| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 741 | | totalAiIsmAdverbs | 1 | | 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) | |
| 79.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 741 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "scanned" | | 1 | "footsteps" | | 2 | "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 | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 741 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.27% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 741 | | uniqueNames | 15 | | maxNameDensity | 1.21 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 9 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Police | 1 | | Soho | 1 | | Morris | 3 | | Saint | 1 | | Christopher | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Camden | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Market" |
| | places | | | globalScore | 0.893 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 741 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 85.04% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 43.59 | | std | 19.51 | | cv | 0.448 | | sampleLengths | | 0 | 63 | | 1 | 60 | | 2 | 5 | | 3 | 60 | | 4 | 46 | | 5 | 62 | | 6 | 12 | | 7 | 52 | | 8 | 42 | | 9 | 7 | | 10 | 59 | | 11 | 56 | | 12 | 47 | | 13 | 62 | | 14 | 49 | | 15 | 38 | | 16 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 131 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 746 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.03351206434316354 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00938337801608579 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 9.5 | | std | 5.52 | | cv | 0.581 | | sampleLengths | | 0 | 26 | | 1 | 10 | | 2 | 13 | | 3 | 14 | | 4 | 10 | | 5 | 22 | | 6 | 21 | | 7 | 2 | | 8 | 5 | | 9 | 2 | | 10 | 3 | | 11 | 10 | | 12 | 17 | | 13 | 16 | | 14 | 17 | | 15 | 10 | | 16 | 14 | | 17 | 19 | | 18 | 3 | | 19 | 8 | | 20 | 17 | | 21 | 5 | | 22 | 23 | | 23 | 9 | | 24 | 3 | | 25 | 9 | | 26 | 3 | | 27 | 18 | | 28 | 18 | | 29 | 13 | | 30 | 9 | | 31 | 4 | | 32 | 15 | | 33 | 14 | | 34 | 7 | | 35 | 13 | | 36 | 11 | | 37 | 9 | | 38 | 7 | | 39 | 10 | | 40 | 9 | | 41 | 5 | | 42 | 15 | | 43 | 3 | | 44 | 11 | | 45 | 13 | | 46 | 5 | | 47 | 2 | | 48 | 2 | | 49 | 2 |
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| 65.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4230769230769231 | | totalSentences | 78 | | uniqueOpeners | 33 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "Of course it was him." | | 1 | "Instead he cut across a" |
| | ratio | 0.027 | |
| 93.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 73 | | matches | | 0 | "She gave chase without hesitation," | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "He veered into a narrow" | | 3 | "She leaped over a split" | | 4 | "His left forearm scraped the" | | 5 | "She kept running." | | 6 | "She thought of DS Morris" | | 7 | "You know I can keep" | | 8 | "He didn't answer." | | 9 | "She saw the Saint Christopher" | | 10 | "He pushed on, leading her" | | 11 | "She crouched, peering into the" | | 12 | "Her brown eyes adjusted slowly." | | 13 | "She had heard the name" | | 14 | "Her hand brushed the radio" | | 15 | "She carried none." | | 16 | "She took another step." | | 17 | "Her mind ticked through the" | | 18 | "She placed her palm against" | | 19 | "Her sharp jaw tightened further." |
| | ratio | 0.315 | |
| 42.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 73 | | matches | | 0 | "Detective Harlow Quinn burst from" | | 1 | "The green sign buzzed overhead" | | 2 | "A figure in a dark" | | 3 | "She gave chase without hesitation," | | 4 | "Her closely cropped salt-and-pepper hair" | | 5 | "The worn leather watch on" | | 6 | "The suspect glanced back, face" | | 7 | "He veered into a narrow" | | 8 | "Quinn followed, her sharp jaw" | | 9 | "She leaped over a split" | | 10 | "Herrera scrambled over a chain-link" | | 11 | "His left forearm scraped the" | | 12 | "Quinn planted one hand and" | | 13 | "She kept running." | | 14 | "The streets widened as they" | | 15 | "Camden lay ahead, its market" | | 16 | "Quinn's breath came steady, controlled." | | 17 | "She thought of DS Morris" | | 18 | "The memory fueled her legs" | | 19 | "You know I can keep" |
| | ratio | 0.836 | |
| 68.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 73 | | matches | | 0 | "If she followed, she might" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "She gave chase without hesitation, boots slapping through puddles that soaked her socks instantly." | | 1 | "An abandoned Tube station beneath Camden that shifted location with every full moon." |
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| 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 | |