| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 55 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.067 | | effectiveRatio | 0.036 | |
| 91.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1175 | | 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) | |
| 82.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1175 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "warmth" | | 1 | "etched" | | 2 | "trembled" | | 3 | "silence" |
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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 | 1 | | narrationSentences | 70 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1175 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 704 | | uniqueNames | 7 | | maxNameDensity | 1.56 | | worstName | "Ferro" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Ferro" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Tube | 1 | | Camden | 1 | | Ferro | 11 | | Arabic | 1 | | Voss | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Ferro" | | 4 | "Voss" |
| | places | (empty) | | globalScore | 0.719 | | windowScore | 0.833 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1175 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 110 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 21.36 | | std | 19.8 | | cv | 0.927 | | sampleLengths | | 0 | 14 | | 1 | 59 | | 2 | 3 | | 3 | 26 | | 4 | 5 | | 5 | 5 | | 6 | 62 | | 7 | 3 | | 8 | 9 | | 9 | 3 | | 10 | 37 | | 11 | 56 | | 12 | 2 | | 13 | 22 | | 14 | 68 | | 15 | 9 | | 16 | 23 | | 17 | 4 | | 18 | 4 | | 19 | 51 | | 20 | 52 | | 21 | 8 | | 22 | 23 | | 23 | 6 | | 24 | 9 | | 25 | 39 | | 26 | 8 | | 27 | 52 | | 28 | 1 | | 29 | 53 | | 30 | 6 | | 31 | 35 | | 32 | 23 | | 33 | 2 | | 34 | 31 | | 35 | 19 | | 36 | 6 | | 37 | 4 | | 38 | 26 | | 39 | 34 | | 40 | 3 | | 41 | 4 | | 42 | 2 | | 43 | 39 | | 44 | 11 | | 45 | 1 | | 46 | 14 | | 47 | 1 | | 48 | 60 | | 49 | 39 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 70 | | matches | | 0 | "been sealed" | | 1 | "been given" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 135 | | matches | | 0 | "were dying" | | 1 | "was still staring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 110 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 705 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03404255319148936 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005673758865248227 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 10.68 | | std | 9.7 | | cv | 0.909 | | sampleLengths | | 0 | 14 | | 1 | 16 | | 2 | 18 | | 3 | 3 | | 4 | 4 | | 5 | 18 | | 6 | 3 | | 7 | 22 | | 8 | 4 | | 9 | 5 | | 10 | 5 | | 11 | 4 | | 12 | 34 | | 13 | 2 | | 14 | 10 | | 15 | 12 | | 16 | 3 | | 17 | 9 | | 18 | 3 | | 19 | 10 | | 20 | 27 | | 21 | 15 | | 22 | 8 | | 23 | 33 | | 24 | 2 | | 25 | 16 | | 26 | 6 | | 27 | 6 | | 28 | 19 | | 29 | 3 | | 30 | 1 | | 31 | 1 | | 32 | 19 | | 33 | 19 | | 34 | 5 | | 35 | 4 | | 36 | 11 | | 37 | 12 | | 38 | 4 | | 39 | 4 | | 40 | 10 | | 41 | 6 | | 42 | 5 | | 43 | 6 | | 44 | 24 | | 45 | 5 | | 46 | 15 | | 47 | 13 | | 48 | 19 | | 49 | 5 |
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| 86.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5636363636363636 | | totalSentences | 110 | | uniqueOpeners | 62 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 59 | | matches | | 0 | "Somewhere down that throat of" |
| | ratio | 0.017 | |
| 84.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 59 | | matches | | 0 | "She took the torch." | | 1 | "She circled the body once," | | 2 | "He held up an evidence" | | 3 | "It didn't point north, didn't" | | 4 | "It pointed along the tracks," | | 5 | "She didn't touch it" | | 6 | "She straightened and swept the" | | 7 | "She photographed it, then leaned" | | 8 | "She'd told herself it was" | | 9 | "It now pointed past her," | | 10 | "She heard her own voice" | | 11 | "She turned back to the" | | 12 | "She photographed the mark again," | | 13 | "It sat against the eardrums." | | 14 | "she said, to break it" | | 15 | "She didn't answer." | | 16 | "She watched his shoulders climb" | | 17 | "She took one last look" | | 18 | "He got on his radio" | | 19 | "She was still staring at" |
| | ratio | 0.339 | |
| 27.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 59 | | matches | | 0 | "Detective Harlow Quinn crouched at" | | 1 | "The abandoned Tube station beneath" | | 2 | "A swept floor meant foot" | | 3 | "DS Ferro dropped beside her," | | 4 | "She took the torch." | | 5 | "The body lay twisted between" | | 6 | "People who knew they were" | | 7 | "People who looked astonished had" | | 8 | "Ferro grinned, all teeth, no" | | 9 | "Quinn climbed down onto the" | | 10 | "She circled the body once," | | 11 | "The sweep pattern in the" | | 12 | "Ferro read from his notebook" | | 13 | "He held up an evidence" | | 14 | "The needle spun." | | 15 | "It didn't point north, didn't" | | 16 | "It pointed along the tracks," | | 17 | "She didn't touch it" | | 18 | "She straightened and swept the" | | 19 | "There, at shoulder height, a" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 2 | | matches | | 0 | "A spiral, drawn in one unbroken stroke, and inside it a symbol that snagged her memory the way a nail snags a coat sleeve." | | 1 | "The compass needle stopped with it, and pointed straight at the mark on the wall, dead steady, as if it had finally been given permission to tell the truth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 55 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.2 | | effectiveRatio | 0.036 | |