| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1431 | | 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) | |
| 44.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1431 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "measured" | | 1 | "shattered" | | 2 | "perfect" | | 3 | "glint" | | 4 | "etching" | | 5 | "magnetic" | | 6 | "traced" | | 7 | "aligned" | | 8 | "quivered" | | 9 | "weight" | | 10 | "pulsed" | | 11 | "rhythmic" | | 12 | "spectral" | | 13 | "standard" | | 14 | "trembled" | | 15 | "vibrated" |
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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 | 0 | | narrationSentences | 284 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 284 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 284 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 16 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1431 | | 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 | 46 | | wordCount | 1431 | | uniqueNames | 16 | | maxNameDensity | 0.77 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Tube | 1 | | Quinn | 1 | | Met | 3 | | Kowalski | 1 | | High | 2 | | Street | 1 | | Harlow | 11 | | Whitechapel | 1 | | Morris | 4 | | Internal | 1 | | Affairs | 1 | | Underground | 1 | | Eva | 7 | | You | 5 | | Look | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Kowalski" | | 3 | "Harlow" | | 4 | "Morris" | | 5 | "Eva" | | 6 | "You" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | 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 | 1431 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 284 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 28.06 | | std | 21.05 | | cv | 0.75 | | sampleLengths | | 0 | 90 | | 1 | 42 | | 2 | 6 | | 3 | 10 | | 4 | 19 | | 5 | 24 | | 6 | 48 | | 7 | 67 | | 8 | 19 | | 9 | 36 | | 10 | 63 | | 11 | 2 | | 12 | 18 | | 13 | 4 | | 14 | 9 | | 15 | 76 | | 16 | 43 | | 17 | 8 | | 18 | 12 | | 19 | 11 | | 20 | 14 | | 21 | 17 | | 22 | 21 | | 23 | 53 | | 24 | 33 | | 25 | 35 | | 26 | 19 | | 27 | 6 | | 28 | 32 | | 29 | 18 | | 30 | 6 | | 31 | 46 | | 32 | 18 | | 33 | 81 | | 34 | 43 | | 35 | 39 | | 36 | 19 | | 37 | 24 | | 38 | 5 | | 39 | 29 | | 40 | 5 | | 41 | 38 | | 42 | 33 | | 43 | 10 | | 44 | 28 | | 45 | 15 | | 46 | 32 | | 47 | 30 | | 48 | 6 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 284 | | matches | | 0 | "are sealed" | | 1 | "are welded" | | 2 | "was taken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 274 | | matches | | 0 | "was trading" | | 1 | "were waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 284 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1434 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.017433751743375175 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003486750348675035 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 284 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 284 | | mean | 5.04 | | std | 2.97 | | cv | 0.59 | | sampleLengths | | 0 | 13 | | 1 | 8 | | 2 | 8 | | 3 | 14 | | 4 | 5 | | 5 | 9 | | 6 | 10 | | 7 | 6 | | 8 | 10 | | 9 | 3 | | 10 | 4 | | 11 | 7 | | 12 | 7 | | 13 | 7 | | 14 | 12 | | 15 | 9 | | 16 | 6 | | 17 | 5 | | 18 | 5 | | 19 | 3 | | 20 | 4 | | 21 | 5 | | 22 | 2 | | 23 | 3 | | 24 | 2 | | 25 | 9 | | 26 | 5 | | 27 | 10 | | 28 | 2 | | 29 | 5 | | 30 | 8 | | 31 | 6 | | 32 | 4 | | 33 | 3 | | 34 | 7 | | 35 | 13 | | 36 | 9 | | 37 | 1 | | 38 | 1 | | 39 | 2 | | 40 | 13 | | 41 | 9 | | 42 | 4 | | 43 | 2 | | 44 | 1 | | 45 | 6 | | 46 | 7 | | 47 | 4 | | 48 | 4 | | 49 | 2 |
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| 57.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 31 | | diversityRatio | 0.4119718309859155 | | totalSentences | 284 | | uniqueOpeners | 117 | |
| 74.40% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 224 | | matches | | 0 | "Then what’s it tracking?" | | 1 | "Then they vanish." | | 2 | "Just a smell of ozone" | | 3 | "Then we have a window." | | 4 | "Then you log the angle." |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 224 | | matches | | 0 | "Her boots struck the platform" | | 1 | "She tucked a stray coil" | | 2 | "You took the long way" | | 3 | "It clattered against Harlow’s toe." | | 4 | "You don’t get past the" | | 5 | "My read is a collection" | | 6 | "She dropped it into a" | | 7 | "Her eyes tracked the scuff" | | 8 | "She knelt by the blood" | | 9 | "She lifted it free." | | 10 | "It spun lazy circles, then" | | 11 | "They’re warding lines." | | 12 | "She paced toward the brick" | | 13 | "She ran her fingers along" | | 14 | "She pressed her palm flat." | | 15 | "She checked the worn leather" | | 16 | "Your collection dispute theory requires" | | 17 | "They doubled back." | | 18 | "We swept the cupboards." | | 19 | "She traced the tread marks" |
| | ratio | 0.25 | |
| 96.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 163 | | totalSentences | 224 | | matches | | 0 | "Harlow Quinn ducked under the" | | 1 | "Her boots struck the platform" | | 2 | "This one refused to align." | | 3 | "Emergency floods washed the arched" | | 4 | "Stalls cobbled from scaffolding and" | | 5 | "Glass ampoules caught the light," | | 6 | "A hidden bazaar." | | 7 | "A figure crouched near the" | | 8 | "Eva Kowalski brushed grey ash" | | 9 | "She tucked a stray coil" | | 10 | "You took the long way" | | 11 | "Traffic on Camden High Street." | | 12 | "Blood on the platform edge." | | 13 | "Eva kicked a small white" | | 14 | "It clattered against Harlow’s toe." | | 15 | "A carved knucklebone, smoothed by" | | 16 | "The vendors call them tokens." | | 17 | "You don’t get past the" | | 18 | "My read is a collection" | | 19 | "Someone shorted a dealer." |
| | ratio | 0.728 | |
| 66.96% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 224 | | matches | | 0 | "Now a cordoned wreck." | | 1 | "If the needle holds, the" | | 2 | "If the fracture widens, you" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | 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 | |