| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 77.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1332 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "carefully" | | 1 | "suddenly" | | 2 | "slowly" | | 3 | "softly" | | 4 | "precisely" |
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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) | |
| 84.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1332 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "could feel" | | 2 | "sense of" | | 3 | "methodical" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1342 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1157 | | uniqueNames | 16 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 9 | | Raven | 1 | | Nest | 1 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Morris | 3 | | London | 1 | | Camden | 1 | | Victorian | 1 | | Underground | 1 | | Tube | 1 | | TfL | 1 | | Tomás | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Victorian" | | 8 | "Underground" | | 9 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a derelict entrance, the kind" | | 1 | "quite any colour she had a name for, at the figures bent over things she couldn't make out" |
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| 50.97% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.49 | | wordCount | 1342 | | matches | | 0 | "not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, e" | | 1 | "not municipal anything, but a series of small paper lanterns strung along the ceiling, e" |
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| 57.97% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 92 | | matches | | 0 | "pushed that thought" | | 1 | "understood that she" | | 2 | "registered that wrongness" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 36.27 | | std | 25.96 | | cv | 0.716 | | sampleLengths | | 0 | 54 | | 1 | 43 | | 2 | 7 | | 3 | 6 | | 4 | 113 | | 5 | 8 | | 6 | 90 | | 7 | 61 | | 8 | 45 | | 9 | 14 | | 10 | 67 | | 11 | 19 | | 12 | 66 | | 13 | 37 | | 14 | 49 | | 15 | 13 | | 16 | 46 | | 17 | 14 | | 18 | 13 | | 19 | 27 | | 20 | 4 | | 21 | 65 | | 22 | 53 | | 23 | 6 | | 24 | 24 | | 25 | 49 | | 26 | 62 | | 27 | 40 | | 28 | 18 | | 29 | 38 | | 30 | 6 | | 31 | 65 | | 32 | 7 | | 33 | 37 | | 34 | 6 | | 35 | 28 | | 36 | 42 |
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| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "being told" | | 1 | "been trained" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 215 | | matches | | 0 | "was nursing" | | 1 | "was heading" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 92 | | ratio | 0.098 | | matches | | 0 | "No excess weight, economical movement — he ran like someone who had needed to run before and had learned not to waste energy on panic." | | 1 | "Herrera vaulted a low bollard without breaking stride and Quinn went around it, the worn leather of her watch strap catching on her coat cuff, her left hand automatically pressing it flat — a habit, not a distraction." | | 2 | "Herrera dropped suddenly, almost disappearing, and it took her half a second to understand he'd taken a staircase — concrete steps leading down into the forecourt of what looked like a derelict entrance, the kind of Victorian mouth the old Underground left scattered across the city like forgotten teeth." | | 3 | "The emergency lighting here was wrong — not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, each one burning amber and casting the kind of light that made depth judgement difficult." | | 4 | "Voices — dozens of them, overlapping, speaking languages she could identify and some she couldn't — and under that a smell: woodsmoke, something chemical she associated with hospital dispensaries, and something else, older, mineral, like wet stone and copper." | | 5 | "That was what made her hesitate — the complete absence of threat in it, only a tired, careful certainty." | | 6 | "She thought about Morris — about the way the file had been quietly closed, the way her superintendent had looked at her when she'd pushed, the way every rational explanation had worn thin and given way to something she'd refused to name for three years because naming it meant she'd been standing on uncertain ground all along." | | 7 | "It was warm in her hand in a way that had nothing to do with Herrera's body heat, and she registered that wrongness and filed it precisely, the way she filed everything — methodical, honest, without flinching." | | 8 | "Quinn followed him into the market, her torch still lit, her eyes moving the way they'd been trained to move — systematically, missing nothing — and her notebook already open in her mind, filling with things she didn't yet have language for." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1149 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.02959094865100087 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.012184508268059183 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 14.59 | | std | 12.7 | | cv | 0.871 | | sampleLengths | | 0 | 27 | | 1 | 27 | | 2 | 11 | | 3 | 32 | | 4 | 7 | | 5 | 3 | | 6 | 3 | | 7 | 22 | | 8 | 37 | | 9 | 7 | | 10 | 30 | | 11 | 6 | | 12 | 11 | | 13 | 8 | | 14 | 3 | | 15 | 4 | | 16 | 25 | | 17 | 24 | | 18 | 34 | | 19 | 3 | | 20 | 3 | | 21 | 17 | | 22 | 38 | | 23 | 5 | | 24 | 31 | | 25 | 4 | | 26 | 5 | | 27 | 3 | | 28 | 5 | | 29 | 6 | | 30 | 49 | | 31 | 9 | | 32 | 3 | | 33 | 6 | | 34 | 19 | | 35 | 5 | | 36 | 39 | | 37 | 5 | | 38 | 1 | | 39 | 4 | | 40 | 12 | | 41 | 11 | | 42 | 13 | | 43 | 13 | | 44 | 10 | | 45 | 39 | | 46 | 6 | | 47 | 7 | | 48 | 2 | | 49 | 28 |
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| 55.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.40217391304347827 | | totalSentences | 92 | | uniqueOpeners | 37 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 75 | | matches | | 0 | "Then he reached into his" |
| | ratio | 0.013 | |
| 38.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 75 | | matches | | 0 | "He'd broken when she'd shown" | | 1 | "He didn't stop." | | 2 | "They never did." | | 3 | "She clocked Herrera at the" | | 4 | "She'd been watching him for" | | 5 | "She'd built the profile carefully:" | | 6 | "She pushed that thought down" | | 7 | "She'd noted that too." | | 8 | "She cut through a narrow" | | 9 | "They went north." | | 10 | "He was heading for Camden." | | 11 | "She could feel it in" | | 12 | "He knew about cameras." | | 13 | "Her radio crackled." | | 14 | "She knew that." | | 15 | "She had maps in her" | | 16 | "She drew her extendable baton." | | 17 | "She wanted her torch." | | 18 | "She unclipped it with her" | | 19 | "He stood with his back" |
| | ratio | 0.453 | |
| 26.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 75 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn moved fast," | | 2 | "He'd broken when she'd shown" | | 3 | "One second he was nursing" | | 4 | "He didn't stop." | | 5 | "They never did." | | 6 | "Quinn cut right around a" | | 7 | "She clocked Herrera at the" | | 8 | "She'd been watching him for" | | 9 | "She'd built the profile carefully:" | | 10 | "Incidents that left no forensic" | | 11 | "Incidents that left her lying" | | 12 | "She pushed that thought down" | | 13 | "Herrera was fit." | | 14 | "She'd noted that too." | | 15 | "She cut through a narrow" | | 16 | "They went north." | | 17 | "The crowds thinned." | | 18 | "The neon advertising gave way" | | 19 | "Herrera vaulted a low bollard" |
| | ratio | 0.867 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 3.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 7 | | matches | | 0 | "Detective Harlow Quinn moved fast, her boots splitting puddles, her eyes fixed on the figure thirty metres ahead who had just made the mistake of looking back." | | 1 | "Incidents that left her lying awake at night thinking about Morris." | | 2 | "Quinn hit the top of the stairs without slowing, her hand finding the iron railing, and descended into dark." | | 3 | "The emergency lighting here was wrong — not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, each one burning …" | | 4 | "In the torchlight his face was younger than his file suggested and older in the eyes, which were dark and exhausted and not, she noted, afraid of her." | | 5 | "It was warm in her hand in a way that had nothing to do with Herrera's body heat, and she registered that wrongness and filed it precisely, the way she filed ev…" | | 6 | "Quinn followed him into the market, her torch still lit, her eyes moving the way they'd been trained to move — systematically, missing nothing — and her noteboo…" |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, his accent blurring the word softly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0 | | effectiveRatio | 0 | |