| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1550 | | 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) | |
| 77.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1550 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "jaw clenched" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "flicker" | | 5 | "velvet" | | 6 | "electric" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 105 | | matches | | |
| 88.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 83 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1555 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1205 | | uniqueNames | 27 | | maxNameDensity | 0.75 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Harlow | 9 | | Quinn | 2 | | Raven | 1 | | Nest | 1 | | Herrera | 2 | | Berwick | 2 | | Street | 2 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Rain | 3 | | Shaftesbury | 1 | | Avenue | 1 | | Leicester | 2 | | Square | 2 | | Night | 1 | | Tube | 3 | | Northern | 1 | | Metropolitan | 1 | | Police | 1 | | Tomás | 4 | | English | 1 | | Dean | 1 | | Morris | 1 | | Camden | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Rain" | | 6 | "Tube" | | 7 | "Police" | | 8 | "Tomás" | | 9 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Berwick" | | 3 | "Street" | | 4 | "Shaftesbury" | | 5 | "Avenue" | | 6 | "Leicester" | | 7 | "Night" | | 8 | "Northern" | | 9 | "Dean" | | 10 | "Camden" | | 11 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 3 | | matches | | 0 | "symbols that seemed to move if she didn't look at them directly" | | 1 | "quite English" | | 2 | "smelled like wet earth and electric rails" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.643 | | wordCount | 1555 | | matches | | 0 | "not guilt, but an exhausted, practiced triage" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 29.9 | | std | 24.07 | | cv | 0.805 | | sampleLengths | | 0 | 7 | | 1 | 76 | | 2 | 4 | | 3 | 20 | | 4 | 73 | | 5 | 47 | | 6 | 7 | | 7 | 14 | | 8 | 2 | | 9 | 2 | | 10 | 59 | | 11 | 66 | | 12 | 22 | | 13 | 16 | | 14 | 19 | | 15 | 51 | | 16 | 11 | | 17 | 10 | | 18 | 74 | | 19 | 55 | | 20 | 12 | | 21 | 2 | | 22 | 36 | | 23 | 45 | | 24 | 9 | | 25 | 6 | | 26 | 46 | | 27 | 19 | | 28 | 38 | | 29 | 1 | | 30 | 26 | | 31 | 9 | | 32 | 19 | | 33 | 98 | | 34 | 18 | | 35 | 31 | | 36 | 47 | | 37 | 17 | | 38 | 36 | | 39 | 11 | | 40 | 86 | | 41 | 44 | | 42 | 21 | | 43 | 33 | | 44 | 24 | | 45 | 59 | | 46 | 33 | | 47 | 25 | | 48 | 4 | | 49 | 45 |
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| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "was supposed" | | 1 | "were supposed" | | 2 | "been painted" | | 3 | "was supposed" |
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| 75.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 215 | | matches | | 0 | "was running" | | 1 | "were starting" | | 2 | "wasn't coming" | | 3 | "was offering" |
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| 72.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 121 | | ratio | 0.025 | | matches | | 0 | "Not hard — a paramedic's shove, pushing her clear, not trying to hurt — and he was running again, south this time, toward Leicester Square, toward the Night Tube stairs." | | 1 | "The people — things — were starting to look at her." | | 2 | "The bone tokens, the impossible stalls — who would believe it?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1205 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.026556016597510373 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006639004149377593 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 12.85 | | std | 11.61 | | cv | 0.904 | | sampleLengths | | 0 | 7 | | 1 | 23 | | 2 | 24 | | 3 | 29 | | 4 | 4 | | 5 | 20 | | 6 | 2 | | 7 | 10 | | 8 | 12 | | 9 | 2 | | 10 | 16 | | 11 | 4 | | 12 | 27 | | 13 | 3 | | 14 | 33 | | 15 | 11 | | 16 | 5 | | 17 | 2 | | 18 | 4 | | 19 | 10 | | 20 | 2 | | 21 | 2 | | 22 | 14 | | 23 | 14 | | 24 | 16 | | 25 | 15 | | 26 | 14 | | 27 | 8 | | 28 | 34 | | 29 | 10 | | 30 | 4 | | 31 | 18 | | 32 | 10 | | 33 | 6 | | 34 | 3 | | 35 | 8 | | 36 | 8 | | 37 | 9 | | 38 | 42 | | 39 | 4 | | 40 | 4 | | 41 | 3 | | 42 | 10 | | 43 | 45 | | 44 | 8 | | 45 | 21 | | 46 | 17 | | 47 | 7 | | 48 | 31 | | 49 | 12 |
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| 57.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4049586776859504 | | totalSentences | 121 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 97 | | matches | | 0 | "Then the door opened." | | 1 | "Then nothing but rain crackle." | | 2 | "Only a scuff ahead, toward" |
| | ratio | 0.031 | |
| 55.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 97 | | matches | | 0 | "She'd been watching the mouth" | | 1 | "She knew him on sight" | | 2 | "He didn't look left." | | 3 | "He looked right, once, fast," | | 4 | "She waited for him to" | | 5 | "She pushed off the wall." | | 6 | "His head snapped up." | | 7 | "He was already gone, cutting" | | 8 | "She was forty-one and he" | | 9 | "She knew these streets better" | | 10 | "She cut through the service" | | 11 | "She came out low, hand" | | 12 | "He spun, almost fell." | | 13 | "she snapped, breathing hard, brown" | | 14 | "His Spanish accent thickened when" | | 15 | "Her gaze dropped to the" | | 16 | "His face went white." | | 17 | "Her sharp jaw clenched" | | 18 | "He looked past her, back" | | 19 | "He clutched the package tighter" |
| | ratio | 0.412 | |
| 57.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 97 | | matches | | 0 | "Rain slicked Soho into a" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "The street was mostly empty" | | 3 | "She'd been watching the mouth" | | 4 | "The distinctive green neon sign" | | 5 | "She knew him on sight" | | 6 | "That loose, quick gait of" | | 7 | "He didn't look left." | | 8 | "He looked right, once, fast," | | 9 | "Harlow didn't move." | | 10 | "Military precision was a phrase" | | 11 | "She waited for him to" | | 12 | "She pushed off the wall." | | 13 | "His head snapped up." | | 14 | "He was already gone, cutting" | | 15 | "Harlow swore and went after" | | 16 | "The worn leather watch on" | | 17 | "The rain was suddenly louder," | | 18 | "She was forty-one and he" | | 19 | "She knew these streets better" |
| | ratio | 0.804 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 38.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 7 | | matches | | 0 | "Detective Harlow Quinn kept her head down against it, hands jammed in the pockets of her waxed jacket, her breath fogging and vanishing." | | 1 | "She'd been watching the mouth of the alley beside The Raven's Nest for three hours, rain working its way down the back of her neck, soaking through her collar." | | 2 | "Up close she could see the rain beading on the Saint Christopher medallion around his neck, swinging wild." | | 3 | "Not hard — a paramedic's shove, pushing her clear, not trying to hurt — and he was running again, south this time, toward Leicester Square, toward the Night Tub…" | | 4 | "Stalls made from market umbrellas and tube luggage racks and overturned service carts lined what used to be the track bed, lit by jars of light that didn't flic…" | | 5 | "The look she'd seen on paramedics at three in the morning holding a kid's head together while waiting for a proper hospital that wasn't coming." | | 6 | "Harlow looked at the market, at the woman with antlers who smiled with too many teeth, at the path ahead where a man she was supposed to arrest was offering to …" |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, his voice low and fast" |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she snapped (snap)" | | 1 | "he gasped (gasp)" | | 2 | "he whispered (whisper)" |
| | dialogueSentences | 24 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0.6 | | effectiveRatio | 0.25 | |