| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1400 | | 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) | |
| 28.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1400 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "flickered" | | 1 | "gloom" | | 2 | "chilling" | | 3 | "pounding" | | 4 | "footsteps" | | 5 | "weight" | | 6 | "scanning" | | 7 | "lurched" | | 8 | "resolved" | | 9 | "fragmented" | | 10 | "echo" | | 11 | "echoed" | | 12 | "chaotic" | | 13 | "scanned" | | 14 | "vibrated" | | 15 | "cacophony" | | 16 | "familiar" |
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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 | 129 | | matches | (empty) | |
| 76.41% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 3 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 1390 | | ratio | 0.004 | | matches | | 0 | "Like old blood and lightning." | | 1 | "thunk" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 84.78% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1380 | | uniqueNames | 20 | | maxNameDensity | 1.3 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 18 | | Raven | 1 | | Nest | 1 | | Herrera | 7 | | Saint | 1 | | Christopher | 1 | | Morris | 5 | | Charing | 1 | | Cross | 1 | | Road | 2 | | Tube | 1 | | Tottenham | 1 | | Court | 1 | | Northern | 1 | | Camden | 1 | | Town | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Northern" | | 7 | "Camden" | | 8 | "Town" |
| | globalScore | 0.848 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 1 | | matches | | 0 | "graffiti that seemed to shift in her peripheral vision" |
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| 56.12% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.439 | | wordCount | 1390 | | matches | | 0 | "not just fearful, but resolved" | | 1 | "not darkness, but a pulsating, multicoloured glow" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 130 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 44.84 | | std | 27.3 | | cv | 0.609 | | sampleLengths | | 0 | 88 | | 1 | 76 | | 2 | 106 | | 3 | 26 | | 4 | 2 | | 5 | 71 | | 6 | 82 | | 7 | 65 | | 8 | 6 | | 9 | 59 | | 10 | 66 | | 11 | 54 | | 12 | 45 | | 13 | 44 | | 14 | 3 | | 15 | 49 | | 16 | 50 | | 17 | 11 | | 18 | 86 | | 19 | 31 | | 20 | 51 | | 21 | 25 | | 22 | 54 | | 23 | 11 | | 24 | 44 | | 25 | 10 | | 26 | 56 | | 27 | 17 | | 28 | 47 | | 29 | 42 | | 30 | 13 |
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| 91.66% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 129 | | matches | | 0 | "was driven" | | 1 | "was sheened" | | 2 | "were tagged" | | 3 | "was gone" | | 4 | "was gone" |
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| 21.43% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 224 | | matches | | 0 | "was heading" | | 1 | "was just pulling" | | 2 | "was already moving" | | 3 | "was going" | | 4 | "was running" | | 5 | "wasn’t leaving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 130 | | ratio | 0.069 | | matches | | 0 | "Her left wrist ached dully beneath the worn leather strap of her watch—a relic, like most of her now." | | 1 | "He paused, glanced left then right, his hand going to the Saint Christopher medallion at his throat—a nervous tic she’d noted in the surveillance photos." | | 2 | "A small one, perhaps, but in the three years since Morris had vanished into a case that made no sense—since the official report had been a masterpiece of obfuscation and the truth had curdled into something she could taste in her nightmares—every link mattered." | | 3 | "They spilled out onto a broader street—Charing Cross Road." | | 4 | "There—the flash of a dark jacket disappearing down the escalator to the Northern line." | | 5 | "The air from beyond was cooler, smelling of damp concrete and something else—ozone, and a faint, coppery tang." | | 6 | "As Quinn watched, he pulled something from his pocket—a small, pale object—and slotted it into a crevice beside the door." | | 7 | "From beyond the door, a laugh echoed—high, clear, and utterly inhuman." | | 8 | "The sound and smell and light of the market washed over her—a cacophony of the impossible." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 741 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short, curly dark hair" |
| | adverbCount | 12 | | adverbRatio | 0.016194331983805668 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006747638326585695 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 10.69 | | std | 6.83 | | cv | 0.638 | | sampleLengths | | 0 | 14 | | 1 | 23 | | 2 | 23 | | 3 | 9 | | 4 | 19 | | 5 | 1 | | 6 | 18 | | 7 | 10 | | 8 | 20 | | 9 | 2 | | 10 | 25 | | 11 | 5 | | 12 | 8 | | 13 | 23 | | 14 | 10 | | 15 | 12 | | 16 | 4 | | 17 | 44 | | 18 | 5 | | 19 | 3 | | 20 | 12 | | 21 | 3 | | 22 | 3 | | 23 | 2 | | 24 | 14 | | 25 | 13 | | 26 | 8 | | 27 | 7 | | 28 | 21 | | 29 | 8 | | 30 | 16 | | 31 | 18 | | 32 | 24 | | 33 | 24 | | 34 | 6 | | 35 | 10 | | 36 | 9 | | 37 | 10 | | 38 | 7 | | 39 | 23 | | 40 | 6 | | 41 | 12 | | 42 | 14 | | 43 | 14 | | 44 | 19 | | 45 | 12 | | 46 | 9 | | 47 | 19 | | 48 | 15 | | 49 | 2 |
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| 40.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2846153846153846 | | totalSentences | 130 | | uniqueOpeners | 37 | |
| 81.97% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 122 | | matches | | 0 | "Instead, he turned into a" | | 1 | "Then he stepped through, and" | | 2 | "Then she stepped across the" |
| | ratio | 0.025 | |
| 62.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 122 | | matches | | 0 | "She’d been here for two" | | 1 | "Her left wrist ached dully" | | 2 | "He paused, glanced left then" | | 3 | "Her movements were economical, born" | | 4 | "She waited until he turned" | | 5 | "Her sharp jaw was set," | | 6 | "He was a link." | | 7 | "He’d made her." | | 8 | "Her shoes skidded on the" | | 9 | "She righted herself, hand slapping" | | 10 | "He was faster, younger, fueled" | | 11 | "She was driven by something" | | 12 | "He led her through a" | | 13 | "She gained on him on" | | 14 | "He vaulted a low iron" | | 15 | "They spilled out onto a" | | 16 | "He was heading for the" | | 17 | "She saw him duck into" | | 18 | "She pushed through the crowd" | | 19 | "She flashed her badge at" |
| | ratio | 0.393 | |
| 41.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 122 | | matches | | 0 | "The rain fell in relentless," | | 1 | "Detective Harlow Quinn leaned against" | | 2 | "She’d been here for two" | | 3 | "Her left wrist ached dully" | | 4 | "The bar door swung open," | | 5 | "A figure emerged, pulling a" | | 6 | "He paused, glanced left then" | | 7 | "Quinn pushed off the wall." | | 8 | "Her movements were economical, born" | | 9 | "She waited until he turned" | | 10 | "The water soaked through her" | | 11 | "Her sharp jaw was set," | | 12 | "He was a link." | | 13 | "A small one, perhaps, but" | | 14 | "Herrera picked up his pace." | | 15 | "He’d made her." | | 16 | "A reflex glance over his" | | 17 | "Quinn abandoned subtlety." | | 18 | "Quinn swore, the curse lost" | | 19 | "Her shoes skidded on the" |
| | ratio | 0.836 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 122 | | matches | | 0 | "Even in the gloom, Quinn" | | 1 | "To go in was to" | | 2 | "To acknowledge that the world" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 2 | | matches | | 0 | "A small one, perhaps, but in the three years since Morris had vanished into a case that made no sense—since the official report had been a masterpiece of obfusc…" | | 1 | "Quinn darted after him, holding up a hand to a taxi that screeched to a halt, the driver’s angry yell fading behind her." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |