| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said finally [finally]" |
| | dialogueSentences | 9 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 89.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1475 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "loosely" | | 2 | "utterly" |
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| 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) | |
| 69.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1475 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flicker" | | 1 | "calculating" | | 2 | "lurched" | | 3 | "stomach" | | 4 | "rhythmic" | | 5 | "mechanical" | | 6 | "resolving" | | 7 | "familiar" | | 8 | "measured" |
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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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 61 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1485 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1441 | | uniqueNames | 15 | | maxNameDensity | 0.62 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Quinn | 7 | | Brewer | 1 | | Street | 3 | | Saint | 2 | | Christopher | 2 | | Herrera | 9 | | Wardour | 1 | | Oxford | 1 | | Camden | 2 | | Asante | 2 | | Road | 1 | | Morris | 4 | | Tube | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Morris" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Wardour" | | 3 | "Oxford" | | 4 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 1 | | matches | | 0 | "not quite but recalculating" |
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| 65.32% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.347 | | wordCount | 1485 | | matches | | 0 | "not panicked, not quite, but recalculating" | | 1 | "not quite, but recalculating" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 39.08 | | std | 31.99 | | cv | 0.819 | | sampleLengths | | 0 | 24 | | 1 | 70 | | 2 | 3 | | 3 | 18 | | 4 | 29 | | 5 | 80 | | 6 | 60 | | 7 | 10 | | 8 | 77 | | 9 | 6 | | 10 | 3 | | 11 | 2 | | 12 | 98 | | 13 | 42 | | 14 | 68 | | 15 | 9 | | 16 | 86 | | 17 | 26 | | 18 | 18 | | 19 | 77 | | 20 | 8 | | 21 | 90 | | 22 | 85 | | 23 | 4 | | 24 | 37 | | 25 | 57 | | 26 | 2 | | 27 | 66 | | 28 | 98 | | 29 | 11 | | 30 | 16 | | 31 | 6 | | 32 | 44 | | 33 | 19 | | 34 | 7 | | 35 | 74 | | 36 | 22 | | 37 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 232 | | matches | | 0 | "was already moving" | | 1 | "was calculating" | | 2 | "wasn't running" | | 3 | "was watching" | | 4 | "was leading" | | 5 | "was already pulling" | | 6 | "was not following" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 95 | | ratio | 0.084 | | matches | | 0 | "She'd clocked him the moment she stepped out of the unmarked car on Brewer Street — olive skin, short curly hair dark with rain, a Saint Christopher medallion catching the light as he turned his collar up — and something in the way he catalogued her, the precise flicker of assessment before the decision to run, told her everything she needed to know." | | 1 | "Quinn slapped the bonnet with her palm without slowing, vaulted the white line, and caught a glimpse of Herrera's face as he looked back — not panicked, not quite, but recalculating." | | 2 | "She'd been a detective long enough to know the difference between panic and navigation, and what she was watching was navigation — quick, confident, purposeful." | | 3 | "She'd almost lost him entirely when she saw the medallion catch the light — Saint Christopher spinning as he ducked down a staircase she hadn't registered from street level, the kind of entrance that blended seamlessly into the derelict shopfront above it, boards over the windows, a padlock that was clearly decorative." | | 4 | "She looked back up at the street — empty, slick, indifferent, the rain falling straight and relentless as accusation." | | 5 | "The steps were damp underfoot and the air changed immediately — warmer than it had any right to be, carrying the smell of tallow and something medicinal and sweet, beneath it the older, mineral smell of deep underground." | | 6 | "This one solid iron, set into the tile of what had clearly once been a Tube station — she could see the curve of the original architecture beneath the modifications, the familiar terracotta of an older station long since closed." | | 7 | "Through the iron door came the unmistakable layered sound of a crowd — dozens of people at least, maybe more, the acoustics of a large underground space shaping their voices into something collective and strange." |
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| 91.74% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1436 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 71 | | adverbRatio | 0.04944289693593315 | | lyAdverbCount | 24 | | lyAdverbRatio | 0.016713091922005572 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 15.63 | | std | 13.79 | | cv | 0.882 | | sampleLengths | | 0 | 24 | | 1 | 63 | | 2 | 4 | | 3 | 3 | | 4 | 3 | | 5 | 14 | | 6 | 4 | | 7 | 3 | | 8 | 3 | | 9 | 23 | | 10 | 5 | | 11 | 34 | | 12 | 3 | | 13 | 6 | | 14 | 15 | | 15 | 17 | | 16 | 18 | | 17 | 42 | | 18 | 6 | | 19 | 4 | | 20 | 21 | | 21 | 31 | | 22 | 16 | | 23 | 2 | | 24 | 7 | | 25 | 6 | | 26 | 3 | | 27 | 2 | | 28 | 48 | | 29 | 4 | | 30 | 4 | | 31 | 25 | | 32 | 17 | | 33 | 22 | | 34 | 7 | | 35 | 13 | | 36 | 11 | | 37 | 27 | | 38 | 3 | | 39 | 27 | | 40 | 9 | | 41 | 24 | | 42 | 17 | | 43 | 45 | | 44 | 2 | | 45 | 24 | | 46 | 5 | | 47 | 13 | | 48 | 52 | | 49 | 25 |
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| 42.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.35789473684210527 | | totalSentences | 95 | | uniqueOpeners | 34 | |
| 39.22% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 85 | | matches | | 0 | "Just her and the decision" |
| | ratio | 0.012 | |
| 17.65% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 85 | | matches | | 0 | "She'd clocked him the moment" | | 1 | "They just moved." | | 2 | "She hit the pavement hard," | | 3 | "He didn't stop." | | 4 | "He never stopped." | | 5 | "He took the corner onto" | | 6 | "She'd read his file three" | | 7 | "He knew how bodies worked," | | 8 | "She suspected he was calculating" | | 9 | "She was faster than she" | | 10 | "His warm brown eyes found" | | 11 | "He'd expected to lose her" | | 12 | "She watched him make a" | | 13 | "He turned north." | | 14 | "He had a destination." | | 15 | "She'd been a detective long" | | 16 | "He was leading her somewhere" | | 17 | "She pulled her phone with" | | 18 | "She didn't wait for the" | | 19 | "She shoved the phone back" |
| | ratio | 0.506 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 85 | | matches | | 0 | "The green neon of The" | | 1 | "She'd clocked him the moment" | | 2 | "They just moved." | | 3 | "She hit the pavement hard," | | 4 | "He didn't stop." | | 5 | "He never stopped." | | 6 | "Tomás Herrera was not drunk." | | 7 | "He took the corner onto" | | 8 | "A former paramedic." | | 9 | "She'd read his file three" | | 10 | "He knew how bodies worked," | | 11 | "She suspected he was calculating" | | 12 | "Quinn kept her jaw set" | | 13 | "The rain had intensified in" | | 14 | "She was faster than she" | | 15 | "People always underestimated that." | | 16 | "The gap closed by half" | | 17 | "Quinn slapped the bonnet with" | | 18 | "His warm brown eyes found" | | 19 | "He'd expected to lose her" |
| | ratio | 0.8 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 26.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 8 | | matches | | 0 | "He took the corner onto Wardour Street without breaking stride, his long legs eating up the distance with the practiced efficiency of someone who'd spent years …" | | 1 | "The rain had intensified in the last hour, turning from drizzle to something with genuine intent, and Oxford Street was a chaos of umbrellas and late buses and …" | | 2 | "The realisation sat strangely in her chest as she followed, matching him turn for turn through the narrowing backstreets, past shuttered restaurants and the sou…" | | 3 | "Herrera barely paused at the kerb, timing the gap between a lorry and a night bus with the kind of split-second calculation that made Quinn's stomach tighten." | | 4 | "Her breathing was controlled, rhythmic, the product of early mornings she'd never quite managed to stop keeping after Morris died, as though discipline was the …" | | 5 | "She'd almost lost him entirely when she saw the medallion catch the light — Saint Christopher spinning as he ducked down a staircase she hadn't registered from …" | | 6 | "What had once been emergency lighting, maybe, reduced now to a sickly glow that was just about sufficient to confirm the steps existed." | | 7 | "She thought of Herrera's files: the revoked medical licence, the patient records that didn't correspond to any NHS admission in the system, the names in his pho…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 2 | | matches | | 0 | "She hit, her boots sending up shallow sprays of water" | | 1 | "she told, and her voice was quiet and entirely serious, the way it got when she meant every syllable" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |