| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 30 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0.063 | | effectiveRatio | 0.063 | |
| 88.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1339 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "deliberately" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1339 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "pulsed" | | 1 | "velvet" | | 2 | "symphony" | | 3 | "scanning" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 96 | | filterMatches | (empty) | | hedgeMatches | | 0 | "appeared to" | | 1 | "tried to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1337 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1050 | | uniqueNames | 19 | | maxNameDensity | 1.43 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 15 | | Murray | 1 | | Street | 1 | | Greek | 1 | | Bellingham | 1 | | Marcus | 1 | | Webb | 10 | | London | 1 | | Confidence | 1 | | Like | 2 | | Saint | 1 | | Christopher | 1 | | Tomás | 1 | | Herrera | 4 | | Veil | 1 | | Market | 2 | | Morris | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marcus" | | 3 | "Webb" | | 4 | "Like" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Herrera" | | 9 | "Market" | | 10 | "Morris" |
| | places | | 0 | "Murray" | | 1 | "Street" | | 2 | "Bellingham" | | 3 | "London" |
| | globalScore | 0.786 | | windowScore | 0.833 | |
| 42.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 3 | | matches | | 0 | "sounded like Greek" | | 1 | "sounded like amusement" | | 2 | "felt like walking toward that same dark" |
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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 | 1337 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 31.83 | | std | 20.58 | | cv | 0.647 | | sampleLengths | | 0 | 62 | | 1 | 10 | | 2 | 48 | | 3 | 20 | | 4 | 9 | | 5 | 47 | | 6 | 16 | | 7 | 4 | | 8 | 51 | | 9 | 5 | | 10 | 76 | | 11 | 30 | | 12 | 24 | | 13 | 68 | | 14 | 67 | | 15 | 7 | | 16 | 49 | | 17 | 16 | | 18 | 15 | | 19 | 30 | | 20 | 37 | | 21 | 4 | | 22 | 39 | | 23 | 23 | | 24 | 31 | | 25 | 40 | | 26 | 54 | | 27 | 56 | | 28 | 6 | | 29 | 54 | | 30 | 52 | | 31 | 44 | | 32 | 5 | | 33 | 40 | | 34 | 12 | | 35 | 63 | | 36 | 27 | | 37 | 28 | | 38 | 6 | | 39 | 12 | | 40 | 24 | | 41 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 97.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 196 | | matches | | 0 | "were screaming" | | 1 | "was showing" | | 2 | "was crossing" |
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| 90.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 110 | | ratio | 0.018 | | matches | | 0 | "The air grew thick with competing scents—incense, copper, something sweet and rotting." | | 1 | "Quinn recognized him from the Bellingham case—Marcus Webb, mid-level fence, known associates with half the criminal element in London." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1059 | | adjectiveStacks | 1 | | stackExamples | | 0 | "same unnatural green flame." |
| | adverbCount | 25 | | adverbRatio | 0.023607176581680833 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.0084985835694051 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 12.15 | | std | 7.64 | | cv | 0.628 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 2 | | 3 | 3 | | 4 | 20 | | 5 | 6 | | 6 | 4 | | 7 | 27 | | 8 | 21 | | 9 | 9 | | 10 | 11 | | 11 | 9 | | 12 | 7 | | 13 | 16 | | 14 | 8 | | 15 | 16 | | 16 | 12 | | 17 | 4 | | 18 | 4 | | 19 | 10 | | 20 | 29 | | 21 | 12 | | 22 | 5 | | 23 | 12 | | 24 | 18 | | 25 | 29 | | 26 | 17 | | 27 | 3 | | 28 | 16 | | 29 | 5 | | 30 | 6 | | 31 | 4 | | 32 | 20 | | 33 | 15 | | 34 | 15 | | 35 | 17 | | 36 | 21 | | 37 | 20 | | 38 | 19 | | 39 | 28 | | 40 | 7 | | 41 | 11 | | 42 | 2 | | 43 | 1 | | 44 | 16 | | 45 | 19 | | 46 | 12 | | 47 | 4 | | 48 | 13 | | 49 | 2 |
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| 50.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.39090909090909093 | | totalSentences | 110 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 87 | | matches | | 0 | "She pressed herself against a" | | 1 | "She could hear voices now," | | 2 | "Her instincts were screaming." | | 3 | "She went down anyway." | | 4 | "She had no token." | | 5 | "She had her badge and" | | 6 | "She moved between the stalls," | | 7 | "He'd run when she'd tried" | | 8 | "She could hear fragments of" | | 9 | "she called out, her voice" | | 10 | "His voice carried an edge" | | 11 | "She took a step closer" | | 12 | "He said her name like" | | 13 | "it said, its voice like" | | 14 | "He gestured to a stall" | | 15 | "He turned back to Quinn" | | 16 | "She followed him, leaving Webb" |
| | ratio | 0.195 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 87 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn's boots splashed" | | 2 | "The suspect had a head" | | 3 | "Quinn didn't know what waited" | | 4 | "That was the problem." | | 5 | "She pressed herself against a" | | 6 | "A green glow emanated from" | | 7 | "Morris would have told her" | | 8 | "Morris would have called it" | | 9 | "Morris was dead, and the" | | 10 | "Quinn moved forward, her service" | | 11 | "The service door hung open" | | 12 | "The green glow pulsed below" | | 13 | "She could hear voices now," | | 14 | "Her instincts were screaming." | | 15 | "She went down anyway." | | 16 | "The stairs spiraled deeper than" | | 17 | "Quinn counted thirty-seven steps before" | | 18 | "The air grew thick with" | | 19 | "The corridor opened into chaos." |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 6 | | matches | | 0 | "A green glow emanated from somewhere beneath the street level, casting strange shadows that didn't match the geometry of the alley." | | 1 | "The service door hung open on corroded hinges, revealing a stairwell that descended into absolute darkness." | | 2 | "Quinn counted thirty-seven steps before the passage leveled out into a narrow corridor lined with exposed brick and flickering gas lamps that burned with that s…" | | 3 | "An old man with too many teeth smiled from behind a table piled high with glass vials containing liquids that glowed." | | 4 | "Morris had found something that killed him, and she'd spent three years wondering if she'd missed something, if she could have saved him." | | 5 | "The file had been incomplete, cryptic, the kind of documentation that made sense only after you'd learned the questions to ask." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "she called out, her voice cutting through the ambient noise" |
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| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "Webb interrupted (interrupt)" | | 2 | "Quinn demanded (demand)" |
| | dialogueSentences | 30 | | tagDensity | 0.233 | | leniency | 0.467 | | rawRatio | 0.429 | | effectiveRatio | 0.2 | |