| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 1 | | adverbTags | | 0 | "The crowd scattered like [like]" |
| | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 591 | | 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) | |
| 57.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 591 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "raced" | | 1 | "glinting" | | 2 | "tracing" | | 3 | "flickered" | | 4 | "echoed" |
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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 | 51 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 51 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 51 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 586 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 1 | | matches | | 0 | "Things will be alright, she told herself." |
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| 89.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 579 | | uniqueNames | 24 | | maxNameDensity | 1.21 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 7 | | Quinn | 1 | | Dean | 1 | | Street | 3 | | Tomás | 1 | | Herrera | 3 | | Soho | 1 | | Theatre | 1 | | Registry | 1 | | Josh | 2 | | Sunday | 1 | | Hopeful | 1 | | Christopher | 2 | | Dept | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Veil | 1 | | Market | 1 | | London | 1 | | Holborn | 1 | | Piccadilly | 1 | | Line | 1 | | Tommy | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Josh" | | 5 | "Christopher" | | 6 | "Veil" | | 7 | "Tommy" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Registry" | | 4 | "Dept" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "London" |
| | globalScore | 0.896 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 586 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 51 | | matches | (empty) | |
| 61.27% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 41.86 | | std | 15.25 | | cv | 0.364 | | sampleLengths | | 0 | 50 | | 1 | 40 | | 2 | 59 | | 3 | 33 | | 4 | 47 | | 5 | 37 | | 6 | 32 | | 7 | 68 | | 8 | 25 | | 9 | 56 | | 10 | 48 | | 11 | 47 | | 12 | 40 | | 13 | 4 |
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| 98.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 51 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 90 | | matches | | |
| 30.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 51 | | ratio | 0.039 | | matches | | 0 | "Her suspect—Tomás Herrera, now pegged as a key operator for the clique—was just ahead, darting between the throngs of theatregoers pouring out from the matinee at the Soho Theatre." | | 1 | "The Veil Market—London's naughty little haberdashery—currently relocated below the abandoned Holborn tunnel of the Piccadilly Line." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 584 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.02910958904109589 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.011986301369863013 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 51 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 51 | | mean | 11.49 | | std | 7.42 | | cv | 0.646 | | sampleLengths | | 0 | 12 | | 1 | 22 | | 2 | 16 | | 3 | 29 | | 4 | 10 | | 5 | 1 | | 6 | 14 | | 7 | 4 | | 8 | 11 | | 9 | 10 | | 10 | 11 | | 11 | 9 | | 12 | 6 | | 13 | 14 | | 14 | 13 | | 15 | 15 | | 16 | 4 | | 17 | 2 | | 18 | 2 | | 19 | 19 | | 20 | 5 | | 21 | 18 | | 22 | 19 | | 23 | 8 | | 24 | 8 | | 25 | 3 | | 26 | 13 | | 27 | 19 | | 28 | 11 | | 29 | 13 | | 30 | 9 | | 31 | 16 | | 32 | 10 | | 33 | 6 | | 34 | 9 | | 35 | 6 | | 36 | 6 | | 37 | 6 | | 38 | 20 | | 39 | 11 | | 40 | 3 | | 41 | 4 | | 42 | 8 | | 43 | 7 | | 44 | 14 | | 45 | 19 | | 46 | 37 | | 47 | 10 | | 48 | 31 | | 49 | 9 |
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| 96.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.6470588235294118 | | totalSentences | 51 | | uniqueOpeners | 33 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 48 | | matches | | 0 | "Hopefully he hasn't gone south" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 48 | | matches | | 0 | "She hadn't slept in two" | | 1 | "Her suspect—Tomás Herrera, now pegged" | | 2 | "He glanced back, wide-eyed, and" | | 3 | "He saw Harlow trailing a" | | 4 | "She'd once heard that many" | | 5 | "Her partner may not approve" | | 6 | "She trotted on, re-tracing their" | | 7 | "She stared into the alleys," | | 8 | "She snapped a photo of" | | 9 | "They searched for the amulet" | | 10 | "She exhaled deeply." | | 11 | "Her hand gripped the door" | | 12 | "She crept into the tunnel" | | 13 | "She descended the staircase..." |
| | ratio | 0.292 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 48 | | matches | | 0 | "She hadn't slept in two" | | 1 | "Detective Harlow Quinn's eyes stung" | | 2 | "The wet cobblestones slipped beneath" | | 3 | "Her suspect—Tomás Herrera, now pegged" | | 4 | "He glanced back, wide-eyed, and" | | 5 | "Harlow recognized it instantly." | | 6 | "A chopper for the clique's" | | 7 | "He saw Harlow trailing a" | | 8 | "The crowd scattered like pigeons" | | 9 | "Herrera seized the opportunity, charging" | | 10 | "Josh stepped on the gas," | | 11 | "Harlow pulled off her soaked" | | 12 | "She'd once heard that many" | | 13 | "Harlow sighed, knowing she'd have" | | 14 | "Her partner may not approve" | | 15 | "That rat bastard didn't like" | | 16 | "She trotted on, re-tracing their" | | 17 | "She stared into the alleys," | | 18 | "Woodley Street sloped down into" | | 19 | "The van had to be" |
| | ratio | 0.708 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 48 | | matches | | | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Tomorrow held new deceptions, so she'd envision smooth ways to untie the ugly knots that came with the job." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |