| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said grimly [grimly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 45 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.2 | | effectiveRatio | 0.089 | |
| 80.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1570 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "carefully" | | 1 | "sharply" | | 2 | "slightly" | | 3 | "very" | | 4 | "slowly" |
| |
| 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) | |
| 36.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1570 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "silence" | | 1 | "gloom" | | 2 | "familiar" | | 3 | "weight" | | 4 | "oppressive" | | 5 | "unsettled" | | 6 | "etched" | | 7 | "intricate" | | 8 | "pulse" | | 9 | "quickened" | | 10 | "implication" | | 11 | "stomach" | | 12 | "racing" | | 13 | "traced" | | 14 | "flickered" | | 15 | "wavering" | | 16 | "jaw clenched" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 3 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
| | 2 | | label | "jaw/fists clenched" | | count | 1 |
| | 3 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "eyes widened" | | 2 | "jaw clenched" | | 3 | "hung heavy in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 156 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1559 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1178 | | uniqueNames | 10 | | maxNameDensity | 2.46 | | worstName | "Quinn" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 2 | | Camden | 1 | | Harlow | 1 | | Quinn | 29 | | Harris | 1 | | Kowalski | 1 | | Eva | 19 | | Morris | 4 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Harris" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.269 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he’d rather be anywhere else" |
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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 | 1559 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 156 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 25.98 | | std | 22.64 | | cv | 0.871 | | sampleLengths | | 0 | 90 | | 1 | 3 | | 2 | 63 | | 3 | 77 | | 4 | 29 | | 5 | 84 | | 6 | 46 | | 7 | 27 | | 8 | 1 | | 9 | 90 | | 10 | 17 | | 11 | 33 | | 12 | 11 | | 13 | 38 | | 14 | 14 | | 15 | 37 | | 16 | 24 | | 17 | 49 | | 18 | 82 | | 19 | 8 | | 20 | 23 | | 21 | 8 | | 22 | 15 | | 23 | 49 | | 24 | 29 | | 25 | 5 | | 26 | 19 | | 27 | 6 | | 28 | 7 | | 29 | 43 | | 30 | 4 | | 31 | 46 | | 32 | 10 | | 33 | 22 | | 34 | 6 | | 35 | 8 | | 36 | 44 | | 37 | 25 | | 38 | 15 | | 39 | 10 | | 40 | 12 | | 41 | 38 | | 42 | 34 | | 43 | 25 | | 44 | 30 | | 45 | 8 | | 46 | 23 | | 47 | 4 | | 48 | 7 | | 49 | 5 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 123 | | matches | | 0 | "was frozen" | | 1 | "been—taken" | | 2 | "were scuffed" | | 3 | "been tied" | | 4 | "was gone" | | 5 | "was fixed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 192 | | matches | | 0 | "was already tucking" | | 1 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 156 | | ratio | 0.064 | | matches | | 0 | "The beam of her torch cut through the gloom, illuminating the peeling posters on the walls—faded advertisements for long-forgotten plays and products, their edges curled like dead leaves." | | 1 | "And then there was the body—or what was left of it." | | 2 | "Just a single, clean puncture wound in the center of the chest, as if something had been—taken." | | 3 | "It twitched erratically, as if caught between invisible forces, before settling on a direction that made no sense—toward the solid wall of the station." | | 4 | "The red curls were a wild halo around her head, and she was already tucking a strand behind her left ear—a nervous habit Quinn had come to recognize over the past few months." | | 5 | "The official report had called it a tragic accident—Morris had fallen down a flight of stairs during a chase." | | 6 | "And beneath the nails—" | | 7 | "A circle with a jagged line through it—like a crude, hastily drawn rune." | | 8 | "For a moment, Quinn could have sworn she saw something move in the darkness—something with too many limbs, too many eyes." | | 9 | "And then, from the darkness beyond the platform, came a sound—soft, wet, like something dragging itself across the floor." |
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| 99.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1191 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.04114189756507137 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.011754827875734676 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 156 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 156 | | mean | 9.99 | | std | 7.41 | | cv | 0.742 | | sampleLengths | | 0 | 26 | | 1 | 16 | | 2 | 28 | | 3 | 20 | | 4 | 3 | | 5 | 20 | | 6 | 13 | | 7 | 19 | | 8 | 11 | | 9 | 14 | | 10 | 22 | | 11 | 8 | | 12 | 6 | | 13 | 10 | | 14 | 17 | | 15 | 18 | | 16 | 11 | | 17 | 9 | | 18 | 15 | | 19 | 8 | | 20 | 6 | | 21 | 9 | | 22 | 20 | | 23 | 13 | | 24 | 4 | | 25 | 17 | | 26 | 5 | | 27 | 24 | | 28 | 3 | | 29 | 13 | | 30 | 3 | | 31 | 5 | | 32 | 3 | | 33 | 1 | | 34 | 8 | | 35 | 23 | | 36 | 33 | | 37 | 3 | | 38 | 23 | | 39 | 17 | | 40 | 13 | | 41 | 20 | | 42 | 6 | | 43 | 5 | | 44 | 9 | | 45 | 29 | | 46 | 9 | | 47 | 5 | | 48 | 2 | | 49 | 35 |
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| 45.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.25 | | totalSentences | 156 | | uniqueOpeners | 39 | |
| 86.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 115 | | matches | | 0 | "Just a single, clean puncture" | | 1 | "Just solid masonry." | | 2 | "Then she saw it." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 115 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "It was the lack of" | | 2 | "His shoes were scuffed, the" | | 3 | "She reached into her coat" | | 4 | "She pulled it out." | | 5 | "It twitched erratically, as if" | | 6 | "She’d seen strange things in" | | 7 | "It was wrong." | | 8 | "She turned at the sound" | | 9 | "She was a researcher, an" | | 10 | "She trailed off, but the" | | 11 | "She’d seen the way his" | | 12 | "She pushed the memory away." | | 13 | "It all pointed to something" | | 14 | "She knelt again, this time" | | 15 | "It was thicker, almost tar-like." | | 16 | "She walked toward it, running" | | 17 | "She traced it with her" | | 18 | "she said, her voice tight" | | 19 | "He was a piece in" |
| | ratio | 0.217 | |
| 73.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 115 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The beam of her torch" | | 3 | "She adjusted the worn leather" | | 4 | "Something was wrong." | | 5 | "The call had come in" | | 6 | "The uniformed officers on scene" | | 7 | "The corpse lay sprawled near" | | 8 | "The face was frozen in" | | 9 | "It was the lack of" | | 10 | "Quinn crouched beside the body," | | 11 | "The victim was male, mid-thirties," | | 12 | "His shoes were scuffed, the" | | 13 | "A man who walked a" | | 14 | "A man who moved in" | | 15 | "She reached into her coat" | | 16 | "She pulled it out." | | 17 | "A small brass compass, its" | | 18 | "The needle didn’t point north." | | 19 | "It twitched erratically, as if" |
| | ratio | 0.774 | |
| 43.48% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 115 | | matches | | 0 | "If the Veil Market was" |
| | ratio | 0.009 | |
| 97.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 3 | | matches | | 0 | "The victim was male, mid-thirties, dressed in a rumpled suit that had seen better days." | | 1 | "It twitched erratically, as if caught between invisible forces, before settling on a direction that made no sense—toward the solid wall of the station." | | 2 | "The way the coroner’s report had been inconclusive, as if the cause of death defied explanation." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "She trailed, but the implication hung heavy in the air" | | 1 | "Eva adjusted, her fingers trembling slightly" | | 2 | "she said, her voice tight" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "muttered PC Harris (mutter)" | | 1 | "she muttered (mutter)" | | 2 | "Eva’s voice squeaked (squeak)" |
| | dialogueSentences | 45 | | tagDensity | 0.178 | | leniency | 0.356 | | rawRatio | 0.375 | | effectiveRatio | 0.133 | |