| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.424 | | leniency | 0.848 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1192 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "gently" | | 2 | "carefully" |
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| 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) | |
| 49.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1192 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "familiar" | | 1 | "pristine" | | 2 | "ensure" | | 3 | "scanned" | | 4 | "perfect" | | 5 | "etched" | | 6 | "tracing" | | 7 | "complex" | | 8 | "rhythmic" | | 9 | "whisper" | | 10 | "gleaming" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 94 | | matches | (empty) | |
| 21.28% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 7 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | 0 | "started to" | | 1 | "seemed to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1190 | | ratio | 0.008 | | matches | | 0 | "The air feels thin, Harlow. Like it's been used up." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 958 | | uniqueNames | 7 | | maxNameDensity | 1.46 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Harlow | 14 | | Quinn | 2 | | Davies | 12 | | Windsor | 1 | | Morris | 1 | | Three | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Morris" |
| | places | | | globalScore | 0.769 | | windowScore | 0.833 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like nothing Harlow had ever seen" | | 1 | "sound that seemed to vibrate up through the soles of her boots" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.84 | | wordCount | 1190 | | matches | | 0 | "not brick and mortar, but a deep, starless black" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 36.06 | | std | 24.76 | | cv | 0.687 | | sampleLengths | | 0 | 69 | | 1 | 36 | | 2 | 32 | | 3 | 73 | | 4 | 16 | | 5 | 22 | | 6 | 19 | | 7 | 99 | | 8 | 3 | | 9 | 24 | | 10 | 47 | | 11 | 21 | | 12 | 76 | | 13 | 52 | | 14 | 11 | | 15 | 49 | | 16 | 7 | | 17 | 39 | | 18 | 12 | | 19 | 70 | | 20 | 16 | | 21 | 5 | | 22 | 23 | | 23 | 43 | | 24 | 35 | | 25 | 6 | | 26 | 30 | | 27 | 55 | | 28 | 11 | | 29 | 51 | | 30 | 66 | | 31 | 9 | | 32 | 63 |
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| 75.40% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 94 | | matches | | 0 | "been made" | | 1 | "was rumpled" | | 2 | "was clenched" | | 3 | "was clutched" | | 4 | "was etched" | | 5 | "were blackened" | | 6 | "been used" | | 7 | "been mugged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 161 | | matches | | 0 | "wasn't pointing" | | 1 | "was spinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 960 | | adjectiveStacks | 1 | | stackExamples | | 0 | "unnatural oil-slick sheen" |
| | adverbCount | 31 | | adverbRatio | 0.03229166666666667 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.0125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 10.72 | | std | 6.5 | | cv | 0.606 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 6 | | 3 | 20 | | 4 | 25 | | 5 | 11 | | 6 | 12 | | 7 | 20 | | 8 | 13 | | 9 | 11 | | 10 | 12 | | 11 | 9 | | 12 | 6 | | 13 | 7 | | 14 | 15 | | 15 | 6 | | 16 | 10 | | 17 | 11 | | 18 | 7 | | 19 | 4 | | 20 | 10 | | 21 | 9 | | 22 | 9 | | 23 | 7 | | 24 | 11 | | 25 | 13 | | 26 | 19 | | 27 | 1 | | 28 | 8 | | 29 | 13 | | 30 | 14 | | 31 | 4 | | 32 | 3 | | 33 | 18 | | 34 | 6 | | 35 | 6 | | 36 | 13 | | 37 | 16 | | 38 | 5 | | 39 | 7 | | 40 | 14 | | 41 | 7 | | 42 | 11 | | 43 | 11 | | 44 | 15 | | 45 | 6 | | 46 | 6 | | 47 | 4 | | 48 | 14 | | 49 | 9 |
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| 62.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4144144144144144 | | totalSentences | 111 | | uniqueOpeners | 46 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Finally, with a soft, dry" |
| | ratio | 0.011 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 90 | | matches | | 0 | "Her worn leather watch read" | | 1 | "She didn't look at him," | | 2 | "His face was a mask" | | 3 | "It was the lack of" | | 4 | "His suit was rumpled, yes," | | 5 | "She pointed with a gloved" | | 6 | "It was clenched into a" | | 7 | "She leaned in, her sharp" | | 8 | "Her eyes scanned the ground" | | 9 | "It was long and iridescent," | | 10 | "She didn't touch it." | | 11 | "He held up an evidence" | | 12 | "It was spinning, a frantic," | | 13 | "It was the smell." | | 14 | "She walked a slow perimeter" | | 15 | "It was a faint scorch" | | 16 | "She ran a gloved hand" | | 17 | "She thought of DS Morris," | | 18 | "He'd said the same thing," | | 19 | "Her attention returned to the" |
| | ratio | 0.333 | |
| 32.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 90 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn ignored the" | | 2 | "Her worn leather watch read" | | 3 | "She didn't look at him," | | 4 | "The tiles were a sickly" | | 5 | "Davies, a young sergeant with" | | 6 | "Harlow stepped closer, her movements" | | 7 | "The victim, a man in" | | 8 | "His face was a mask" | | 9 | "It was the lack of" | | 10 | "His suit was rumpled, yes," | | 11 | "Harlow's voice was flat" | | 12 | "She pointed with a gloved" | | 13 | "It was clenched into a" | | 14 | "Something was clutched inside." | | 15 | "Davies said, missing her point" | | 16 | "Harlow knelt, the joints in" | | 17 | "The concrete was cold through" | | 18 | "She leaned in, her sharp" | | 19 | "The victim's shirt collar was" |
| | ratio | 0.856 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn ignored the uniformed constables who parted before her like a nervous sea, their faces pale in the harsh glare of the portable floodlight…" |
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| 17.86% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 3 | | matches | | 0 | "Davies said, a note of unease finally creeping into his voice" | | 1 | "She walked, her eyes tracing the patterns in the dust" | | 2 | "Davies suggested, but his voice lacked conviction" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Davies suggested (suggest)" |
| | dialogueSentences | 33 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.333 | | effectiveRatio | 0.061 | |