| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 36 | | adverbTagCount | 2 | | adverbTags | | 0 | "The sergeant smiled thinly [thinly]" | | 1 | "Quinn crouched again [again]" |
| | dialogueSentences | 86 | | tagDensity | 0.419 | | leniency | 0.837 | | rawRatio | 0.056 | | effectiveRatio | 0.047 | |
| 95.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1205 | | totalAiIsmAdverbs | 1 | | found | | 0 | | 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) | |
| 58.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1205 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "etched" | | 1 | "standard" | | 2 | "flicked" | | 3 | "traced" | | 4 | "familiar" | | 5 | "weight" | | 6 | "vibrated" | | 7 | "flickered" | | 8 | "gloom" | | 9 | "flicker" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1205 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 716 | | uniqueNames | 8 | | maxNameDensity | 3.91 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 28 | | Camden | 1 | | Kowalski | 1 | | Eva | 18 | | Morris | 1 | | French | 1 | | Veil | 1 | | Compass | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Kowalski" | | 2 | "Eva" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1205 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 157 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 15.45 | | std | 11.17 | | cv | 0.723 | | sampleLengths | | 0 | 7 | | 1 | 45 | | 2 | 9 | | 3 | 18 | | 4 | 21 | | 5 | 47 | | 6 | 15 | | 7 | 51 | | 8 | 6 | | 9 | 17 | | 10 | 14 | | 11 | 4 | | 12 | 27 | | 13 | 9 | | 14 | 3 | | 15 | 16 | | 16 | 2 | | 17 | 48 | | 18 | 1 | | 19 | 14 | | 20 | 12 | | 21 | 19 | | 22 | 4 | | 23 | 19 | | 24 | 10 | | 25 | 6 | | 26 | 23 | | 27 | 6 | | 28 | 22 | | 29 | 14 | | 30 | 33 | | 31 | 10 | | 32 | 15 | | 33 | 11 | | 34 | 19 | | 35 | 30 | | 36 | 17 | | 37 | 13 | | 38 | 14 | | 39 | 13 | | 40 | 12 | | 41 | 36 | | 42 | 9 | | 43 | 14 | | 44 | 27 | | 45 | 1 | | 46 | 7 | | 47 | 20 | | 48 | 10 | | 49 | 21 |
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| 92.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 107 | | matches | | 0 | "been siphoned" | | 1 | "was dusted" | | 2 | "been scuffed" | | 3 | "been drawn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 157 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 716 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small rectangular green French" |
| | adverbCount | 16 | | adverbRatio | 0.0223463687150838 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009776536312849162 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 7.68 | | std | 5.25 | | cv | 0.685 | | sampleLengths | | 0 | 7 | | 1 | 4 | | 2 | 15 | | 3 | 11 | | 4 | 8 | | 5 | 7 | | 6 | 4 | | 7 | 5 | | 8 | 8 | | 9 | 9 | | 10 | 1 | | 11 | 14 | | 12 | 7 | | 13 | 3 | | 14 | 14 | | 15 | 8 | | 16 | 3 | | 17 | 2 | | 18 | 17 | | 19 | 14 | | 20 | 1 | | 21 | 2 | | 22 | 8 | | 23 | 11 | | 24 | 21 | | 25 | 9 | | 26 | 6 | | 27 | 3 | | 28 | 14 | | 29 | 5 | | 30 | 6 | | 31 | 3 | | 32 | 4 | | 33 | 9 | | 34 | 18 | | 35 | 4 | | 36 | 4 | | 37 | 1 | | 38 | 3 | | 39 | 11 | | 40 | 5 | | 41 | 2 | | 42 | 19 | | 43 | 29 | | 44 | 1 | | 45 | 10 | | 46 | 4 | | 47 | 8 | | 48 | 4 | | 49 | 17 |
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| 65.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4267515923566879 | | totalSentences | 157 | | uniqueOpeners | 67 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 83 | | matches | | 0 | "She pressed the splintered edge" | | 1 | "Her worn leather watch caught" | | 2 | "It weighed more than it" | | 3 | "She clutched a worn leather" | | 4 | "They were fresh, still tacky." | | 5 | "He had not come back." | | 6 | "It vibrated, faintly, like a" | | 7 | "It looked absurdly domestic in" |
| | ratio | 0.096 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 83 | | matches | | 0 | "The bone token cracked in" | | 1 | "Quinn did not flinch." | | 2 | "She pressed the splintered edge" | | 3 | "The sergeant nodded" | | 4 | "Quinn stepped past the tape" | | 5 | "Her worn leather watch caught" | | 6 | "The sergeant pointed to the" | | 7 | "The dead man lay on" | | 8 | "A thin, dark line ran" | | 9 | "The skin around the wound" | | 10 | "The sergeant's voice carried a" | | 11 | "The corridor narrowed to a" | | 12 | "Graffiti covered the walls, fresh" | | 13 | "A small brass compass with" | | 14 | "The needle twitched, then steadied," | | 15 | "The sergeant shrugged." | | 16 | "Quinn picked up the compass." | | 17 | "It weighed more than it" | | 18 | "The needle shivered." | | 19 | "The sergeant smiled thinly" |
| | ratio | 0.952 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 17 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn looked at the broken salt line, at the dead man who had not died the way the evidence suggested, at the wall that should not exist, and at the black that …" |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 36 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva knelt, not touching it" | | 1 | "Quinn said, voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn murmured (murmur)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 86 | | tagDensity | 0.151 | | leniency | 0.302 | | rawRatio | 0.154 | | effectiveRatio | 0.047 | |