| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn peeled back [back]" |
| | dialogueSentences | 46 | | tagDensity | 0.283 | | leniency | 0.565 | | rawRatio | 0.077 | | effectiveRatio | 0.043 | |
| 96.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1356 | | 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) | |
| 70.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1356 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "measured" | | 1 | "traced" | | 2 | "etched" | | 3 | "pulsed" | | 4 | "intricate" | | 5 | "weight" |
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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 | 97 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 97 | | 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 | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1364 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 908 | | uniqueNames | 8 | | maxNameDensity | 1.21 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Price" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 11 | | Callum | 1 | | Price | 10 | | Banksy | 1 | | Morris | 2 | | Brixton | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Price" | | 3 | "Banksy" | | 4 | "Morris" |
| | places | | | globalScore | 0.894 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.733 | | wordCount | 1364 | | matches | | 0 | "not a rectangle but a partial seal, the incomplete border of something meant to" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 25.74 | | std | 20.68 | | cv | 0.803 | | sampleLengths | | 0 | 40 | | 1 | 58 | | 2 | 53 | | 3 | 7 | | 4 | 4 | | 5 | 4 | | 6 | 20 | | 7 | 59 | | 8 | 9 | | 9 | 54 | | 10 | 9 | | 11 | 49 | | 12 | 38 | | 13 | 5 | | 14 | 64 | | 15 | 4 | | 16 | 8 | | 17 | 7 | | 18 | 16 | | 19 | 32 | | 20 | 8 | | 21 | 3 | | 22 | 41 | | 23 | 60 | | 24 | 6 | | 25 | 35 | | 26 | 45 | | 27 | 14 | | 28 | 4 | | 29 | 18 | | 30 | 67 | | 31 | 28 | | 32 | 2 | | 33 | 40 | | 34 | 61 | | 35 | 6 | | 36 | 45 | | 37 | 2 | | 38 | 9 | | 39 | 5 | | 40 | 46 | | 41 | 5 | | 42 | 17 | | 43 | 21 | | 44 | 26 | | 45 | 16 | | 46 | 57 | | 47 | 9 | | 48 | 49 | | 49 | 12 |
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| 83.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 97 | | matches | | 0 | "was rusted" | | 1 | "was covered" | | 2 | "been scored" | | 3 | "was blackened" | | 4 | "been pulled" | | 5 | "been open" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 152 | | matches | (empty) | |
| 10.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 130 | | ratio | 0.046 | | matches | | 0 | "The brickwork was covered in graffiti — tags, political slogans, a faded Banksy knockoff." | | 1 | "Even in the torchlight, she could see them shifting — the dark lines pulsed faintly, like veins carrying something that wasn't blood." | | 2 | "She ignored the discomfort and checked her watch — the leather strap cracked and supple against her wrist, 18 years of wear." | | 3 | "An image surfaced unbidden — three years ago, another alley, another dead man." | | 4 | "His eyes had been open when they arrived; someone from forensics had closed them." | | 5 | "The scored lines in the concrete forming a shape she now recognized — not a rectangle but a partial seal, the incomplete border of something meant to contain or summon." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 908 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02753303964757709 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003303964757709251 | |
| 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 | 10.49 | | std | 8.04 | | cv | 0.766 | | sampleLengths | | 0 | 20 | | 1 | 14 | | 2 | 1 | | 3 | 5 | | 4 | 12 | | 5 | 4 | | 6 | 15 | | 7 | 2 | | 8 | 10 | | 9 | 15 | | 10 | 21 | | 11 | 22 | | 12 | 10 | | 13 | 4 | | 14 | 3 | | 15 | 4 | | 16 | 4 | | 17 | 3 | | 18 | 17 | | 19 | 13 | | 20 | 20 | | 21 | 7 | | 22 | 1 | | 23 | 10 | | 24 | 8 | | 25 | 4 | | 26 | 5 | | 27 | 10 | | 28 | 13 | | 29 | 12 | | 30 | 13 | | 31 | 6 | | 32 | 5 | | 33 | 4 | | 34 | 12 | | 35 | 8 | | 36 | 4 | | 37 | 9 | | 38 | 9 | | 39 | 3 | | 40 | 2 | | 41 | 2 | | 42 | 22 | | 43 | 14 | | 44 | 2 | | 45 | 5 | | 46 | 19 | | 47 | 2 | | 48 | 2 | | 49 | 17 |
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| 67.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.45384615384615384 | | totalSentences | 130 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 81 | | matches | | 0 | "Too dark for mere water." | | 1 | "Then her light caught something." | | 2 | "Then she put the phone" |
| | ratio | 0.037 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 81 | | matches | | 0 | "He wore a grey suit" | | 1 | "She tilted her head, studying" | | 2 | "They glistened faintly in the" | | 3 | "He was twenty-four, built like" | | 4 | "She traced one with her" | | 5 | "She worked the fabric free" | | 6 | "He'd walked here." | | 7 | "She moved to the other" | | 8 | "She followed it with her" | | 9 | "It traced a partial rectangle" | | 10 | "He crossed the alley in" | | 11 | "She knelt beside him" | | 12 | "He touched the groove and" | | 13 | "He pressed his palm flat" | | 14 | "She walked around it, crouched" | | 15 | "He stepped back from the" | | 16 | "Her knees ached." | | 17 | "She ignored the discomfort and" | | 18 | "Her partner, DS Morris, lying" | | 19 | "She pushed the memory down." |
| | ratio | 0.333 | |
| 40.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 81 | | matches | | 0 | "The body lay in the" | | 1 | "Rain hadn't fallen in three" | | 2 | "Detective Harlow Quinn crouched beside" | | 3 | "He wore a grey suit" | | 4 | "She tilted her head, studying" | | 5 | "They glistened faintly in the" | | 6 | "DS Callum Price appeared at" | | 7 | "He was twenty-four, built like" | | 8 | "Quinn didn't look up." | | 9 | "Price's jaw worked." | | 10 | "Quinn peeled back the left" | | 11 | "The skin beneath was a" | | 12 | "She traced one with her" | | 13 | "Price crouched beside her." | | 14 | "Quinn pulled the sleeve higher" | | 15 | "The lines branched across his" | | 16 | "She worked the fabric free" | | 17 | "The marks fanned across his" | | 18 | "Price set his cup down." | | 19 | "Quinn stood and swept the" |
| | ratio | 0.84 | |
| 61.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 81 | | matches | | 0 | "Even in the torchlight, she" |
| | ratio | 0.012 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "He wore a grey suit that had cost serious money, now stiff with dried fluid." | | 1 | "He was twenty-four, built like a rugby prop, and still carried the enthusiasm of a man who thought detective work resembled television." | | 2 | "Even in the torchlight, she could see them shifting — the dark lines pulsed faintly, like veins carrying something that wasn't blood." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 46 | | tagDensity | 0.065 | | leniency | 0.13 | | rawRatio | 0.333 | | effectiveRatio | 0.043 | |