| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1691 | | 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) | |
| 79.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1691 | | totalAiIsms | 7 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "down her spine" | | 1 | "footsteps" | | 2 | "database" | | 3 | "flicker" | | 4 | "warmth" | | 5 | "pulsed" |
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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 | 110 | | matches | (empty) | |
| 90.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 30 | | totalWords | 1706 | | ratio | 0.018 | | matches | | 0 | "You want to die curious, Quinn, there are cheaper ways." | | 1 | "That's not mine, Detective. And if I were you I'd put it back." | | 2 | "Entry requirement" | | 3 | "You want to die curious" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1672 | | uniqueNames | 20 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Euston | 1 | | Road | 1 | | Harlow | 1 | | Quinn | 13 | | Morris | 4 | | Tesco | 1 | | Metro | 1 | | Soho | 1 | | Tuesday | 1 | | Raven | 2 | | Nest | 2 | | Camden | 2 | | High | 1 | | Street | 1 | | Midlands | 1 | | Static | 1 | | Silas | 2 | | Detective | 1 | | Thames | 1 | | London | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Silas" |
| | places | | 0 | "Euston" | | 1 | "Road" | | 2 | "Tesco" | | 3 | "Soho" | | 4 | "Camden" | | 5 | "High" | | 6 | "Street" | | 7 | "Midlands" | | 8 | "Thames" | | 9 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 1 | | matches | | 0 | "something like a butcher's cellar" |
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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 | 1706 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 44.89 | | std | 36.16 | | cv | 0.805 | | sampleLengths | | 0 | 64 | | 1 | 43 | | 2 | 77 | | 3 | 92 | | 4 | 84 | | 5 | 49 | | 6 | 18 | | 7 | 97 | | 8 | 31 | | 9 | 54 | | 10 | 12 | | 11 | 4 | | 12 | 15 | | 13 | 116 | | 14 | 39 | | 15 | 20 | | 16 | 3 | | 17 | 96 | | 18 | 39 | | 19 | 23 | | 20 | 7 | | 21 | 164 | | 22 | 6 | | 23 | 24 | | 24 | 36 | | 25 | 79 | | 26 | 19 | | 27 | 57 | | 28 | 26 | | 29 | 84 | | 30 | 8 | | 31 | 8 | | 32 | 59 | | 33 | 41 | | 34 | 44 | | 35 | 39 | | 36 | 25 | | 37 | 4 |
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| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 110 | | matches | | 0 | "been thrown" | | 1 | "been pulled" | | 2 | "been kept" | | 3 | "been expected" |
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| 95.42% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 255 | | matches | | 0 | "was getting" | | 1 | "were receding" | | 2 | "wasn't running" | | 3 | "was going" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 112 | | ratio | 0.08 | | matches | | 0 | "Not slow down — stop, dead, at a doorway with no sign above it." | | 1 | "Whatever had taken him three years ago had happened in a place like this — a place that wasn't on any map she'd been able to draw." | | 2 | "A finger bone — human, small, polished to a shine by handling — with a mark burned into the base that matched nothing in any database she'd run." | | 3 | "Underground, Camden's abandoned stations lay in a knot beneath the streets, and she had been in two of them on raids — but this was not one of those." | | 4 | "It stood open, and through it poured light the colour of a low fire — amber and gold and smoke." | | 5 | "A woman with silver hair braided to her waist stood behind a table of stoppered bottles that glowed faintly from within, each one holding its own weather — a curl of fog, a flicker of lightning no bigger than a match head." | | 6 | "A man with too many rings on his fingers sold information from ledgers stacked to his chin, and a queue of — of clients, she made herself think, clients — waited with their hoods up and their faces turned away." | | 7 | "She weighed it in her palm — an ounce, perhaps less — and watched the crowd for any sign that they had noticed her, this woman in a soaked trench coat with a police baton and a dead man's watch and rain in her shoes." | | 8 | "She folded the empty evidence bag and put it away — contamination, chain of custody, all of it already ruined, and she would think about that later — and she walked in." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1661 | | adjectiveStacks | 1 | | stackExamples | | 0 | "slim, cold, irrational chance" |
| | adverbCount | 46 | | adverbRatio | 0.027694160144491272 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.006622516556291391 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 15.23 | | std | 11.79 | | cv | 0.774 | | sampleLengths | | 0 | 18 | | 1 | 4 | | 2 | 42 | | 3 | 3 | | 4 | 15 | | 5 | 15 | | 6 | 10 | | 7 | 18 | | 8 | 10 | | 9 | 24 | | 10 | 6 | | 11 | 3 | | 12 | 16 | | 13 | 5 | | 14 | 39 | | 15 | 32 | | 16 | 4 | | 17 | 12 | | 18 | 32 | | 19 | 16 | | 20 | 14 | | 21 | 6 | | 22 | 16 | | 23 | 10 | | 24 | 39 | | 25 | 5 | | 26 | 13 | | 27 | 11 | | 28 | 14 | | 29 | 27 | | 30 | 27 | | 31 | 10 | | 32 | 8 | | 33 | 8 | | 34 | 3 | | 35 | 12 | | 36 | 4 | | 37 | 4 | | 38 | 4 | | 39 | 38 | | 40 | 12 | | 41 | 1 | | 42 | 9 | | 43 | 2 | | 44 | 3 | | 45 | 1 | | 46 | 9 | | 47 | 6 | | 48 | 18 | | 49 | 34 |
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| 55.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.38392857142857145 | | totalSentences | 112 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 104 | | matches | | 0 | "Twice now he'd looked back," | | 1 | "Then a wash of sound" | | 2 | "Somewhere above, London went on" | | 3 | "Somewhere above, control would be" |
| | ratio | 0.038 | |
| 31.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 104 | | matches | | 0 | "She didn't slow down." | | 1 | "She slapped its bonnet, vaulted" | | 2 | "Her watch, the worn leather" | | 3 | "She tightened the strap with" | | 4 | "He was younger than her" | | 5 | "He clipped a bin outside" | | 6 | "Her radio crackled against her" | | 7 | "She ignored it." | | 8 | "She knew his face now." | | 9 | "She'd first clocked him leaving" | | 10 | "It was the walk." | | 11 | "He walked like a man" | | 12 | "He ducked into the warren" | | 13 | "He pressed something to the" | | 14 | "She was ten feet behind" | | 15 | "She got her fingers in" | | 16 | "She could hear Morris saying" | | 17 | "She still had the case" | | 18 | "She still opened it on" | | 19 | "He was moving fast but" |
| | ratio | 0.471 | |
| 65.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 104 | | matches | | 0 | "The rain came sideways off" | | 1 | "She didn't slow down." | | 2 | "A taxi blared." | | 3 | "She slapped its bonnet, vaulted" | | 4 | "Her watch, the worn leather" | | 5 | "She tightened the strap with" | | 6 | "He was younger than her" | | 7 | "He clipped a bin outside" | | 8 | "Her radio crackled against her" | | 9 | "She ignored it." | | 10 | "Whatever the dispatcher wanted could" | | 11 | "She knew his face now." | | 12 | "She'd first clocked him leaving" | | 13 | "It was the walk." | | 14 | "He walked like a man" | | 15 | "He ducked into the warren" | | 16 | "Quinn took the corner wide," | | 17 | "He pressed something to the" | | 18 | "She was ten feet behind" | | 19 | "She got her fingers in" |
| | ratio | 0.788 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 104 | | matches | | 0 | "Now the suspect stopped at" | | 1 | "If she lost him here," | | 2 | "As though she had been" |
| | ratio | 0.029 | |
| 97.51% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 4 | | matches | | 0 | "She got her fingers in the gap and hauled it open onto a flight of concrete steps descending into a thick, warm dark that smelled of candle smoke and wet stone …" | | 1 | "A woman with silver hair braided to her waist stood behind a table of stoppered bottles that glowed faintly from within, each one holding its own weather — a cu…" | | 2 | "The silver-haired woman at the bottle stall regarded her with an expression that might have been pity and might have been hunger." | | 3 | "Ahead, at the far end of the platform, the grey hood bobbed once and disappeared around a stall stacked with jars of something that moved." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn muttered (mutter)" |
| | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 1 | | effectiveRatio | 0.5 | |