| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 41 | | tagDensity | 0.415 | | leniency | 0.829 | | rawRatio | 0.059 | | effectiveRatio | 0.049 | |
| 95.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1077 | | totalAiIsmAdverbs | 1 | | found | | 0 | | adverb | "ever so slightly" | | count | 1 |
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| | highlights | | |
| 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) | |
| 72.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1077 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "methodical" | | 1 | "charm" | | 2 | "tracing" | | 3 | "pulsed" | | 4 | "database" | | 5 | "flickered" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | 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 | 1077 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 747 | | uniqueNames | 6 | | maxNameDensity | 1.07 | | worstName | "Owusu" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Owusu" | | discoveredNames | | Detective | 1 | | Sergeant | 1 | | Owusu | 8 | | Tube | 1 | | Morris | 1 | | Quinn | 5 |
| | persons | | 0 | "Sergeant" | | 1 | "Owusu" | | 2 | "Morris" | | 3 | "Quinn" |
| | places | (empty) | | globalScore | 0.965 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 14.30% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.857 | | wordCount | 1077 | | matches | | 0 | "not on the ceiling, not on death, but on the wall" | | 1 | "not on death, but on the wall" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 23.93 | | std | 20.55 | | cv | 0.859 | | sampleLengths | | 0 | 13 | | 1 | 37 | | 2 | 5 | | 3 | 39 | | 4 | 5 | | 5 | 59 | | 6 | 39 | | 7 | 5 | | 8 | 20 | | 9 | 11 | | 10 | 1 | | 11 | 40 | | 12 | 5 | | 13 | 60 | | 14 | 1 | | 15 | 3 | | 16 | 45 | | 17 | 11 | | 18 | 56 | | 19 | 5 | | 20 | 2 | | 21 | 12 | | 22 | 47 | | 23 | 54 | | 24 | 5 | | 25 | 16 | | 26 | 45 | | 27 | 5 | | 28 | 6 | | 29 | 60 | | 30 | 62 | | 31 | 4 | | 32 | 25 | | 33 | 64 | | 34 | 6 | | 35 | 37 | | 36 | 8 | | 37 | 34 | | 38 | 40 | | 39 | 23 | | 40 | 5 | | 41 | 23 | | 42 | 15 | | 43 | 5 | | 44 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 751 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.023968042609853527 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005326231691078562 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 9.88 | | std | 7.48 | | cv | 0.757 | | sampleLengths | | 0 | 13 | | 1 | 8 | | 2 | 22 | | 3 | 4 | | 4 | 3 | | 5 | 5 | | 6 | 23 | | 7 | 16 | | 8 | 5 | | 9 | 20 | | 10 | 8 | | 11 | 10 | | 12 | 21 | | 13 | 3 | | 14 | 10 | | 15 | 1 | | 16 | 7 | | 17 | 2 | | 18 | 2 | | 19 | 14 | | 20 | 5 | | 21 | 8 | | 22 | 12 | | 23 | 11 | | 24 | 1 | | 25 | 4 | | 26 | 9 | | 27 | 2 | | 28 | 10 | | 29 | 15 | | 30 | 5 | | 31 | 8 | | 32 | 43 | | 33 | 9 | | 34 | 1 | | 35 | 3 | | 36 | 7 | | 37 | 15 | | 38 | 23 | | 39 | 7 | | 40 | 4 | | 41 | 9 | | 42 | 10 | | 43 | 3 | | 44 | 8 | | 45 | 7 | | 46 | 8 | | 47 | 11 | | 48 | 5 | | 49 | 2 |
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| 67.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.46788990825688076 | | totalSentences | 109 | | uniqueOpeners | 51 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | 0 | "Just tile, cracked and stained," |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 67 | | matches | | 0 | "His shoes pointed down at" | | 1 | "She circled the body." | | 2 | "She'd seen pain on the" | | 3 | "He gestured vaguely" | | 4 | "She didn't say it unkindly" | | 5 | "She leaned close." | | 6 | "She touched a gloved fingertip" | | 7 | "He held up an evidence" | | 8 | "She pushed the thought down." | | 9 | "She'd built her career on" | | 10 | "She'd never once been wrong" | | 11 | "She walked the length of" | | 12 | "He came over, crouched." | | 13 | "She studied the wall where" | | 14 | "She didn't answer." | | 15 | "She pressed her palm flat" | | 16 | "She was certain of it" | | 17 | "She looked up at the" | | 18 | "Her own voice sounded distant" |
| | ratio | 0.284 | |
| 94.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 67 | | matches | | 0 | "The body hung three feet" | | 1 | "Quinn stopped at the bottom" | | 2 | "This was wrong." | | 3 | "Detective Sergeant Owusu crouched near" | | 4 | "The abandoned Tube platform smelled" | | 5 | "The tiles, once cream, had" | | 6 | "Someone had strung battery lamps" | | 7 | "Quinn walked closer." | | 8 | "The dead man wore a" | | 9 | "His shoes pointed down at" | | 10 | "Owusu stood, knees cracking" | | 11 | "She circled the body." | | 12 | "The face had frozen mid-expression," | | 13 | "She'd seen pain on the" | | 14 | "This looked closer to wonder," | | 15 | "Owusu pulled out his notebook," | | 16 | "He gestured vaguely" | | 17 | "She didn't say it unkindly" | | 18 | "Owusu was good, methodical, but" | | 19 | "The body hung still as" |
| | ratio | 0.731 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "Owusu was good, methodical, but he was reaching for the explanation that let him sleep." |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "Owusu stood, knees cracking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.146 | | leniency | 0.293 | | rawRatio | 0 | | effectiveRatio | 0 | |