| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 610 | | 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) | |
| 59.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 610 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "shattered" | | 1 | "echoed" | | 2 | "gloom" | | 3 | "flicked" | | 4 | "glinting" |
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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 | 1 | | narrationSentences | 79 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 6 | | totalWords | 598 | | ratio | 0.01 | | matches | | 0 | "Abandon hope, all who enter here." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 529 | | uniqueNames | 13 | | maxNameDensity | 1.7 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 9 | | Soho | 1 | | Thames | 1 | | Tube | 1 | | Camden | 1 | | Town | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Thames" | | 2 | "Camden" | | 3 | "Town" |
| | globalScore | 0.649 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | 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 | 598 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 16.16 | | std | 14.28 | | cv | 0.884 | | sampleLengths | | 0 | 43 | | 1 | 3 | | 2 | 40 | | 3 | 9 | | 4 | 32 | | 5 | 21 | | 6 | 8 | | 7 | 5 | | 8 | 33 | | 9 | 9 | | 10 | 5 | | 11 | 30 | | 12 | 3 | | 13 | 33 | | 14 | 32 | | 15 | 3 | | 16 | 22 | | 17 | 55 | | 18 | 3 | | 19 | 18 | | 20 | 5 | | 21 | 38 | | 22 | 15 | | 23 | 4 | | 24 | 11 | | 25 | 8 | | 26 | 9 | | 27 | 2 | | 28 | 32 | | 29 | 21 | | 30 | 4 | | 31 | 21 | | 32 | 6 | | 33 | 10 | | 34 | 1 | | 35 | 1 | | 36 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 91 | | ratio | 0.099 | | matches | | 0 | "The neon glow of Soho blurred past—pink, blue, green—reflected in puddles like shattered stained glass." | | 1 | "The figure ahead—hood up, head down—didn’t break stride." | | 2 | "The drain led underground—abandoned tunnels, maybe." | | 3 | "No—oil." | | 4 | "Movement there—a figure slipping around a corner." | | 5 | "Then—light." | | 6 | "The arch opened into a cavernous space—an abandoned Tube station, its tiled walls cracked but still bearing faded signs for Camden Town." | | 7 | "Morris had been investigating it before he—" | | 8 | "A vendor lowered his hood—too sharp teeth glinting in a smile." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 541 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.022181146025878003 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0036968576709796672 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 6.57 | | std | 4.8 | | cv | 0.73 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 15 | | 3 | 3 | | 4 | 8 | | 5 | 15 | | 6 | 17 | | 7 | 4 | | 8 | 5 | | 9 | 6 | | 10 | 8 | | 11 | 11 | | 12 | 2 | | 13 | 5 | | 14 | 6 | | 15 | 2 | | 16 | 2 | | 17 | 11 | | 18 | 2 | | 19 | 6 | | 20 | 3 | | 21 | 2 | | 22 | 2 | | 23 | 6 | | 24 | 2 | | 25 | 11 | | 26 | 12 | | 27 | 9 | | 28 | 5 | | 29 | 5 | | 30 | 14 | | 31 | 1 | | 32 | 1 | | 33 | 1 | | 34 | 8 | | 35 | 3 | | 36 | 4 | | 37 | 12 | | 38 | 10 | | 39 | 7 | | 40 | 4 | | 41 | 13 | | 42 | 1 | | 43 | 1 | | 44 | 13 | | 45 | 3 | | 46 | 7 | | 47 | 1 | | 48 | 1 | | 49 | 4 |
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| 75.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4725274725274725 | | totalSentences | 91 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 61 | | matches | | 0 | "Just a rusted fire escape" | | 1 | "Only the drip of water" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 61 | | matches | | 0 | "They wove through a group" | | 1 | "She thumbed her radio." | | 2 | "She slowed at a dead" | | 3 | "They’d found his badge in" | | 4 | "Her fingers brushed the worn" | | 5 | "She yanked the grate open." | | 6 | "Her boots hit concrete." | | 7 | "She unholstered her torch." | | 8 | "She’d heard rumours." | | 9 | "His olive skin gleamed under" | | 10 | "His grip tightened" | | 11 | "She jerked free." | | 12 | "He tapped the Saint Christopher" | | 13 | "His eyes flicked past her" |
| | ratio | 0.23 | |
| 9.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 61 | | matches | | 0 | "The suspect’s shoulder clipped a" | | 1 | "Harlow Quinn vaulted over the" | | 2 | "The neon glow of Soho" | | 3 | "The figure ahead—hood up, head" | | 4 | "They wove through a group" | | 5 | "The air smelled of fried" | | 6 | "She thumbed her radio." | | 7 | "A sharp left into an" | | 8 | "Quinn followed, her breath burning" | | 9 | "The alley narrowed, walls closing" | | 10 | "A door slammed somewhere ahead." | | 11 | "She slowed at a dead" | | 12 | "The radio crackled." | | 13 | "The drain led underground—abandoned tunnels," | | 14 | "Morris had vanished down a" | | 15 | "They’d found his badge in" | | 16 | "Her fingers brushed the worn" | | 17 | "She yanked the grate open." | | 18 | "The ladder descended into blackness." | | 19 | "The air rising from below" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "The suspect’s shoulder clipped a street vendor’s stall, sending oranges tumbling across the slick pavement." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás Herrera said, his Spanish accent thickening with urgency" |
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| 32.35% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Tomás muttered (mutter)" |
| | dialogueSentences | 17 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0.667 | | effectiveRatio | 0.235 | |