| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |
| 96.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1380 | | totalAiIsmAdverbs | 1 | | 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) | |
| 74.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1380 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "flicker" | | 2 | "lurch" | | 3 | "grave" | | 4 | "glint" | | 5 | "footsteps" | | 6 | "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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1395 | | 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 | 41 | | wordCount | 1389 | | uniqueNames | 21 | | maxNameDensity | 0.58 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 6 | | Vauxhall | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Herrera | 8 | | Morris | 4 | | Frith | 1 | | Street | 1 | | Camden | 2 | | Victorian | 1 | | Tube | 1 | | Catholic | 1 | | Saint | 1 | | Christopher | 1 | | Whitechapel | 2 | | London | 1 | | Tomás | 2 | | Procedure | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Catholic" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Tomás" | | 10 | "Procedure" |
| | places | | 0 | "Vauxhall" | | 1 | "Soho" | | 2 | "Frith" | | 3 | "Street" | | 4 | "Camden" | | 5 | "Victorian" | | 6 | "Whitechapel" | | 7 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "as if steadying something under his shirt" |
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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 | 1395 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 48.1 | | std | 35.92 | | cv | 0.747 | | sampleLengths | | 0 | 79 | | 1 | 6 | | 2 | 86 | | 3 | 12 | | 4 | 71 | | 5 | 50 | | 6 | 50 | | 7 | 2 | | 8 | 25 | | 9 | 116 | | 10 | 71 | | 11 | 11 | | 12 | 2 | | 13 | 88 | | 14 | 5 | | 15 | 79 | | 16 | 21 | | 17 | 69 | | 18 | 92 | | 19 | 129 | | 20 | 15 | | 21 | 57 | | 22 | 16 | | 23 | 64 | | 24 | 15 | | 25 | 72 | | 26 | 57 | | 27 | 19 | | 28 | 16 |
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| 90.44% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 71 | | matches | | 0 | "been locked" | | 1 | "been peeled" | | 2 | "been arranged" |
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| 85.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 232 | | matches | | 0 | "were going" | | 1 | "was descending" | | 2 | "was coming" | | 3 | "was listening" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 15 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 73 | | ratio | 0.151 | | matches | | 0 | "She clocked him the way she clocked everything — by habit, by instinct, by the cold arithmetic of eighteen years on the job." | | 1 | "Her worn leather watch — the one thing she'd kept from her old life, the one thing Morris had never teased her about, because he'd understood what it meant — slid wet against her wrist as she pumped her arms for speed." | | 2 | "Warm brown eyes, wide with something that wasn't guilt exactly — it was closer to fear, but fear with a strange, practiced edge to it, like a man who had rehearsed being afraid and knew what it cost him." | | 3 | "He was fast — paramedic-fast, the kind of fast built by years of hauling stretchers up narrow stairwells — and he knew the streets in a way that her A-to-Z didn't cover, ducking through a service alley, vaulting a stack of pallets, cutting across a car park where the security light strobed over them both in freezing white bursts." | | 4 | "He slipped through a gap where the fence had been peeled back like tin foil, and then he was descending a cracked concrete ramp into the black mouth of an old Tube station — one of the dead ones, she realized, one of the stations the maps had quietly forgotten decades ago." | | 5 | "Her hand went to her hip, found the radio, and found it dead — waterlogged or out of range, she couldn't tell which, and it didn't matter, because both meant the same thing." | | 6 | "There were more of them — dozens — hanging along the fence like a wind chime made of grave goods, and as the rain struck them they clicked together with a sound that was almost musical and entirely wrong." | | 7 | "And Herrera had touched one as he passed — she'd seen it, she realized now, seen his fingers brush a token the way a Catholic touches a saint's foot in a cathedral doorway." | | 8 | "What they'd found afterward — what was left of him, arranged in a way no human being could have been arranged by another human being — had closed the case without closing a single question." | | 9 | "The cord was greasy with rain and something older than rain, and the disc was cold in a way the night couldn't explain — cold like a coin left on a dead man's eyes." | | 10 | "The tunnel swallowed the sound of the city above her one step at a time, and by the tenth step there was no London left at all — only the glow ahead, growing warmer and stranger, and the smell of smoke and copper and things she had no vocabulary for yet." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1383 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same bone-colored glint." |
| | adverbCount | 31 | | adverbRatio | 0.022415039768618944 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005784526391901663 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 19.11 | | std | 14.83 | | cv | 0.776 | | sampleLengths | | 0 | 37 | | 1 | 26 | | 2 | 16 | | 3 | 6 | | 4 | 23 | | 5 | 36 | | 6 | 3 | | 7 | 3 | | 8 | 21 | | 9 | 12 | | 10 | 8 | | 11 | 21 | | 12 | 42 | | 13 | 5 | | 14 | 13 | | 15 | 32 | | 16 | 8 | | 17 | 39 | | 18 | 3 | | 19 | 2 | | 20 | 22 | | 21 | 3 | | 22 | 3 | | 23 | 59 | | 24 | 20 | | 25 | 34 | | 26 | 30 | | 27 | 2 | | 28 | 39 | | 29 | 11 | | 30 | 2 | | 31 | 52 | | 32 | 36 | | 33 | 5 | | 34 | 33 | | 35 | 3 | | 36 | 5 | | 37 | 38 | | 38 | 21 | | 39 | 2 | | 40 | 28 | | 41 | 39 | | 42 | 10 | | 43 | 15 | | 44 | 3 | | 45 | 33 | | 46 | 31 | | 47 | 20 | | 48 | 2 | | 49 | 3 |
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| 42.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3561643835616438 | | totalSentences | 73 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 66 | | matches | | 0 | "Then he walked north, fast," | | 1 | "Then again at the corner," | | 2 | "Then he did something that" | | 3 | "Then he ran." | | 4 | "Somewhere deeper in, a crowd" | | 5 | "Somewhere deeper in, Tomás Herrera" | | 6 | "Then she stepped into the" |
| | ratio | 0.106 | |
| 80.61% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 66 | | matches | | 0 | "She had learned to hate" | | 1 | "She clocked him the way" | | 2 | "He glanced left." | | 3 | "He glanced right." | | 4 | "She pulled her collar up" | | 5 | "Her worn leather watch —" | | 6 | "She saw his face for" | | 7 | "she shouted, though she knew" | | 8 | "He didn't stop." | | 9 | "He was fast — paramedic-fast," | | 10 | "She kept her breathing in" | | 11 | "She was forty-one and she" | | 12 | "He took a left toward" | | 13 | "He slipped through a gap" | | 14 | "Her hand went to her" | | 15 | "She could see the ramp" | | 16 | "They had been put there" | | 17 | "She'd heard his voice on" | | 18 | "She thought about procedure." | | 19 | "She thought about Morris, who" |
| | ratio | 0.348 | |
| 73.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 66 | | matches | | 0 | "The rain had been falling" | | 1 | "The sign buzzed and flickered," | | 2 | "She had learned to hate" | | 3 | "Herrera came out at 1:14" | | 4 | "She clocked him the way" | | 5 | "Tomás Herrera, twenty-nine, olive-skinned, dark" | | 6 | "He glanced left." | | 7 | "He glanced right." | | 8 | "Quinn was out of the" | | 9 | "The rain hit her like" | | 10 | "She pulled her collar up" | | 11 | "Her worn leather watch —" | | 12 | "Herrera turned off Frith Street." | | 13 | "She saw his face for" | | 14 | "she shouted, though she knew" | | 15 | "He didn't stop." | | 16 | "He was fast — paramedic-fast," | | 17 | "She kept her breathing in" | | 18 | "She was forty-one and she" | | 19 | "He took a left toward" |
| | ratio | 0.773 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 8 | | matches | | 0 | "Tomás Herrera, twenty-nine, olive-skinned, dark curls plastered flat by the rain, no umbrella, no coat despite the weather, moving like a man who expected the s…" | | 1 | "Then he walked north, fast, shoulders hunched, one hand pressed flat against his chest as if steadying something under his shirt." | | 2 | "Then he did something that told her he'd made her: he stopped dead in the middle of the pavement, in the full glare of a kebab shop's fluorescent light, and loo…" | | 3 | "Warm brown eyes, wide with something that wasn't guilt exactly — it was closer to fear, but fear with a strange, practiced edge to it, like a man who had rehear…" | | 4 | "She was forty-one and she could still outrun most of the young ones on her team, and she proved it now, closing to thirty yards, twenty, her boots slapping stan…" | | 5 | "The tiled arch above the entrance had lost most of its lettering, but enough remained for her to read the ghost of a name in the lamplight spilling from the ram…" | | 6 | "She could see the ramp ahead, see the faint orange glow of something burning below ground at the far end of the tunnel, and see Herrera's silhouette moving down…" | | 7 | "There were more of them — dozens — hanging along the fence like a wind chime made of grave goods, and as the rain struck them they clicked together with a sound…" |
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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 | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |