| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.189 | | leniency | 0.378 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1374 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 41.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1374 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "weight" | | 1 | "etched" | | 2 | "silk" | | 3 | "familiar" | | 4 | "scanned" | | 5 | "oppressive" | | 6 | "chill" | | 7 | "silence" | | 8 | "remarkable" | | 9 | "sense of" | | 10 | "intricate" | | 11 | "magnetic" | | 12 | "rhythmic" | | 13 | "vibrated" | | 14 | "echoing" |
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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 | 1 | | narrationSentences | 91 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | 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 | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1374 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1044 | | uniqueNames | 18 | | maxNameDensity | 1.72 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Met | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Miller | 8 | | Tube | 1 | | West | 1 | | End | 1 | | Harlow | 18 | | Savile | 1 | | Row | 1 | | Flash-frozen | 1 | | Rotherhithe | 1 | | Morris | 1 | | London | 1 | | Kowalski | 1 | | Eva | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Miller" | | 2 | "Harlow" | | 3 | "Morris" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "West" | | 4 | "End" | | 5 | "Savile" | | 6 | "Rotherhithe" | | 7 | "London" |
| | globalScore | 0.638 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | 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 | 1374 | | matches | (empty) | |
| 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 | 59 | | mean | 23.29 | | std | 20.22 | | cv | 0.868 | | sampleLengths | | 0 | 20 | | 1 | 69 | | 2 | 5 | | 3 | 10 | | 4 | 9 | | 5 | 6 | | 6 | 71 | | 7 | 53 | | 8 | 4 | | 9 | 4 | | 10 | 16 | | 11 | 3 | | 12 | 5 | | 13 | 41 | | 14 | 3 | | 15 | 50 | | 16 | 19 | | 17 | 13 | | 18 | 5 | | 19 | 10 | | 20 | 4 | | 21 | 48 | | 22 | 54 | | 23 | 15 | | 24 | 15 | | 25 | 83 | | 26 | 49 | | 27 | 6 | | 28 | 41 | | 29 | 5 | | 30 | 8 | | 31 | 59 | | 32 | 50 | | 33 | 19 | | 34 | 28 | | 35 | 2 | | 36 | 1 | | 37 | 21 | | 38 | 19 | | 39 | 10 | | 40 | 17 | | 41 | 24 | | 42 | 8 | | 43 | 3 | | 44 | 24 | | 45 | 7 | | 46 | 37 | | 47 | 54 | | 48 | 30 | | 49 | 14 |
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| 97.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 91 | | matches | | 0 | "been swept" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 168 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 121 | | ratio | 0.008 | | matches | | 0 | "It hadn't been swept; the broom marks were absent." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1050 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over grime-caked tiles." |
| | adverbCount | 25 | | adverbRatio | 0.023809523809523808 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005714285714285714 | |
| 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 | 11.36 | | std | 7.1 | | cv | 0.625 | | sampleLengths | | 0 | 20 | | 1 | 13 | | 2 | 27 | | 3 | 14 | | 4 | 15 | | 5 | 5 | | 6 | 10 | | 7 | 9 | | 8 | 6 | | 9 | 14 | | 10 | 13 | | 11 | 18 | | 12 | 17 | | 13 | 2 | | 14 | 7 | | 15 | 9 | | 16 | 7 | | 17 | 19 | | 18 | 2 | | 19 | 2 | | 20 | 14 | | 21 | 4 | | 22 | 4 | | 23 | 16 | | 24 | 3 | | 25 | 5 | | 26 | 7 | | 27 | 8 | | 28 | 26 | | 29 | 1 | | 30 | 2 | | 31 | 12 | | 32 | 9 | | 33 | 11 | | 34 | 18 | | 35 | 6 | | 36 | 13 | | 37 | 1 | | 38 | 7 | | 39 | 5 | | 40 | 5 | | 41 | 10 | | 42 | 4 | | 43 | 15 | | 44 | 13 | | 45 | 8 | | 46 | 1 | | 47 | 11 | | 48 | 11 | | 49 | 14 |
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| 76.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.48760330578512395 | | totalSentences | 121 | | uniqueOpeners | 59 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 78 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "They did not freeze solid" | | 2 | "He shone his torch along" | | 3 | "She walked the perimeter of" | | 4 | "She swept her torchlight across" | | 5 | "It hadn't been swept; the" | | 6 | "She dropped to one knee" | | 7 | "She brought her fingers to" | | 8 | "Her chest tightened." | | 9 | "She walked back toward the" | | 10 | "She tucked the same lock" | | 11 | "She stood five inches taller" | | 12 | "Her hand rose to her" | | 13 | "She reached into her coat" | | 14 | "She held it up between" | | 15 | "Her pupils dilated behind her" | | 16 | "She hadn't logged this second" | | 17 | "She had plucked it from" | | 18 | "She laid the small brass" | | 19 | "It didn't even point toward" |
| | ratio | 0.256 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 78 | | matches | | 0 | "The corpse’s fingers gripped the" | | 1 | "Harlow Quinn crouched beside the" | | 2 | "She adjusted the worn leather" | | 3 | "They did not freeze solid" | | 4 | "DS Miller said" | | 5 | "Miller shifted his weight, his" | | 6 | "He shone his torch along" | | 7 | "The beam caught peeling posters" | | 8 | "A sweet, metallic stench hung" | | 9 | "Ozone, mixed with burnt sage" | | 10 | "Harlow stood, her posture snapping" | | 11 | "She walked the perimeter of" | | 12 | "The victim was male, thirties," | | 13 | "Harlow didn't dignify that with" | | 14 | "She swept her torchlight across" | | 15 | "The dust here was thick," | | 16 | "It hadn't been swept; the" | | 17 | "The stone flags simply gleamed," | | 18 | "The clean zone formed a" | | 19 | "Harlow stepped into the clean" |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 3 | | matches | | 0 | "The victim was male, thirties, dressed in a bespoke charcoal wool suit that cost more than her monthly rent." | | 1 | "Five minutes later, DS Morris was dead, his heart stopped without a mark on him, his eyes wide and milky as if he had stared into a welding arc." | | 2 | "The casing had a patina of verdigris, green and crusty with age, and its face was etched with intricate, jagged sigils that made Harlow's eyes ache if she stare…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "Miller flipped, the pages fluttering" | | 1 | "Eva said, her voice dropping an octave" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.162 | | leniency | 0.324 | | rawRatio | 0 | | effectiveRatio | 0 | |