| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow agreed softly [softly]" |
| | dialogueSentences | 26 | | tagDensity | 0.423 | | leniency | 0.846 | | rawRatio | 0.091 | | effectiveRatio | 0.077 | |
| 85.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1052 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "carefully" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 33.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1052 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "stark" | | 1 | "profound" | | 2 | "echoing" | | 3 | "perfect" | | 4 | "shimmered" | | 5 | "almost alive" | | 6 | "echoed" | | 7 | "intricate" | | 8 | "etched" | | 9 | "racing" | | 10 | "tracing" | | 11 | "shattered" | | 12 | "whisper" | | 13 | "oppressive" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 10 | | totalWords | 1047 | | ratio | 0.01 | | matches | | 0 | "‘It’s not a person, Harlow. The footprints… they’re all wrong…’" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.83% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 845 | | uniqueNames | 8 | | maxNameDensity | 1.18 | | worstName | "Evans" | | maxWindowNameDensity | 2 | | worstWindowName | "Evans" | | discoveredNames | | Harlow | 5 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Victorian | 1 | | Evans | 10 | | Morris | 2 | | Leo | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Evans" | | 3 | "Morris" | | 4 | "Leo" |
| | places | (empty) | | globalScore | 0.908 | | windowScore | 1 | |
| 27.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 3 | | matches | | 0 | "as if reaching for something" | | 1 | "seemed straightforward too, until it wasn’t" | | 2 | "felt like bone" |
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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 | 1047 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 31.73 | | std | 22.72 | | cv | 0.716 | | sampleLengths | | 0 | 30 | | 1 | 75 | | 2 | 26 | | 3 | 3 | | 4 | 3 | | 5 | 52 | | 6 | 23 | | 7 | 56 | | 8 | 13 | | 9 | 28 | | 10 | 77 | | 11 | 9 | | 12 | 21 | | 13 | 30 | | 14 | 56 | | 15 | 52 | | 16 | 38 | | 17 | 23 | | 18 | 7 | | 19 | 14 | | 20 | 11 | | 21 | 6 | | 22 | 56 | | 23 | 43 | | 24 | 14 | | 25 | 60 | | 26 | 10 | | 27 | 52 | | 28 | 82 | | 29 | 9 | | 30 | 31 | | 31 | 11 | | 32 | 26 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 82 | | matches | | 0 | "was frozen" | | 1 | "was curled" | | 2 | "were etched" | | 3 | "was disturbed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 143 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 98 | | ratio | 0.01 | | matches | | 0 | "She crouched, ignoring the protest from her knee—a souvenir from a chase three years back." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 650 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.023076923076923078 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009230769230769232 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 10.68 | | std | 6.6 | | cv | 0.617 | | sampleLengths | | 0 | 4 | | 1 | 4 | | 2 | 22 | | 3 | 27 | | 4 | 11 | | 5 | 24 | | 6 | 13 | | 7 | 20 | | 8 | 6 | | 9 | 3 | | 10 | 3 | | 11 | 17 | | 12 | 11 | | 13 | 3 | | 14 | 21 | | 15 | 8 | | 16 | 15 | | 17 | 8 | | 18 | 10 | | 19 | 25 | | 20 | 13 | | 21 | 13 | | 22 | 28 | | 23 | 4 | | 24 | 15 | | 25 | 10 | | 26 | 16 | | 27 | 1 | | 28 | 5 | | 29 | 17 | | 30 | 9 | | 31 | 9 | | 32 | 7 | | 33 | 14 | | 34 | 18 | | 35 | 7 | | 36 | 5 | | 37 | 12 | | 38 | 18 | | 39 | 26 | | 40 | 15 | | 41 | 1 | | 42 | 15 | | 43 | 10 | | 44 | 11 | | 45 | 9 | | 46 | 2 | | 47 | 18 | | 48 | 9 | | 49 | 10 |
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| 67.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.42857142857142855 | | totalSentences | 98 | | uniqueOpeners | 42 | |
| 88.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 75 | | matches | | 0 | "Then she saw it." | | 1 | "Just outside the circle of" |
| | ratio | 0.027 | |
| 70.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 75 | | matches | | 0 | "She found DS Evans kneeling" | | 1 | "His face was frozen in" | | 2 | "Her sharp jaw was set," | | 3 | "She noted the positioning of" | | 4 | "she asked, her voice echoing" | | 5 | "She crouched, ignoring the protest" | | 6 | "She shone her torch on" | | 7 | "It shimmered faintly, catching the" | | 8 | "she asked, pointing to the" | | 9 | "She leaned closer, careful not" | | 10 | "He was a good detective," | | 11 | "Her eyes caught on a" | | 12 | "She used a pen to" | | 13 | "It was a token, carved" | | 14 | "she asked, holding it up" | | 15 | "she said, her voice low" | | 16 | "She didn't answer." | | 17 | "Her mind was racing, piecing" | | 18 | "She stood, her torch beam" | | 19 | "They came in from the" |
| | ratio | 0.373 | |
| 46.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 75 | | matches | | 0 | "The stench hit first." | | 1 | "Something metallic and sweet, like" | | 2 | "Detective Harlow Quinn stepped over" | | 3 | "The abandoned Tube station beneath" | | 4 | "Arches curved into darkness, their" | | 5 | "The air hummed with a" | | 6 | "A uniformed officer gestured with" | | 7 | "She found DS Evans kneeling" | | 8 | "A young man, late twenties," | | 9 | "His face was frozen in" | | 10 | "Evans didn't look up from" | | 11 | "Harlow circled the body, her" | | 12 | "Her sharp jaw was set," | | 13 | "She noted the positioning of" | | 14 | "The metallic sweetness was stronger" | | 15 | "she asked, her voice echoing" | | 16 | "Harlow agreed softly" | | 17 | "She crouched, ignoring the protest" | | 18 | "She shone her torch on" | | 19 | "The dust was thick, undisturbed" |
| | ratio | 0.827 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 6 | | matches | | 0 | "Detective Harlow Quinn stepped over the rusted chain that marked the boundary of the crime scene, her worn leather boots crunching on gravel and discarded ceram…" | | 1 | "Arches curved into darkness, their Victorian brickwork scarred with graffiti and patches of lurid moss that glowed faintly in the beam of her torch." | | 2 | "The air hummed with a low, sub-audible frequency that made her teeth ache." | | 3 | "She noted the positioning of the feet, the way one hand was curled into a fist, the other splayed open as if reaching for something." | | 4 | "Three-toed, with a long, dragging claw mark that scored the concrete beneath the dust." | | 5 | "The memory of Morris’s last call crackled in her memory, his voice frantic, describing things that couldn’t be real." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 4 | | matches | | 0 | "she asked, her voice echoing slightly in the cavernous space" | | 1 | "she said, her voice low" | | 2 | "Harlow said, her voice barely a whisper" | | 3 | "she said, her tone sharp, cutting through Evans’s scepticism" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "Harlow agreed softly (agree)" |
| | dialogueSentences | 26 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.125 | | effectiveRatio | 0.077 | |