| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 82.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1174 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "softly" | | 2 | "suddenly" | | 3 | "really" |
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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) | |
| 70.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1174 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "chill" | | 1 | "methodical" | | 2 | "gloom" | | 3 | "pristine" | | 4 | "processed" | | 5 | "raced" | | 6 | "shattered" |
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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 | 104 | | matches | (empty) | |
| 46.70% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1172 | | ratio | 0 | | matches | (empty) | |
| 13.89% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 4 | | matches | | 0 | "Rough night for a midnight stroll, Quinn said, her voice a low, even rumble." | | 1 | "Open and shut, Vance muttered, gesturing broadly toward the tracks." | | 2 | "You mentioned a struggle, Quinn said softly." | | 3 | "Get the crime scene techs back here with the ultraviolet lamps, Quinn ordered, her tone leaving no room for argument." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1172 | | uniqueNames | 13 | | maxNameDensity | 1.37 | | worstName | "Vance" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Vance" | | discoveredNames | | Harlow | 2 | | Quinn | 15 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Metropolitan | 2 | | Police | 2 | | Sergeant | 1 | | Vance | 16 | | Higgins | 4 | | Morris | 2 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Police" | | 4 | "Sergeant" | | 5 | "Vance" | | 6 | "Higgins" | | 7 | "Morris" | | 8 | "You" |
| | places | | | globalScore | 0.817 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 1 | | matches | | 0 | "as if reciting scripture" |
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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 | 1172 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 34.47 | | std | 22.22 | | cv | 0.645 | | sampleLengths | | 0 | 67 | | 1 | 62 | | 2 | 16 | | 3 | 61 | | 4 | 40 | | 5 | 19 | | 6 | 57 | | 7 | 23 | | 8 | 6 | | 9 | 61 | | 10 | 25 | | 11 | 7 | | 12 | 9 | | 13 | 6 | | 14 | 13 | | 15 | 55 | | 16 | 27 | | 17 | 28 | | 18 | 4 | | 19 | 7 | | 20 | 73 | | 21 | 17 | | 22 | 31 | | 23 | 69 | | 24 | 51 | | 25 | 19 | | 26 | 63 | | 27 | 19 | | 28 | 35 | | 29 | 73 | | 30 | 17 | | 31 | 42 | | 32 | 27 | | 33 | 43 |
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| 81.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 104 | | matches | | 0 | "were gone" | | 1 | "was crouched" | | 2 | "been dragged" | | 3 | "was stabbed" | | 4 | "was placed" | | 5 | "is tied" | | 6 | "was delivered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 185 | | matches | | 0 | "was stammering" | | 1 | "was already shaking" |
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| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "Something caught the pale beam of her flashlight—a faint, iridescent smear, glowing with a dull greenish sheen that didn't match anything in the Metropolitan Police forensic kit." | | 1 | "The casing of her own watch caught the pale light—a worn leather watch on her left wrist that she had worn every day since Morris died." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1178 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.028862478777589132 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.01697792869269949 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 11.27 | | std | 7.27 | | cv | 0.645 | | sampleLengths | | 0 | 22 | | 1 | 10 | | 2 | 4 | | 3 | 31 | | 4 | 15 | | 5 | 28 | | 6 | 19 | | 7 | 16 | | 8 | 22 | | 9 | 11 | | 10 | 19 | | 11 | 9 | | 12 | 27 | | 13 | 13 | | 14 | 14 | | 15 | 5 | | 16 | 10 | | 17 | 3 | | 18 | 9 | | 19 | 8 | | 20 | 19 | | 21 | 8 | | 22 | 5 | | 23 | 18 | | 24 | 6 | | 25 | 13 | | 26 | 6 | | 27 | 4 | | 28 | 9 | | 29 | 12 | | 30 | 3 | | 31 | 5 | | 32 | 9 | | 33 | 13 | | 34 | 12 | | 35 | 7 | | 36 | 2 | | 37 | 2 | | 38 | 2 | | 39 | 3 | | 40 | 6 | | 41 | 10 | | 42 | 3 | | 43 | 9 | | 44 | 1 | | 45 | 9 | | 46 | 20 | | 47 | 16 | | 48 | 8 | | 49 | 6 |
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| 71.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4423076923076923 | | totalSentences | 104 | | uniqueOpeners | 46 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 97 | | matches | | 0 | "Maybe he took the service" | | 1 | "Maybe he was stabbed while" | | 2 | "Then why are his knees" | | 3 | "Just a sudden, suffocating cold," |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 97 | | matches | | 0 | "She checked the worn leather" | | 1 | "She swept her gaze across" | | 2 | "He was already shaking his" | | 3 | "She was crouched beside the" | | 4 | "She didn't touch the fabric," | | 5 | "You mentioned a struggle, Quinn" | | 6 | "It gets messy." | | 7 | "You walk through these tunnels" | | 8 | "She looked past him, down" | | 9 | "It's on his shirt." | | 10 | "It's on his shirt, yes," | | 11 | "He didn't fall here." | | 12 | "He was placed here." | | 13 | "She began to pace along" | | 14 | "Her mind raced back three" | | 15 | "She knelt again, this time" | | 16 | "It smelled faintly of old" | | 17 | "She pulled a small brass" | | 18 | "It felt heavy now, the" | | 19 | "You're overthinking it, Harlow." |
| | ratio | 0.247 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 97 | | matches | | 0 | "Detective Harlow Quinn stood five" | | 1 | "She checked the worn leather" | | 2 | "The damp subterranean air clung" | | 3 | "She swept her gaze across" | | 4 | "This was the Veil Market" | | 5 | "Tonight, however, the stalls were" | | 6 | "A body lay sprawled across" | | 7 | "Forensics had already cleared the" | | 8 | "Quinn stepped past a discarded" | | 9 | "This one was stammering, tripping" | | 10 | "Detective Sergeant Vance, a heavset" | | 11 | "He was already shaking his" | | 12 | "Fellow named Higgins." | | 13 | "Looks like a classic drug" | | 14 | "The transit police found him" | | 15 | "Quinn didn't look at Vance." | | 16 | "She was crouched beside the" | | 17 | "Vance flipped open his notebook," | | 18 | "Perp approaches from the northern" | | 19 | "Higgins tries to run." |
| | ratio | 0.691 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 6 | | matches | | 0 | "He was already shaking his head, his breath pluming in the chill air." | | 1 | "Vance flipped open his notebook, reading from his notes as if reciting scripture." | | 2 | "Her mind raced back three years, to the case that had shattered her world, the night she lost DS Morris under circumstances that the department's reports glosse…" | | 3 | "Just a sudden, suffocating cold, and a world that felt fundamentally wrong at the seams." | | 4 | "Something caught the pale beam of her flashlight—a faint, iridescent smear, glowing with a dull greenish sheen that didn't match anything in the Metropolitan Po…" | | 5 | "And right now, this body is telling me we're looking at a corpse that was delivered." |
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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 | |