| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said slowly [slowly]" |
| | dialogueSentences | 33 | | tagDensity | 0.515 | | leniency | 1 | | rawRatio | 0.059 | | effectiveRatio | 0.059 | |
| 75.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1024 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "carefully" | | 1 | "really" | | 2 | "slowly" |
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| 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) | |
| 56.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1024 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "glistening" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "beacon" | | 4 | "scanned" | | 5 | "stark" | | 6 | "flicked" | | 7 | "calculating" | | 8 | "weight" |
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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 | 52 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1026 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 740 | | uniqueNames | 16 | | maxNameDensity | 2.43 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Detroit | 1 | | Plcazar | 1 | | Harlow | 1 | | Quinn | 18 | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Ian | 1 | | Wilkins | 6 | | Descending | 1 | | Metropolitan | 1 | | Police | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 |
| | persons | | 0 | "Plcazar" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Ian" | | 6 | "Wilkins" | | 7 | "Police" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Herrera" |
| | places | | 0 | "Detroit" | | 1 | "Soho" | | 2 | "Metropolitan" |
| | globalScore | 0.284 | | windowScore | 0 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 4 | | matches | | 0 | "quite line up with the wall properly" | | 1 | "looked like an abandoned Tube station pla" | | 2 | "as if making sure no one watched" | | 3 | "something between a guide and a threat" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1026 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 35.38 | | std | 19.43 | | cv | 0.549 | | sampleLengths | | 0 | 85 | | 1 | 24 | | 2 | 26 | | 3 | 64 | | 4 | 71 | | 5 | 34 | | 6 | 50 | | 7 | 19 | | 8 | 33 | | 9 | 10 | | 10 | 43 | | 11 | 47 | | 12 | 6 | | 13 | 43 | | 14 | 24 | | 15 | 19 | | 16 | 14 | | 17 | 36 | | 18 | 32 | | 19 | 39 | | 20 | 38 | | 21 | 10 | | 22 | 23 | | 23 | 42 | | 24 | 28 | | 25 | 40 | | 26 | 12 | | 27 | 39 | | 28 | 75 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 52 | | matches | | 0 | "was dressed" | | 1 | "were committed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 128 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 68 | | ratio | 0.015 | | matches | | 0 | "Before anyone could answer, Quinn's eyes landed on a bookshelf in the back corner – one that didn't quite line up with the wall properly." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 741 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.03643724696356275 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.018893387314439947 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 15.09 | | std | 8.4 | | cv | 0.557 | | sampleLengths | | 0 | 21 | | 1 | 27 | | 2 | 37 | | 3 | 24 | | 4 | 19 | | 5 | 7 | | 6 | 13 | | 7 | 28 | | 8 | 23 | | 9 | 8 | | 10 | 24 | | 11 | 19 | | 12 | 20 | | 13 | 20 | | 14 | 14 | | 15 | 25 | | 16 | 11 | | 17 | 14 | | 18 | 6 | | 19 | 13 | | 20 | 21 | | 21 | 12 | | 22 | 7 | | 23 | 3 | | 24 | 28 | | 25 | 15 | | 26 | 11 | | 27 | 5 | | 28 | 31 | | 29 | 3 | | 30 | 3 | | 31 | 19 | | 32 | 24 | | 33 | 20 | | 34 | 4 | | 35 | 10 | | 36 | 9 | | 37 | 14 | | 38 | 18 | | 39 | 18 | | 40 | 11 | | 41 | 8 | | 42 | 13 | | 43 | 18 | | 44 | 21 | | 45 | 16 | | 46 | 22 | | 47 | 5 | | 48 | 5 | | 49 | 11 |
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| 87.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5588235294117647 | | totalSentences | 68 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 48 | | matches | | 0 | "Her partner, Ian Wilkins, splashed" | | 1 | "They didn't speak as they" | | 2 | "She shoved open the heavy" | | 3 | "she barked, flashing her badge" | | 4 | "Her instincts screamed as she" | | 5 | "She moved down the stairs," | | 6 | "He was dressed unremarkably, but" | | 7 | "he said, his voice soft" | | 8 | "She could turn around, let" | | 9 | "He turned and walked toward" | | 10 | "They were committed." |
| | ratio | 0.229 | |
| 53.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 48 | | matches | | 0 | "The rain hammered against the" | | 1 | "Detective Harlow Quinn cursed under" | | 2 | "Her partner, Ian Wilkins, splashed" | | 3 | "Quinn snapped, pointing down a" | | 4 | "They didn't speak as they" | | 5 | "The rain had intensified, a" | | 6 | "The green neon sign above" | | 7 | "She shoved open the heavy" | | 8 | "The air smelled of whiskey" | | 9 | "she barked, flashing her badge" | | 10 | "Her instincts screamed as she" | | 11 | "Wilkins hissed, following closely" | | 12 | "The hallway opened into a" | | 13 | "Quinn whispered, glancing at her" | | 14 | "She moved down the stairs," | | 15 | "The passage opened into a" | | 16 | "This was no basement storage." | | 17 | "A man stepped into their" | | 18 | "He was dressed unremarkably, but" | | 19 | "he said, his voice soft" |
| | ratio | 0.813 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 48 | | matches | | 0 | "Before anyone could answer, Quinn's" |
| | ratio | 0.021 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn cursed under her breath, her lungs burning as she pounded her boots against the wet cobblestones, her leather watch glistening under the …" | | 1 | "The rain had intensified, a curtain that separated them from the rest of humanity, the two sworn to uphold the law now crashing through it like avenging angels." | | 2 | "He was dressed unremarkably, but the Saint Christopher medallion around his neck caught the light, flickering in a way that shouldn't have been possible." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his voice soft yet carrying easily through the bustling market" | | 1 | "Quinn replied, her sharp jaw tightening with determination" | | 2 | "Herrera continued, his voice quieter now" | | 3 | "Quinn said slowly, her brown eyes locking onto Herrera's" | | 4 | "Quinn nodded, understanding dawning" | | 5 | "He turned, not looking back" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 9 | | fancyTags | | 0 | "Quinn snapped (snap)" | | 1 | "she barked (bark)" | | 2 | "Wilkins hissed (hiss)" | | 3 | "Quinn whispered (whisper)" | | 4 | "Wilkins breathed (breathe)" | | 5 | "Quinn continued (continue)" | | 6 | "Herrera continued (continue)" | | 7 | "Herrera explained (explain)" | | 8 | "Herrera confirmed (confirm)" |
| | dialogueSentences | 33 | | tagDensity | 0.424 | | leniency | 0.848 | | rawRatio | 0.643 | | effectiveRatio | 0.545 | |