| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1223 | | 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) | |
| 75.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1223 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "gleaming" | | 1 | "footsteps" | | 2 | "echoed" | | 3 | "flickered" | | 4 | "reminder" |
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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 | 108 | | matches | (empty) | |
| 89.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1217 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1208 | | uniqueNames | 15 | | maxNameDensity | 0.91 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 3 | | Harlow | 1 | | Quinn | 8 | | Herrera | 11 | | Saint | 2 | | Christopher | 2 | | Morris | 3 | | Camden | 2 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Met | 1 | | Standing | 1 |
| | persons | | 0 | "Nest" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Market" | | 8 | "Met" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1217 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 109 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 57.95 | | std | 33.76 | | cv | 0.583 | | sampleLengths | | 0 | 78 | | 1 | 54 | | 2 | 3 | | 3 | 42 | | 4 | 100 | | 5 | 95 | | 6 | 109 | | 7 | 98 | | 8 | 2 | | 9 | 75 | | 10 | 65 | | 11 | 70 | | 12 | 66 | | 13 | 69 | | 14 | 78 | | 15 | 58 | | 16 | 2 | | 17 | 83 | | 18 | 53 | | 19 | 11 | | 20 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 108 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 224 | | matches | (empty) | |
| 38.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 109 | | ratio | 0.037 | | matches | | 0 | "A black cab nearly took her out; she slammed a palm on its bonnet and kept moving." | | 1 | "Herrera was one of theirs—off-the-books medic, former NHS, licence gone after he’d started treating things that weren’t supposed to exist." | | 2 | "A sound drifted up—low voices, the clink of glass, a laugh that didn’t belong to any human throat she’d ever heard." | | 3 | "Figures moved between them—some human-shaped, some not." |
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| 99.26% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1224 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 50 | | adverbRatio | 0.04084967320261438 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007352941176470588 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 11.17 | | std | 7.97 | | cv | 0.713 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 24 | | 3 | 18 | | 4 | 7 | | 5 | 14 | | 6 | 19 | | 7 | 9 | | 8 | 2 | | 9 | 3 | | 10 | 3 | | 11 | 2 | | 12 | 19 | | 13 | 5 | | 14 | 3 | | 15 | 13 | | 16 | 13 | | 17 | 10 | | 18 | 8 | | 19 | 16 | | 20 | 21 | | 21 | 17 | | 22 | 15 | | 23 | 8 | | 24 | 32 | | 25 | 5 | | 26 | 2 | | 27 | 5 | | 28 | 5 | | 29 | 26 | | 30 | 12 | | 31 | 26 | | 32 | 3 | | 33 | 14 | | 34 | 20 | | 35 | 10 | | 36 | 2 | | 37 | 3 | | 38 | 20 | | 39 | 7 | | 40 | 4 | | 41 | 5 | | 42 | 8 | | 43 | 4 | | 44 | 28 | | 45 | 3 | | 46 | 24 | | 47 | 26 | | 48 | 2 | | 49 | 8 |
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| 68.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.43119266055045874 | | totalSentences | 109 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 102 | | matches | | 0 | "Then he ran." | | 1 | "Always the clique." | | 2 | "Then the second." | | 3 | "Only that low hum, like" | | 4 | "Then the market swallowed him" |
| | ratio | 0.049 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 102 | | matches | | 0 | "His warm brown eyes found" | | 1 | "He cut left into an" | | 2 | "She went over after him," | | 3 | "She gained on him at" | | 4 | "He twisted, elbow snapping back." | | 5 | "It caught her in the" | | 6 | "Her watch face was fogged," | | 7 | "She thought of DS Morris," | | 8 | "She’d been watching the Nest" | | 9 | "He led her into Camden." | | 10 | "He slipped through." | | 11 | "She had no token." | | 12 | "She had no backup." | | 13 | "She had eighteen years of" | | 14 | "Her hand went to the" | | 15 | "They’d pull her off the" | | 16 | "She knew it." | | 17 | "They all knew it." | | 18 | "She thought of Morris again." | | 19 | "He’d gone into a place" |
| | ratio | 0.284 | |
| 92.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 102 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "Tomás Herrera stepped out into" | | 3 | "The Saint Christopher medallion at" | | 4 | "His warm brown eyes found" | | 5 | "Quinn launched after him, boots" | | 6 | "Military precision in every stride." | | 7 | "He cut left into an" | | 8 | "Quinn followed, shoulder clipping a" | | 9 | "Herrera vaulted a low wall" | | 10 | "She went over after him," | | 11 | "A black cab nearly took" | | 12 | "Herrera was already twenty metres" | | 13 | "She gained on him at" | | 14 | "Knife work, old but deep." | | 15 | "Fingers closed on wet fabric." | | 16 | "He twisted, elbow snapping back." | | 17 | "It caught her in the" | | 18 | "The chase stretched north through" | | 19 | "Quinn’s lungs burned." |
| | ratio | 0.735 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 102 | | matches | | 0 | "By the time she was" | | 1 | "If she waited, he’d vanish" | | 2 | "If she followed, she walked" |
| | ratio | 0.029 | |
| 2.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 10 | | matches | | 0 | "Green neon from The Raven’s Nest smeared across every puddle, a sickly glow that made the wet cobbles look diseased." | | 1 | "Close enough to see the medallion bouncing against his chest, close enough to catch the flash of the long pale scar that ran along his left forearm when his sle…" | | 2 | "The chase stretched north through rain-slicked streets that grew quieter, darker, the neon giving way to sodium lamps that turned everything the colour of old b…" | | 3 | "Herrera glanced back once, brown eyes wide, then ducked down a side passage that ended at a rusted iron gate half-hidden behind a skip full of sodden cardboard." | | 4 | "Quinn reached it three seconds later, shouldered it open, and found herself at the top of a concrete stairwell that dropped into absolute black." | | 5 | "Suspect entered an unmapped underground market that sells enchanted goods and banned alchemical substances?" | | 6 | "The rain noise faded behind her, replaced by the drip of water somewhere in the dark and the distant murmur of a market that wasn’t supposed to be real." | | 7 | "The third-to-last stair was slick with something that wasn’t water." | | 8 | "She started forward, baton low at her side, every sense screaming that this was the kind of place that ate detectives and left no bodies to find." | | 9 | "The Saint Christopher medallion glinted as Herrera turned, just slightly, as if he’d felt her arrive." |
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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 | |