| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1335 | | 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) | |
| 92.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1335 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1348 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1270 | | uniqueNames | 26 | | maxNameDensity | 0.79 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Brewer | 1 | | Street | 2 | | Raven | 1 | | Nest | 1 | | Chalk | 2 | | Farm | 2 | | Quinn | 10 | | Great | 1 | | Windmill | 1 | | Soho | 1 | | Shaftesbury | 1 | | Avenue | 1 | | Cambridge | 1 | | Circus | 1 | | Charing | 1 | | Cross | 1 | | Road | 3 | | Tube | 1 | | Leicester | 1 | | Square | 1 | | Morris | 3 | | Euston | 1 | | Kentish | 1 | | Town | 1 | | Herrera | 6 | | Tomás | 1 |
| | persons | | 0 | "Raven" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Herrera" | | 4 | "Tomás" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Chalk" | | 3 | "Farm" | | 4 | "Windmill" | | 5 | "Soho" | | 6 | "Shaftesbury" | | 7 | "Avenue" | | 8 | "Cambridge" | | 9 | "Charing" | | 10 | "Cross" | | 11 | "Road" | | 12 | "Tube" | | 13 | "Leicester" | | 14 | "Euston" | | 15 | "Kentish" | | 16 | "Town" |
| | 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 he was sorry" | | 1 | "not quite music, that she felt in her back teeth" |
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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 | 1348 | | matches | (empty) | |
| 99.33% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 99 | | matches | | 0 | "read that report" | | 1 | "making that sound" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 34.56 | | std | 28.25 | | cv | 0.817 | | sampleLengths | | 0 | 9 | | 1 | 68 | | 2 | 68 | | 3 | 3 | | 4 | 2 | | 5 | 21 | | 6 | 99 | | 7 | 63 | | 8 | 10 | | 9 | 38 | | 10 | 12 | | 11 | 7 | | 12 | 19 | | 13 | 85 | | 14 | 74 | | 15 | 77 | | 16 | 20 | | 17 | 5 | | 18 | 71 | | 19 | 1 | | 20 | 62 | | 21 | 10 | | 22 | 51 | | 23 | 25 | | 24 | 50 | | 25 | 3 | | 26 | 9 | | 27 | 25 | | 28 | 69 | | 29 | 3 | | 30 | 24 | | 31 | 15 | | 32 | 5 | | 33 | 26 | | 34 | 62 | | 35 | 9 | | 36 | 42 | | 37 | 54 | | 38 | 52 |
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| 97.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 95 | | matches | | |
| 35.80% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 203 | | matches | | 0 | "wasn't running" | | 1 | "wasn't taking" | | 2 | "was standing" | | 3 | "was holding" | | 4 | "was making" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 99 | | ratio | 0.101 | | matches | | 0 | "Quinn heard him before she saw him — the wet slap of trainers on pavement, the sound bouncing off the shuttered fronts of Brewer Street." | | 1 | "And the coat — the coat with the deep pockets, the one caught on the CCTV at the Chalk Farm loading bay at 3:14 a.m." | | 2 | "Her boots hit the puddles hard and the neon smeared green across the black water and she felt the old familiar thing come alive in her chest — not excitement exactly, something colder and more useful." | | 3 | "Twenty-nine years old, and he ran like a man who'd spent years hauling stretchers up stairwells — economical, no wasted motion, using the crown of the road where the camber shed water." | | 4 | "She saw his face under a streetlamp — warm brown eyes gone wide, water running off his jaw, and something in his expression that snagged on her." | | 5 | "She hurdled a crate of bottles, felt her knee complain — forty-one, she thought, forty-one and chasing a paramedic through Soho — and came out onto Shaftesbury Avenue into headlights and horns." | | 6 | "He wasn't taking the obvious routes — not toward the crowds on Charing Cross Road, not toward the Tube at Leicester Square where a crowd could swallow him." | | 7 | "There was a hoarding at the end of it — plywood, blue, plastered with fly posters for a club night in 2019 — and a gap in the hoarding held shut with a loop of chain that Herrera lifted off its nail without breaking stride." | | 8 | "He'd pulled something out from under his collar and was holding it in his fist — a chain, a little medallion, his knuckles white around it." | | 9 | "Then she took her hand off the hoarding, and stepped through, and went down into the dark after him — one hand on the tiled wall, her boots ringing on the stairs, the cardamom-and-butcher smell rising to meet her, and the not-music getting louder and louder until it drowned out the rain." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1266 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.030015797788309637 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005529225908372828 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 13.62 | | std | 12.94 | | cv | 0.95 | | sampleLengths | | 0 | 9 | | 1 | 25 | | 2 | 43 | | 3 | 2 | | 4 | 6 | | 5 | 25 | | 6 | 35 | | 7 | 3 | | 8 | 2 | | 9 | 21 | | 10 | 4 | | 11 | 36 | | 12 | 7 | | 13 | 4 | | 14 | 21 | | 15 | 4 | | 16 | 23 | | 17 | 3 | | 18 | 32 | | 19 | 28 | | 20 | 3 | | 21 | 7 | | 22 | 3 | | 23 | 8 | | 24 | 27 | | 25 | 2 | | 26 | 10 | | 27 | 1 | | 28 | 6 | | 29 | 19 | | 30 | 8 | | 31 | 3 | | 32 | 32 | | 33 | 17 | | 34 | 6 | | 35 | 19 | | 36 | 4 | | 37 | 19 | | 38 | 28 | | 39 | 3 | | 40 | 18 | | 41 | 2 | | 42 | 5 | | 43 | 10 | | 44 | 6 | | 45 | 56 | | 46 | 7 | | 47 | 13 | | 48 | 5 | | 49 | 30 |
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| 62.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4489795918367347 | | totalSentences | 98 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 77 | | matches | | 0 | "Just once, at the mouth" | | 1 | "Then he was gone into" | | 2 | "Somewhere behind her, three years" | | 3 | "Then the streets narrowed and" | | 4 | "Then she took her hand" |
| | ratio | 0.065 | |
| 32.99% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 77 | | matches | | 0 | "She'd been standing under the" | | 1 | "She went after him." | | 2 | "Her boots hit the puddles" | | 3 | "Her radio was in her" | | 4 | "She left it there." | | 5 | "She'd been told, twice now," | | 6 | "She'd said yes, sir." | | 7 | "She'd meant it about as" | | 8 | "He cut left down Great" | | 9 | "He glanced back." | | 10 | "She saw his face under" | | 11 | "He looked like he was" | | 12 | "She hurdled a crate of" | | 13 | "He wasn't running blind." | | 14 | "He wasn't taking the obvious" | | 15 | "He turned north." | | 16 | "He turned north like a" | | 17 | "She keyed her radio then." | | 18 | "They'd written it up as" | | 19 | "She'd read that report so" |
| | ratio | 0.468 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 77 | | matches | | 0 | "The rain came down like" | | 1 | "Quinn heard him before she" | | 2 | "She'd been standing under the" | | 3 | "She went after him." | | 4 | "Her boots hit the puddles" | | 5 | "Her radio was in her" | | 6 | "She left it there." | | 7 | "She'd been told, twice now," | | 8 | "She'd said yes, sir." | | 9 | "She'd meant it about as" | | 10 | "Herrera was fast." | | 11 | "He cut left down Great" | | 12 | "He glanced back." | | 13 | "She saw his face under" | | 14 | "He looked like he was" | | 15 | "The alley stank of bins" | | 16 | "She hurdled a crate of" | | 17 | "Herrera was already across, dodging" | | 18 | "The driver leaned on the" | | 19 | "Quinn went through the gap" |
| | ratio | 0.714 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 57.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 5 | | matches | | 0 | "on the ninth, carrying a black medical case away from a body that had bled out on the concrete and then, according to two witnesses she couldn't put on a stand,…" | | 1 | "Twenty-nine years old, and he ran like a man who'd spent years hauling stretchers up stairwells — economical, no wasted motion, using the crown of the road wher…" | | 2 | "He cut left down Great Windmill Street and she lost a half-second on the corner, her shoulder clipping a bollard, pain flaring bright and clean along her arm." | | 3 | "She saw his face under a streetlamp — warm brown eyes gone wide, water running off his jaw, and something in his expression that snagged on her." | | 4 | "Then she took her hand off the hoarding, and stepped through, and went down into the dark after him — one hand on the tiled wall, her boots ringing on the stair…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0.2 | | effectiveRatio | 0.182 | |