| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Nasser flipped back [back]" |
| | dialogueSentences | 27 | | tagDensity | 0.519 | | leniency | 1 | | rawRatio | 0.071 | | effectiveRatio | 0.071 | |
| 86.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1134 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "very" | | 2 | "slightly" |
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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) | |
| 91.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1134 | | 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 | 41 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 41 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 53 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 79 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1139 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 799 | | uniqueNames | 11 | | maxNameDensity | 1.38 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Town | 1 | | Harlow | 1 | | Quinn | 11 | | Priya | 1 | | Nasser | 7 | | Terrence | 1 | | Aldwych | 3 | | Whitechapel | 1 | | July | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nasser" | | 3 | "Terrence" | | 4 | "Aldwych" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Whitechapel" | | 3 | "July" |
| | globalScore | 0.812 | | windowScore | 1 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | glossingSentenceCount | 1 | | matches | | 0 | "something like it before" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.878 | | wordCount | 1139 | | matches | | 0 | "not closed but wide, fixed on something above him" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 49.52 | | std | 34.65 | | cv | 0.7 | | sampleLengths | | 0 | 61 | | 1 | 28 | | 2 | 12 | | 3 | 39 | | 4 | 32 | | 5 | 48 | | 6 | 54 | | 7 | 53 | | 8 | 5 | | 9 | 46 | | 10 | 103 | | 11 | 11 | | 12 | 125 | | 13 | 23 | | 14 | 41 | | 15 | 79 | | 16 | 21 | | 17 | 97 | | 18 | 5 | | 19 | 96 | | 20 | 93 | | 21 | 3 | | 22 | 64 |
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| 79.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 41 | | matches | | 0 | "been closed" | | 1 | "been shown" | | 2 | "been closed" |
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| 43.75% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 128 | | matches | | 0 | "weren't looking" | | 1 | "wasn't laughing" | | 2 | "was already moving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 53 | | ratio | 0.094 | | matches | | 0 | "She let her torch travel the length of the body first — the waxy pallor of the skin, the lips gone the color of a bruise, hands curled inward like a man who'd died reaching for something and never gotten there." | | 1 | "There, faint but unmistakable in the residual grime — a scorch mark." | | 2 | "Eighteen years on the force and she'd developed a private catalog of things she didn't have names for yet — marks on walls in the Whitechapel warehouse fire that never should have spread the way it had, the frost that formed inside a locked car in July three summers back, the case that had taken Morris from her and left nothing behind but questions the coroner's report couldn't answer and a hollow place in her chest that never quite closed." | | 3 | "\"Or there was no screaming for them to hear.\" Quinn walked the platform's edge, torch trained downward, until she found what she'd been looking for — a second mark, smaller, half-hidden beneath a fallen ceiling tile." | | 4 | "She thought of a compass she'd been shown once, off the books, by a source who wanted money for information he swore was true — a small brass thing gone green with age, its needle spinning wild in places where the world was supposedly thinner than it should be." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 796 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.03391959798994975 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007537688442211055 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 53 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 53 | | mean | 21.49 | | std | 18.18 | | cv | 0.846 | | sampleLengths | | 0 | 38 | | 1 | 23 | | 2 | 7 | | 3 | 21 | | 4 | 10 | | 5 | 2 | | 6 | 39 | | 7 | 20 | | 8 | 12 | | 9 | 24 | | 10 | 24 | | 11 | 5 | | 12 | 41 | | 13 | 8 | | 14 | 4 | | 15 | 24 | | 16 | 25 | | 17 | 5 | | 18 | 42 | | 19 | 4 | | 20 | 48 | | 21 | 12 | | 22 | 9 | | 23 | 34 | | 24 | 6 | | 25 | 5 | | 26 | 39 | | 27 | 6 | | 28 | 80 | | 29 | 20 | | 30 | 3 | | 31 | 27 | | 32 | 14 | | 33 | 36 | | 34 | 4 | | 35 | 1 | | 36 | 1 | | 37 | 37 | | 38 | 21 | | 39 | 52 | | 40 | 45 | | 41 | 5 | | 42 | 39 | | 43 | 49 | | 44 | 4 | | 45 | 4 | | 46 | 8 | | 47 | 46 | | 48 | 39 | | 49 | 2 |
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| 92.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.7547169811320755 | | totalSentences | 53 | | uniqueOpeners | 40 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 35 | | matches | | 0 | "Then she looked at the" | | 1 | "Just Terrence Aldwych, and a" |
| | ratio | 0.057 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 35 | | matches | | 0 | "She smelled it before she" | | 1 | "She let her torch travel" | | 2 | "She'd seen something like it" | | 3 | "She stood and turned in" | | 4 | "She thought of a compass" | | 5 | "She'd laughed at him." | | 6 | "She wasn't laughing now." | | 7 | "She snapped her notebook shut" |
| | ratio | 0.229 | |
| 74.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 27 | | totalSentences | 35 | | matches | | 0 | "The Camden Town station had" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "She smelled it before she" | | 3 | "Copper and something else, something" | | 4 | "DC Priya Nasser stood at" | | 5 | "Quinn said, coming down slowly," | | 6 | "Nasser crouched and drew back" | | 7 | "Quinn didn't answer right away." | | 8 | "She let her torch travel" | | 9 | "Nasser shrugged, the gesture too" | | 10 | "Quinn crouched now too, careful" | | 11 | "There, faint but unmistakable in" | | 12 | "Something wider, almost geometric, curling" | | 13 | "Quinn reached into her coat" | | 14 | "She'd seen something like it" | | 15 | "She stood and turned in" | | 16 | "Nasser flipped back through her" | | 17 | "Quinn walked the platform's edge," | | 18 | "A symbol she didn't recognize," | | 19 | "Nasser had come up beside" |
| | ratio | 0.771 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 5 | | matches | | 0 | "DC Priya Nasser stood at the bottom of the stairwell in a paper suit, her own torch angled down at the platform floor, where a shape lay under a foil blanket th…" | | 1 | "Eighteen years on the force and she'd developed a private catalog of things she didn't have names for yet — marks on walls in the Whitechapel warehouse fire tha…" | | 2 | "A symbol she didn't recognize, angular and old-looking despite the freshness of the cuts, as though someone had taken a blade to the concrete with the same casu…" | | 3 | "Quinn looked at Aldwych's face one more time, the fear still stamped into it even in death, the eyes not closed but wide, fixed on something above him that wasn…" | | 4 | "She thought of a compass she'd been shown once, off the books, by a source who wanted money for information he swore was true — a small brass thing gone green w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn photographed (photograph)" | | 1 | "She snapped (snap)" |
| | dialogueSentences | 27 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.333 | | effectiveRatio | 0.148 | |