| 86.79% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 3 | | adverbTags | | 0 | "Quinn knelt again [again]" | | 1 | "Eva said quietly [quietly]" | | 2 | "she said finally [finally]" |
| | dialogueSentences | 53 | | tagDensity | 0.396 | | leniency | 0.792 | | rawRatio | 0.143 | | effectiveRatio | 0.113 | |
| 94.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1721 | | 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) | |
| 59.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1721 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "silence" | | 1 | "cataloged" | | 2 | "stark" | | 3 | "chaotic" | | 4 | "standard" | | 5 | "raced" | | 6 | "etched" | | 7 | "intricate" | | 8 | "trembled" | | 9 | "unwavering" | | 10 | "echoing" | | 11 | "weight" | | 12 | "glinting" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 164 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1703 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 49.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1148 | | uniqueNames | 15 | | maxNameDensity | 2 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Town | 1 | | Second | 1 | | World | 1 | | War | 1 | | Harlow | 1 | | Quinn | 23 | | Tube | 2 | | Kowalski | 1 | | Eva | 16 | | Three | 1 | | Morris | 3 | | Victorian-tiled | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Victorian-tiled" |
| | globalScore | 0.498 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to shift under the light" |
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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 | 1703 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 164 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 30.41 | | std | 20.27 | | cv | 0.666 | | sampleLengths | | 0 | 67 | | 1 | 69 | | 2 | 16 | | 3 | 76 | | 4 | 2 | | 5 | 50 | | 6 | 17 | | 7 | 29 | | 8 | 54 | | 9 | 8 | | 10 | 30 | | 11 | 7 | | 12 | 71 | | 13 | 14 | | 14 | 34 | | 15 | 31 | | 16 | 10 | | 17 | 58 | | 18 | 7 | | 19 | 33 | | 20 | 8 | | 21 | 43 | | 22 | 30 | | 23 | 27 | | 24 | 3 | | 25 | 10 | | 26 | 31 | | 27 | 52 | | 28 | 18 | | 29 | 48 | | 30 | 12 | | 31 | 25 | | 32 | 12 | | 33 | 39 | | 34 | 22 | | 35 | 37 | | 36 | 40 | | 37 | 14 | | 38 | 37 | | 39 | 66 | | 40 | 40 | | 41 | 30 | | 42 | 29 | | 43 | 81 | | 44 | 18 | | 45 | 9 | | 46 | 43 | | 47 | 1 | | 48 | 32 | | 49 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 135 | | matches | | 0 | "been sealed" | | 1 | "were cracked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 202 | | matches | (empty) | |
| 55.75% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 164 | | ratio | 0.03 | | matches | | 0 | "The air changed the moment she stepped into the abandoned Tube station—cold, damp, and carrying a metallic scent that wasn’t just rust." | | 1 | "The cause of death was obvious—a single gunshot wound to the chest." | | 2 | "At the edge of her beam, she saw it—a single drop of dark blood on the stone step." | | 3 | "She pointed to symbols carved into the brickwork near the floor—the same swirling patterns as on the compass." | | 4 | "He’s shot, but not with a normal bullet—something that leaves residue, something that doesn’t behave like standard ballistics." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1160 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.025 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008620689655172414 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 164 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 164 | | mean | 10.38 | | std | 7.63 | | cv | 0.735 | | sampleLengths | | 0 | 22 | | 1 | 15 | | 2 | 22 | | 3 | 8 | | 4 | 15 | | 5 | 22 | | 6 | 16 | | 7 | 16 | | 8 | 9 | | 9 | 7 | | 10 | 14 | | 11 | 9 | | 12 | 12 | | 13 | 12 | | 14 | 5 | | 15 | 2 | | 16 | 12 | | 17 | 10 | | 18 | 2 | | 19 | 2 | | 20 | 15 | | 21 | 18 | | 22 | 10 | | 23 | 5 | | 24 | 7 | | 25 | 10 | | 26 | 11 | | 27 | 18 | | 28 | 14 | | 29 | 10 | | 30 | 8 | | 31 | 10 | | 32 | 4 | | 33 | 8 | | 34 | 8 | | 35 | 4 | | 36 | 26 | | 37 | 7 | | 38 | 3 | | 39 | 5 | | 40 | 7 | | 41 | 54 | | 42 | 2 | | 43 | 4 | | 44 | 10 | | 45 | 9 | | 46 | 25 | | 47 | 13 | | 48 | 18 | | 49 | 10 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.32926829268292684 | | totalSentences | 164 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 120 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 120 | | matches | | 0 | "It was the sharp, coppery" | | 1 | "She adjusted the strap as" | | 2 | "He wore a dark wool" | | 3 | "Her curly red hair was" | | 4 | "She didn’t look like she" | | 5 | "She belonged in a library." | | 6 | "She crouched, examining the ground" | | 7 | "Her gaze swept the platform." | | 8 | "She pointed to the clean," | | 9 | "She moved around the body," | | 10 | "She used her gloved hand" | | 11 | "It wasn’t blood." | | 12 | "It wasn’t anything she recognized." | | 13 | "She pulled out a small" | | 14 | "She’d dismissed it as urban" | | 15 | "She looked from the compass" | | 16 | "Her eyes narrowed" | | 17 | "She ran her gloved hand" | | 18 | "Her flashlight beam cut into" | | 19 | "Her light illuminated steps carved" |
| | ratio | 0.333 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 120 | | matches | | 0 | "The Camden Town branch line" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The air changed the moment" | | 3 | "It was the sharp, coppery" | | 4 | "The platform stretched into shadow," | | 5 | "Police floodlights had been set" | | 6 | "A forensic team in white" | | 7 | "Quinn’s worn leather watch felt" | | 8 | "She adjusted the strap as" | | 9 | "A man lay sprawled across" | | 10 | "He wore a dark wool" | | 11 | "Quinn’s eyes cataloged the details:" | | 12 | "The cause of death was" | | 13 | "Execution-style, but there was no" | | 14 | "Eva Kowalski stood near the" | | 15 | "Her curly red hair was" | | 16 | "She didn’t look like she" | | 17 | "She belonged in a library." | | 18 | "Quinn said, her voice flat" | | 19 | "Eva’s knuckles were white on" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 120 | | matches | (empty) | | ratio | 0 | |
| 68.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 5 | | matches | | 0 | "The air changed the moment she stepped into the abandoned Tube station—cold, damp, and carrying a metallic scent that wasn’t just rust." | | 1 | "Police floodlights had been set up around a central area, casting harsh white light that made the surrounding darkness seem even deeper." | | 2 | "The casing was tarnished with verdigris, its face etched with intricate, swirling symbols that seemed to shift under the light." | | 3 | "It felt gritty, like sand, but with a static charge that made the hairs on her arm stand up." | | 4 | "He’s shot, but not with a normal bullet—something that leaves residue, something that doesn’t behave like standard ballistics." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "she said, her voice echoing softly in the passage" | | 2 | "Quinn told, her voice leaving no room for argument" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 53 | | tagDensity | 0.132 | | leniency | 0.264 | | rawRatio | 0.143 | | effectiveRatio | 0.038 | |