| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1709 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "carefully" |
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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) | |
| 44.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1709 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "gloom" | | 1 | "pulse" | | 2 | "echoed" | | 3 | "shimmered" | | 4 | "scanned" | | 5 | "etched" | | 6 | "familiar" | | 7 | "flickered" | | 8 | "resonated" | | 9 | "vibrated" | | 10 | "pulsed" | | 11 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 331 | | matches | (empty) | |
| 82.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 13 | | hedgeCount | 1 | | narrationSentences | 331 | | filterMatches | | 0 | "watch" | | 1 | "hear" | | 2 | "think" | | 3 | "see" | | 4 | "know" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 333 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 15 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1709 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 16 | | matches | | 0 | "Roughhousing gone wrong, he said." | | 1 | "Look at the burn pattern, Quinn said." | | 2 | "Trauma, Davies said." | | 3 | "Deposition says he took a train ticket home, Davies said." | | 4 | "Neighbors live in the buildings above, Quinn said." | | 5 | "Pointing at something specific, Quinn said." | | 6 | "It points toward the rift, Quinn said." | | 7 | "Shade craftsmanship, Quinn said." | | 8 | "Look at the needle, Quinn said." | | 9 | "We have a token collector, Quinn said." | | 10 | "Not until I know why the compass is pointing at you, Quinn said." | | 11 | "The market is moving, Quinn said." | | 12 | "We need to leave, Davies said." | | 13 | "We need to find who left the body here, Quinn said." | | 14 | "It traces the path to the next location, Quinn said." | | 15 | "Not to the police station, she said." |
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| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 130 | | wordCount | 1700 | | uniqueNames | 11 | | maxNameDensity | 3.88 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 66 | | London | 2 | | Camden | 2 | | Davies | 37 | | Sergeant | 3 | | Underground | 1 | | North | 2 | | Detective | 2 | | Harlow | 2 | | Morris | 2 | | You | 11 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Davies" | | 3 | "Sergeant" | | 4 | "Harlow" | | 5 | "Morris" | | 6 | "You" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 130 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1709 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 333 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 125 | | mean | 13.67 | | std | 8.95 | | cv | 0.654 | | sampleLengths | | 0 | 49 | | 1 | 48 | | 2 | 27 | | 3 | 10 | | 4 | 36 | | 5 | 7 | | 6 | 22 | | 7 | 7 | | 8 | 18 | | 9 | 24 | | 10 | 25 | | 11 | 5 | | 12 | 38 | | 13 | 29 | | 14 | 10 | | 15 | 30 | | 16 | 9 | | 17 | 16 | | 18 | 15 | | 19 | 10 | | 20 | 22 | | 21 | 5 | | 22 | 7 | | 23 | 41 | | 24 | 3 | | 25 | 26 | | 26 | 7 | | 27 | 9 | | 28 | 24 | | 29 | 12 | | 30 | 22 | | 31 | 13 | | 32 | 20 | | 33 | 24 | | 34 | 8 | | 35 | 9 | | 36 | 24 | | 37 | 5 | | 38 | 7 | | 39 | 20 | | 40 | 4 | | 41 | 9 | | 42 | 26 | | 43 | 18 | | 44 | 7 | | 45 | 12 | | 46 | 27 | | 47 | 9 | | 48 | 10 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 331 | | matches | | 0 | "were dilated" | | 1 | "was delivered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 360 | | matches | | 0 | "was waiting" | | 1 | "wasn’t pointing" | | 2 | "was pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 333 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1106 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.020795660036166366 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003616636528028933 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 333 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 333 | | mean | 5.13 | | std | 2.61 | | cv | 0.508 | | sampleLengths | | 0 | 8 | | 1 | 15 | | 2 | 5 | | 3 | 7 | | 4 | 3 | | 5 | 11 | | 6 | 5 | | 7 | 10 | | 8 | 11 | | 9 | 4 | | 10 | 4 | | 11 | 3 | | 12 | 11 | | 13 | 11 | | 14 | 8 | | 15 | 8 | | 16 | 5 | | 17 | 2 | | 18 | 3 | | 19 | 2 | | 20 | 11 | | 21 | 10 | | 22 | 7 | | 23 | 2 | | 24 | 2 | | 25 | 2 | | 26 | 5 | | 27 | 2 | | 28 | 2 | | 29 | 4 | | 30 | 7 | | 31 | 5 | | 32 | 2 | | 33 | 2 | | 34 | 7 | | 35 | 7 | | 36 | 3 | | 37 | 6 | | 38 | 2 | | 39 | 2 | | 40 | 5 | | 41 | 5 | | 42 | 6 | | 43 | 6 | | 44 | 3 | | 45 | 8 | | 46 | 8 | | 47 | 6 | | 48 | 3 | | 49 | 2 |
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| 40.09% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 33 | | diversityRatio | 0.23423423423423423 | | totalSentences | 333 | | uniqueOpeners | 78 | |
| 48.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 274 | | matches | | 0 | "Just ash on the floor." | | 1 | "Just keep back." | | 2 | "Just the tip." | | 3 | "Then I’ll have to catch" |
| | ratio | 0.015 | |
| 91.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 88 | | totalSentences | 274 | | matches | | 0 | "It wasn't London." | | 1 | "She checked her watch." | | 2 | "His face looked pale in" | | 3 | "He tapped the body with" | | 4 | "Her sharp jaw tightened." | | 5 | "She ran a finger along" | | 6 | "You ever see glass like" | | 7 | "She kicked her torch aside," | | 8 | "She didn't need the light" | | 9 | "It hung in the air," | | 10 | "It hovered, pulsing slightly." | | 11 | "Her breath fogged." | | 12 | "She watched the floating drop" | | 13 | "It didn't fall." | | 14 | "It didn't drift." | | 15 | "You call this an accident," | | 16 | "He lived alone." | | 17 | "They hear the London Underground" | | 18 | "You think this was a" | | 19 | "She looked at the victim’s" |
| | ratio | 0.321 | |
| 29.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 236 | | totalSentences | 274 | | matches | | 0 | "Dust settled in the air," | | 1 | "Harlow Quinn stepped through the" | | 2 | "The smell hit her first." | | 3 | "It wasn't London." | | 4 | "Something beneath Camden shifted when" | | 5 | "Quinn switched on her torch." | | 6 | "The beam cut through the" | | 7 | "Shadows stretched long along the" | | 8 | "She checked her watch." | | 9 | "The leather strap creaked." | | 10 | "The bone token sat in" | | 11 | "Sergeant Davies stood over the" | | 12 | "His face looked pale in" | | 13 | "He tapped the body with" | | 14 | "The victim wore a suit" | | 15 | "The victim’s hair stuck to" | | 16 | "Her sharp jaw tightened." | | 17 | "She ran a finger along" | | 18 | "Ash smeared across her fingertips." | | 19 | "Davies wiped the ash on" |
| | ratio | 0.861 | |
| 72.99% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 274 | | matches | | 0 | "If it’s alive, why no" | | 1 | "If you’re right, they might" | | 2 | "To the archives." | | 3 | "If you go there you" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |