| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 71.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 695 | | totalAiIsmAdverbs | 4 | | 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) | |
| 64.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 695 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "racing" | | 1 | "scanning" | | 2 | "silence" | | 3 | "footsteps" |
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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 | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 46 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 52 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 700 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 574 | | uniqueNames | 10 | | maxNameDensity | 1.74 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 5 | | Quinn | 10 | | London | 1 | | Vauxhall | 1 | | Astra | 2 | | Met | 1 | | Raven | 1 | | Nest | 1 | | Trying | 1 | | Railway | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Met" | | 3 | "Raven" | | 4 | "Nest" |
| | places | | | globalScore | 0.629 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 700 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 52 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 25.93 | | std | 13.7 | | cv | 0.528 | | sampleLengths | | 0 | 42 | | 1 | 52 | | 2 | 14 | | 3 | 38 | | 4 | 27 | | 5 | 36 | | 6 | 26 | | 7 | 52 | | 8 | 35 | | 9 | 40 | | 10 | 20 | | 11 | 25 | | 12 | 35 | | 13 | 33 | | 14 | 20 | | 15 | 26 | | 16 | 5 | | 17 | 12 | | 18 | 36 | | 19 | 1 | | 20 | 9 | | 21 | 30 | | 22 | 4 | | 23 | 21 | | 24 | 22 | | 25 | 32 | | 26 | 7 |
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| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 46 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 101 | | matches | (empty) | |
| 32.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 52 | | ratio | 0.038 | | matches | | 0 | "Trembling, she looked down to see the roof of an underground platform several stories below - and the distinctive green neon sign of a Raven's Nest pointing up at her from the empty station beneath Trying to still her racing heart, Harlow crept forward and grabbed the iron railing, letting herself back onto solid ground." | | 1 | "But time's a funnel - and it was sink or swim." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 574 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.02613240418118467 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.017421602787456445 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 52 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 52 | | mean | 13.46 | | std | 10.33 | | cv | 0.767 | | sampleLengths | | 0 | 21 | | 1 | 21 | | 2 | 14 | | 3 | 19 | | 4 | 11 | | 5 | 8 | | 6 | 14 | | 7 | 4 | | 8 | 34 | | 9 | 15 | | 10 | 2 | | 11 | 10 | | 12 | 16 | | 13 | 20 | | 14 | 6 | | 15 | 14 | | 16 | 6 | | 17 | 24 | | 18 | 10 | | 19 | 18 | | 20 | 55 | | 21 | 3 | | 22 | 6 | | 23 | 11 | | 24 | 20 | | 25 | 25 | | 26 | 12 | | 27 | 23 | | 28 | 22 | | 29 | 11 | | 30 | 14 | | 31 | 6 | | 32 | 22 | | 33 | 4 | | 34 | 5 | | 35 | 12 | | 36 | 36 | | 37 | 1 | | 38 | 3 | | 39 | 6 | | 40 | 4 | | 41 | 26 | | 42 | 2 | | 43 | 2 | | 44 | 16 | | 45 | 5 | | 46 | 10 | | 47 | 2 | | 48 | 10 | | 49 | 5 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6538461538461539 | | totalSentences | 52 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 40 | | matches | | 0 | "Suddenly, the van made a" | | 1 | "Abruptly, the alleyway disappeared and" | | 2 | "Suddenly a dim light shone" |
| | ratio | 0.075 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 40 | | matches | | 0 | "She'd finally gotten a lead" | | 1 | "They'd been spotted by an" | | 2 | "She teetered on the ledge" | | 3 | "Her heart leapt into her" | | 4 | "She started forward, her eyes" | | 5 | "She found herself in a" | | 6 | "She crept closer -" | | 7 | "Her breathing was the only" |
| | ratio | 0.2 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 26 | | totalSentences | 40 | | matches | | 0 | "Detective Harlow Quinn gripped the" | | 1 | "The wipers struggled against the" | | 2 | "She'd finally gotten a lead" | | 3 | "They'd been spotted by an" | | 4 | "A golden opportunity in an" | | 5 | "Quinn barked into her radio" | | 6 | "A burst of static." | | 7 | "Quinn cursed under her breath" | | 8 | "Quinn screeched to a halt," | | 9 | "The alley was dark and" | | 10 | "Puddles splashed up as Harlow" | | 11 | "She teetered on the ledge" | | 12 | "Her heart leapt into her" | | 13 | "This was illogical." | | 14 | "Soho was over 5km from" | | 15 | "Harlow spied a nearby ladder" | | 16 | "She started forward, her eyes" | | 17 | "She found herself in a" | | 18 | "She crept closer -" | | 19 | "Quinn listened to the conversation," |
| | ratio | 0.65 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 40 | | matches | | 0 | "Now the detective had a" | | 1 | "Just as Quinn was about" |
| | ratio | 0.05 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Trembling, she looked down to see the roof of an underground platform several stories below - and the distinctive green neon sign of a Raven's Nest pointing up …" |
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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 | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "Quinn called out (call out)" |
| | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 1 | | effectiveRatio | 0.444 | |