| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 1 | | adverbTags | | 0 | "Tomás said quietly [quietly]" |
| | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.333 | | effectiveRatio | 0.2 | |
| 95.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1144 | | 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) | |
| 47.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1144 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "rhythmic" | | 2 | "maw" | | 3 | "electric" | | 4 | "silence" | | 5 | "cacophony" | | 6 | "chaotic" | | 7 | "lilt" | | 8 | "weight" | | 9 | "scanning" | | 10 | "shimmered" |
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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 | 66 | | matches | (empty) | |
| 77.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 66 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 941 | | uniqueNames | 15 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | London | 1 | | Morris | 2 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | | globalScore | 0.862 | | windowScore | 0.833 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like one" | | 1 | "felt like a thin coat of paint peeling" |
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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 | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 75.22% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 47.33 | | std | 19.56 | | cv | 0.413 | | sampleLengths | | 0 | 69 | | 1 | 42 | | 2 | 68 | | 3 | 45 | | 4 | 66 | | 5 | 52 | | 6 | 70 | | 7 | 38 | | 8 | 5 | | 9 | 61 | | 10 | 39 | | 11 | 57 | | 12 | 52 | | 13 | 8 | | 14 | 23 | | 15 | 67 | | 16 | 18 | | 17 | 62 | | 18 | 17 | | 19 | 60 | | 20 | 62 | | 21 | 60 | | 22 | 42 | | 23 | 53 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 66 | | matches | | |
| 68.42% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 152 | | matches | | 0 | "was losing" | | 1 | "was intensifying" | | 2 | "was willing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 7 | | totalSentences | 73 | | ratio | 0.096 | | matches | | 0 | "The rain didn't just fall in Soho; it hammered, turning the asphalt into a black mirror that fractured every neon light into a thousand jagged shards." | | 1 | "Detective Harlow Quinn didn't run—she pursued." | | 2 | "The suspect reached a dead end—or what looked like one." | | 3 | "The smell wafting up from the hole wasn't the usual scent of London sewers—sulfur and wet stone—but something sweeter, like old parchment and bruised lilies." | | 4 | "The suspect was linked to the clique she had been hunting for months—a group that operated in the seams of the city, slipping through the cracks of the law and physics alike." | | 5 | "She was a detective of the Metropolitan Police; she dealt in fingerprints, CCTV, and sworn testimonies." | | 6 | "He looked less like a criminal and more like a tired doctor, though his eyes—warm brown and perceptive—held a wary intelligence." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 951 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, leather-bound grimoires." |
| | adverbCount | 18 | | adverbRatio | 0.01892744479495268 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005257623554153523 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 15.56 | | std | 10.14 | | cv | 0.652 | | sampleLengths | | 0 | 26 | | 1 | 6 | | 2 | 17 | | 3 | 20 | | 4 | 8 | | 5 | 15 | | 6 | 19 | | 7 | 3 | | 8 | 16 | | 9 | 24 | | 10 | 11 | | 11 | 4 | | 12 | 10 | | 13 | 10 | | 14 | 16 | | 15 | 19 | | 16 | 6 | | 17 | 11 | | 18 | 25 | | 19 | 24 | | 20 | 5 | | 21 | 26 | | 22 | 21 | | 23 | 6 | | 24 | 11 | | 25 | 10 | | 26 | 11 | | 27 | 32 | | 28 | 10 | | 29 | 11 | | 30 | 17 | | 31 | 5 | | 32 | 12 | | 33 | 15 | | 34 | 10 | | 35 | 24 | | 36 | 12 | | 37 | 20 | | 38 | 7 | | 39 | 16 | | 40 | 8 | | 41 | 15 | | 42 | 18 | | 43 | 12 | | 44 | 6 | | 45 | 16 | | 46 | 18 | | 47 | 8 | | 48 | 10 | | 49 | 13 |
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| 44.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3424657534246575 | | totalSentences | 73 | | uniqueOpeners | 25 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 68.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 66 | | matches | | 0 | "Her stride was a study" | | 1 | "She kept her center of" | | 2 | "He was lean and desperate," | | 3 | "He banked a hard right," | | 4 | "She swung around the corner," | | 5 | "She glanced at the worn" | | 6 | "He scrambled toward a heavy" | | 7 | "She knelt, the dampness of" | | 8 | "It was a smell that" | | 9 | "She thought of DS Morris." | | 10 | "She thought of the way" | | 11 | "She looked back at the" | | 12 | "She didn't believe in fairy" | | 13 | "She slid into the hole." | | 14 | "She rounded a bend and" | | 15 | "It was a chaotic sprawl" | | 16 | "She was a detective of" | | 17 | "He wore a faded medical" | | 18 | "He looked less like a" | | 19 | "He shifted his weight, and" |
| | ratio | 0.379 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 66 | | matches | | 0 | "The rain didn't just fall" | | 1 | "Detective Harlow Quinn didn't run—she" | | 2 | "Her stride was a study" | | 3 | "She kept her center of" | | 4 | "He was lean and desperate," | | 5 | "He banked a hard right," | | 6 | "Quinn didn't hesitate." | | 7 | "She swung around the corner," | | 8 | "She glanced at the worn" | | 9 | "The hour when the city" | | 10 | "The suspect reached a dead" | | 11 | "He scrambled toward a heavy" | | 12 | "Quinn reached the opening and" | | 13 | "She knelt, the dampness of" | | 14 | "The smell wafting up from" | | 15 | "It was a smell that" | | 16 | "She thought of DS Morris." | | 17 | "She thought of the way" | | 18 | "Morris had disappeared into a" | | 19 | "She looked back at the" |
| | ratio | 0.833 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 66 | | matches | | 0 | "If she called for backup," | | 1 | "If she waited for a" |
| | ratio | 0.03 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 2 | | matches | | 0 | "The tiles were cracked and peeling, the air thick with a damp, electric charge that made the fine hairs on her arms stand up." | | 1 | "She stepped into the violet haze, her boots clicking against the ancient stone, moving deeper into a world that defied every logic she had ever known, driven by…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn said, her voice a sharp blade" | | 1 | "Quinn replied, her gaze scanning the crowd behind him" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |