| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1137 | | totalAiIsmAdverbs | 2 | | 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) | |
| 86.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1137 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "fractured" | | 1 | "footfall" | | 2 | "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 | 0 | | narrationSentences | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1130 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 908 | | uniqueNames | 14 | | maxNameDensity | 0.77 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 7 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 3 | | Tube | 1 | | Herrera | 5 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1130 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 26.28 | | std | 17.96 | | cv | 0.683 | | sampleLengths | | 0 | 56 | | 1 | 1 | | 2 | 19 | | 3 | 60 | | 4 | 26 | | 5 | 10 | | 6 | 62 | | 7 | 22 | | 8 | 33 | | 9 | 32 | | 10 | 5 | | 11 | 33 | | 12 | 26 | | 13 | 39 | | 14 | 4 | | 15 | 22 | | 16 | 8 | | 17 | 67 | | 18 | 23 | | 19 | 6 | | 20 | 53 | | 21 | 10 | | 22 | 46 | | 23 | 19 | | 24 | 27 | | 25 | 43 | | 26 | 42 | | 27 | 55 | | 28 | 4 | | 29 | 9 | | 30 | 10 | | 31 | 5 | | 32 | 37 | | 33 | 17 | | 34 | 2 | | 35 | 42 | | 36 | 20 | | 37 | 40 | | 38 | 27 | | 39 | 21 | | 40 | 16 | | 41 | 13 | | 42 | 18 |
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| 92.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 80 | | matches | | 0 | "got swallowed" | | 1 | "been peeled" | | 2 | "been painted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 149 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 96 | | ratio | 0.073 | | matches | | 0 | "She'd recognised him outside the Raven's Nest an hour ago—the Saint Christopher medallion catching the green light of that ugly neon sign, the scar on his forearm she'd memorised from a clinic photograph two years stale." | | 1 | "Stone steps descended below the street—an old pedestrian underpass, or it had been once." | | 2 | "Her torch beam jumped across tile and graffiti and then, abruptly, across stalls—canvas awnings strung between the pillars of a platform that no train had visited in fifty years." | | 3 | "It meant nothing here; she understood that even as she held it up." | | 4 | "He shook his head once—still that warning, still that strange regret—and turned to push through a gap between two stalls toward a curtained archway at the far end." | | 5 | "She'd stood over his empty coffin—empty, because there'd been nothing to bury, just his blood and his radio and a smell exactly like the one filling her nose now." | | 6 | "Something in his expression shifted—pity, maybe, or recognition." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 917 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.027262813522355506 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0054525627044711015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.77 | | std | 8.13 | | cv | 0.691 | | sampleLengths | | 0 | 21 | | 1 | 21 | | 2 | 14 | | 3 | 1 | | 4 | 11 | | 5 | 4 | | 6 | 4 | | 7 | 36 | | 8 | 2 | | 9 | 4 | | 10 | 11 | | 11 | 7 | | 12 | 11 | | 13 | 15 | | 14 | 10 | | 15 | 12 | | 16 | 14 | | 17 | 9 | | 18 | 27 | | 19 | 10 | | 20 | 1 | | 21 | 11 | | 22 | 18 | | 23 | 6 | | 24 | 5 | | 25 | 4 | | 26 | 8 | | 27 | 16 | | 28 | 8 | | 29 | 5 | | 30 | 21 | | 31 | 12 | | 32 | 10 | | 33 | 5 | | 34 | 11 | | 35 | 14 | | 36 | 25 | | 37 | 4 | | 38 | 14 | | 39 | 8 | | 40 | 8 | | 41 | 18 | | 42 | 29 | | 43 | 20 | | 44 | 3 | | 45 | 15 | | 46 | 5 | | 47 | 6 | | 48 | 5 | | 49 | 3 |
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| 83.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.53125 | | totalSentences | 96 | | uniqueOpeners | 51 | |
| 44.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 75 | | matches | | | ratio | 0.013 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 75 | | matches | | 0 | "She'd recognised him outside the" | | 1 | "She reached the spot and" | | 2 | "It caught him standing on" | | 3 | "He wiped rain from his" | | 4 | "She kept her face flat." | | 5 | "He shook his head, almost" | | 6 | "He started backing away, deeper," | | 7 | "She took the stairs two" | | 8 | "Her torch beam jumped across" | | 9 | "She'd heard the name in" | | 10 | "She'd filed it under superstition." | | 11 | "She wasn't filing it there" | | 12 | "She spun, fist rising." | | 13 | "He nodded at a wooden" | | 14 | "She showed her warrant card" | | 15 | "It meant nothing here; she" | | 16 | "Their eyes met over the" | | 17 | "He shook his head once—still" | | 18 | "They told her to climb" | | 19 | "She'd stood over his empty" |
| | ratio | 0.4 | |
| 40.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 75 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Detective Harlow Quinn ran through" | | 2 | "The name bounced off brick" | | 3 | "The figure didn't slow." | | 4 | "She'd recognised him outside the" | | 5 | "People like the ones who'd" | | 6 | "Quinn's watch dug into her" | | 7 | "Herrera vaulted a low railing" | | 8 | "She reached the spot and" | | 9 | "Stone steps descended below the" | | 10 | "A hoarding had been peeled" | | 11 | "The mouth of it breathed" | | 12 | "A Tube roundel hung above" | | 13 | "The line beneath the name" | | 14 | "A voice from the dark" | | 15 | "Herrera's voice, breath ragged but" | | 16 | "Quinn drew her torch, swept" | | 17 | "It caught him standing on" | | 18 | "He wiped rain from his" | | 19 | "The mention of Morris hit" |
| | ratio | 0.84 | |
| 66.67% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 75 | | matches | | 0 | "Now patching up people the" |
| | ratio | 0.013 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn ran through it, her coat slapping wet against her thighs, her boots throwing up arcs of dirty water." | | 1 | "She thought of the way the brass kept catching that wrong blue glow, of finger bones in a bowl, of a man who'd run from her precisely because he didn't want her…" | | 2 | "She moved through the crowd, shouldering past a hooded shape that hissed at her, fixing her eyes on the swaying curtain and the dark mouth behind it." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |