 1 | | 10 | 2 | | 11 | 3 | | 12 | 18 | | 13 | 31 | | 14 | 15 | | 15 | 4 | | 16 | 17 | | 17 | 10 | | 18 | 21 | | 19 | 7 | | 20 | 11 | | 21 | 9 | | 22 | 10 | | 23 | 1 | | 24 | 24 | | 25 | 8 | | 26 | 5 | | 27 | 22 | | 28 | 5 | | 29 | 21 | | 30 | 9 | | 31 | 6 | | 32 | 3 | | 33 | 25 | | 34 | 9 | | 35 | 17 | | 36 | 21 | | 37 | 3 | | 38 | 7 | | 39 | 11 | | 40 | 2 | | 41 | 32 | | 42 | 10 | | 43 | 5 | | 44 | 34 | | 45 | 6 | | 46 | 3 | | 47 | 32 | | 48 | 5 | | 49 | 2 |
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| 80.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5051546391752577 | | totalSentences | 97 | | uniqueOpeners | 49 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Twice he stopped at shop" | | 1 | "Somewhere below, feet on stone." |
| | ratio | 0.03 | |
| 46.87% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 67 | | matches | | 0 | "Her radio hissed against her" | | 1 | "She thumbed the volume down" | | 2 | "He turned left at the" | | 3 | "She saw his jaw shift." | | 4 | "He was quick." | | 5 | "Her voice cracked off the" | | 6 | "He vaulted a bin." | | 7 | "She went round it, lost" | | 8 | "Her lungs burned in a" | | 9 | "She'd stopped smoking three years" | | 10 | "He didn't turn his head" | | 11 | "He hurdled a low chain" | | 12 | "He looked back for half" | | 13 | "He cut down a side" | | 14 | "She let the radio drop" | | 15 | "He went through." | | 16 | "She took the stairs three" | | 17 | "She could hear her own" | | 18 | "He was breathing hard" | | 19 | "His hand had gone up" |
| | ratio | 0.433 | |
| 49.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 67 | | matches | | 0 | "The rain had been falling" | | 1 | "Quinn kept forty feet behind" | | 2 | "Herrera walked with his shoulders" | | 3 | "Her radio hissed against her" | | 4 | "She thumbed the volume down" | | 5 | "He turned left at the" | | 6 | "She saw his jaw shift." | | 7 | "He was quick." | | 8 | "Quinn went after him with" | | 9 | "Her voice cracked off the" | | 10 | "He vaulted a bin." | | 11 | "She went round it, lost" | | 12 | "Her lungs burned in a" | | 13 | "She'd stopped smoking three years" | | 14 | "He didn't turn his head" | | 15 | "The words came back over" | | 16 | "He hurdled a low chain" | | 17 | "He looked back for half" | | 18 | "He cut down a side" | | 19 | "She let the radio drop" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 44.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn went after him with her coat flapping open, water coming up through her left shoe where the sole had split a month ago and she'd never got round to it." | | 1 | "Beyond was a construction yard, then a stairwell going down — a proper one, tiled, cream and oxblood, the tiles of a Tube station that had closed before her mot…" | | 2 | "She could hear her own heart and, behind the felt, a sound she couldn't organise into anything — a low churn like a market crowd played at the wrong speed, and …" | | 3 | "Not from wind — there was no wind — but as if something on the other side had leaned against it and thought better of coming through." |
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| 58.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 2 | | matches | | 0 | "Her voice cracked, useless" | | 1 | "He said, the way a man says NHS number" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 37 | | tagDensity | 0.162 | | leniency | 0.324 | | rawRatio | 0.167 | | effectiveRatio | 0.054 | |