| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said quietly [quietly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 48 | | tagDensity | 0.438 | | leniency | 0.875 | | rawRatio | 0.095 | | effectiveRatio | 0.083 | |
| 92.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1401 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | 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) | |
| 71.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1401 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "gloom" | | 1 | "weight" | | 2 | "traced" | | 3 | "glint" | | 4 | "etched" | | 5 | "unwavering" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1401 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 830 | | uniqueNames | 7 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Bell" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Tube | 1 | | Camden | 1 | | Sergeant | 1 | | Bell | 13 | | Graffiti | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Sergeant" | | 4 | "Bell" | | 5 | "Graffiti" |
| | places | (empty) | | globalScore | 0.717 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1401 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 30.46 | | std | 22.36 | | cv | 0.734 | | sampleLengths | | 0 | 57 | | 1 | 38 | | 2 | 42 | | 3 | 33 | | 4 | 5 | | 5 | 15 | | 6 | 50 | | 7 | 13 | | 8 | 59 | | 9 | 25 | | 10 | 60 | | 11 | 8 | | 12 | 3 | | 13 | 10 | | 14 | 11 | | 15 | 4 | | 16 | 36 | | 17 | 69 | | 18 | 28 | | 19 | 6 | | 20 | 28 | | 21 | 3 | | 22 | 48 | | 23 | 10 | | 24 | 49 | | 25 | 41 | | 26 | 12 | | 27 | 37 | | 28 | 27 | | 29 | 8 | | 30 | 7 | | 31 | 43 | | 32 | 14 | | 33 | 4 | | 34 | 70 | | 35 | 10 | | 36 | 100 | | 37 | 13 | | 38 | 73 | | 39 | 41 | | 40 | 38 | | 41 | 26 | | 42 | 37 | | 43 | 30 | | 44 | 41 | | 45 | 19 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 93 | | matches | | 0 | "been sealed" | | 1 | "was etched" | | 2 | "were caked" |
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| 67.55% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 151 | | matches | | 0 | "was still pointing" | | 1 | "was, listening" | | 2 | "was missing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 831 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.02286401925391095 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0048134777376654635 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 11.77 | | std | 9.99 | | cv | 0.848 | | sampleLengths | | 0 | 19 | | 1 | 16 | | 2 | 19 | | 3 | 3 | | 4 | 24 | | 5 | 4 | | 6 | 4 | | 7 | 6 | | 8 | 10 | | 9 | 16 | | 10 | 16 | | 11 | 3 | | 12 | 11 | | 13 | 8 | | 14 | 6 | | 15 | 5 | | 16 | 5 | | 17 | 7 | | 18 | 8 | | 19 | 21 | | 20 | 29 | | 21 | 4 | | 22 | 9 | | 23 | 14 | | 24 | 8 | | 25 | 12 | | 26 | 12 | | 27 | 13 | | 28 | 10 | | 29 | 9 | | 30 | 6 | | 31 | 14 | | 32 | 4 | | 33 | 7 | | 34 | 16 | | 35 | 5 | | 36 | 14 | | 37 | 4 | | 38 | 4 | | 39 | 3 | | 40 | 6 | | 41 | 4 | | 42 | 11 | | 43 | 4 | | 44 | 15 | | 45 | 21 | | 46 | 10 | | 47 | 20 | | 48 | 13 | | 49 | 22 |
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| 59.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.40336134453781514 | | totalSentences | 119 | | uniqueOpeners | 48 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Then she put the notebook" |
| | ratio | 0.013 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 78 | | matches | | 0 | "His coat was expensive." | | 1 | "His shoes were polished." | | 2 | "She crouched, careful to keep" | | 3 | "She pulled out her phone" | | 4 | "She crouched again, angling the" | | 5 | "She studied it from where" | | 6 | "It spun in slow, lazy" | | 7 | "she said quietly" | | 8 | "She stood and stepped back" | | 9 | "She followed the beam of" | | 10 | "She could see the victim’s" | | 11 | "she said slowly" | | 12 | "She stopped beside the body" | | 13 | "It pointed, steady and unwavering," | | 14 | "She walked to the wall" | | 15 | "It was fresh." | | 16 | "It had been cut within" | | 17 | "She looked back at the" | | 18 | "She didn’t know what the" | | 19 | "She didn’t know why a" |
| | ratio | 0.333 | |
| 17.69% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 78 | | matches | | 0 | "The tunnel smelled of rust" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The abandoned Tube station beneath" | | 3 | "Somebody always did." | | 4 | "The body lay at the" | | 5 | "His coat was expensive." | | 6 | "His shoes were polished." | | 7 | "That was the first wrong" | | 8 | "Sergeant Bell, a stout man" | | 9 | "Quinn didn’t answer." | | 10 | "She crouched, careful to keep" | | 11 | "The man’s face was intact," | | 12 | "Bell flipped a page in" | | 13 | "Quinn traced the line of" | | 14 | "Bell shifted his weight." | | 15 | "Quinn stood and swept her" | | 16 | "Graffiti layered over faded tile," | | 17 | "A symbol, clean and sharp," | | 18 | "A circle with interlocking lines," | | 19 | "She pulled out her phone" |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "The kind that came from looking at a puzzle and realizing the pieces didn’t fit because she was missing a whole section of the picture." |
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| 77.38% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn walked, careful to keep to the edges" | | 1 | "Quinn stood, her torch sweeping the floor" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.229 | | leniency | 0.458 | | rawRatio | 0 | | effectiveRatio | 0 | |