| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1139 | | 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) | |
| 60.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1139 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "looming" | | 1 | "flicker" | | 2 | "scanned" | | 3 | "eyebrow" | | 4 | "gloom" | | 5 | "potential" | | 6 | "pounding" | | 7 | "charged" | | 8 | "familiar" |
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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 | 1 | | narrationSentences | 118 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1135 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1134 | | uniqueNames | 18 | | maxNameDensity | 0.88 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 10 | | Morris | 4 | | Herrera | 6 | | Raven | 1 | | Nest | 1 | | Camden | 1 | | West | 1 | | End | 1 | | Christopher | 1 | | Tomás | 1 | | Underground | 1 | | Tube | 2 | | London | 2 | | English | 1 | | Veil | 2 | | Market | 2 | | Met | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Christopher" | | 5 | "Tomás" | | 6 | "Market" | | 7 | "Met" |
| | places | | 0 | "Soho" | | 1 | "West" | | 2 | "End" | | 3 | "London" | | 4 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 56.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 3 | | matches | | 0 | "quite see" | | 1 | "quite English, or any other single tongue she recognised" | | 2 | "looked like bottled lightning in mason ja" |
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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 | 1135 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 59.74 | | std | 30.39 | | cv | 0.509 | | sampleLengths | | 0 | 111 | | 1 | 96 | | 2 | 74 | | 3 | 3 | | 4 | 66 | | 5 | 61 | | 6 | 69 | | 7 | 71 | | 8 | 18 | | 9 | 104 | | 10 | 34 | | 11 | 86 | | 12 | 54 | | 13 | 1 | | 14 | 51 | | 15 | 32 | | 16 | 59 | | 17 | 83 | | 18 | 62 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 172 | | matches | | 0 | "was heading" | | 1 | "was selling" |
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| 46.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 119 | | ratio | 0.034 | | matches | | 0 | "Ahead, the suspect—a wiry figure in a dark hoodie—darted through the throng of late-night revellers, shoving past umbrellas and brollies." | | 1 | "People—some looking perfectly normal, others…" | | 2 | "less so—moved between the stalls." | | 3 | "He wasn’t a hardened criminal; he was a scared man who knew things." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1146 | | adjectiveStacks | 1 | | stackExamples | | 0 | "single, distant green spark," |
| | adverbCount | 28 | | adverbRatio | 0.02443280977312391 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006108202443280977 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 9.54 | | std | 6.1 | | cv | 0.639 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 20 | | 3 | 10 | | 4 | 13 | | 5 | 5 | | 6 | 4 | | 7 | 22 | | 8 | 8 | | 9 | 18 | | 10 | 5 | | 11 | 20 | | 12 | 12 | | 13 | 3 | | 14 | 1 | | 15 | 10 | | 16 | 8 | | 17 | 19 | | 18 | 8 | | 19 | 11 | | 20 | 2 | | 21 | 5 | | 22 | 2 | | 23 | 12 | | 24 | 7 | | 25 | 9 | | 26 | 12 | | 27 | 6 | | 28 | 3 | | 29 | 12 | | 30 | 6 | | 31 | 14 | | 32 | 12 | | 33 | 7 | | 34 | 4 | | 35 | 11 | | 36 | 2 | | 37 | 5 | | 38 | 3 | | 39 | 7 | | 40 | 12 | | 41 | 7 | | 42 | 3 | | 43 | 2 | | 44 | 4 | | 45 | 16 | | 46 | 10 | | 47 | 4 | | 48 | 11 | | 49 | 13 |
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| 49.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3445378151260504 | | totalSentences | 119 | | uniqueOpeners | 41 | |
| 90.91% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 110 | | matches | | 0 | "Then she caught the flash" | | 1 | "Just the hiss of the" | | 2 | "Then she stepped into the" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 110 | | matches | | 0 | "He’d bolted the second she’d" | | 1 | "He cut left down an" | | 2 | "He vaulted a rusted fence" | | 3 | "He was heading for the" | | 4 | "She lost sight of him" | | 5 | "He was fast, but she" | | 6 | "He rounded a corner near" | | 7 | "He was gone." | | 8 | "Her breath plumed in the" | | 9 | "She scanned the shadows." | | 10 | "Her eyes dropped to the" | | 11 | "She knew the maps of" | | 12 | "She knelt, ignoring the cold" | | 13 | "It was a smell that" | | 14 | "It was the smell from" | | 15 | "Her training screamed about tactical" | | 16 | "He’d followed it somewhere." | | 17 | "She pulled her flashlight, its" | | 18 | "She went down." | | 19 | "She pulled her warrant card" |
| | ratio | 0.3 | |
| 64.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 110 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn’s lungs burned, each" | | 2 | "Quinn’s worn leather watch strap" | | 3 | "This one was different, though." | | 4 | "This wasn’t some pickpocket." | | 5 | "The files on her desk," | | 6 | "He’d bolted the second she’d" | | 7 | "A mistake on his part." | | 8 | "He cut left down an" | | 9 | "A dead end?" | | 10 | "He vaulted a rusted fence" | | 11 | "Quinn followed, the metal biting" | | 12 | "He was heading for the" | | 13 | "She lost sight of him" | | 14 | "The file said Tomás Herrera." | | 15 | "He was fast, but she" | | 16 | "The rain drummed a frantic" | | 17 | "He rounded a corner near" | | 18 | "Quinn rounded it a second" | | 19 | "He was gone." |
| | ratio | 0.791 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 110 | | matches | | 0 | "Now she was just another" | | 1 | "Now a ghost who patched" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 3 | | matches | | 0 | "The air buzzed with a low murmur of voices, a language that wasn’t quite English, or any other single tongue she recognised." | | 1 | "Quinn stepped off the ladder, her boots crunching on something that might have been gravel or might have been old teeth." | | 2 | "He shoved the stallkeeper’s cart, sending vials shattering on the platform in a burst of coloured light and acrid smoke." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |