| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1287 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 84.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1287 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "mechanical" | | 2 | "measured" |
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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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1287 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.89% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1178 | | uniqueNames | 8 | | maxNameDensity | 2.12 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 25 | | Tomás | 1 | | Herrera | 17 | | Raven | 1 | | Nest | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" |
| | places | | | globalScore | 0.439 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | 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 | 1287 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 21.81 | | std | 20.81 | | cv | 0.954 | | sampleLengths | | 0 | 45 | | 1 | 26 | | 2 | 5 | | 3 | 5 | | 4 | 2 | | 5 | 40 | | 6 | 7 | | 7 | 13 | | 8 | 57 | | 9 | 55 | | 10 | 8 | | 11 | 1 | | 12 | 4 | | 13 | 10 | | 14 | 39 | | 15 | 39 | | 16 | 4 | | 17 | 13 | | 18 | 6 | | 19 | 3 | | 20 | 53 | | 21 | 13 | | 22 | 6 | | 23 | 41 | | 24 | 1 | | 25 | 20 | | 26 | 28 | | 27 | 39 | | 28 | 6 | | 29 | 3 | | 30 | 36 | | 31 | 7 | | 32 | 23 | | 33 | 21 | | 34 | 43 | | 35 | 57 | | 36 | 6 | | 37 | 7 | | 38 | 50 | | 39 | 39 | | 40 | 5 | | 41 | 32 | | 42 | 6 | | 43 | 13 | | 44 | 5 | | 45 | 13 | | 46 | 14 | | 47 | 76 | | 48 | 8 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 193 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1180 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.015254237288135594 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003389830508474576 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 9.33 | | std | 5.51 | | cv | 0.591 | | sampleLengths | | 0 | 12 | | 1 | 22 | | 2 | 11 | | 3 | 10 | | 4 | 16 | | 5 | 5 | | 6 | 5 | | 7 | 2 | | 8 | 12 | | 9 | 6 | | 10 | 22 | | 11 | 7 | | 12 | 3 | | 13 | 7 | | 14 | 3 | | 15 | 19 | | 16 | 14 | | 17 | 14 | | 18 | 10 | | 19 | 11 | | 20 | 6 | | 21 | 14 | | 22 | 5 | | 23 | 13 | | 24 | 6 | | 25 | 8 | | 26 | 1 | | 27 | 4 | | 28 | 8 | | 29 | 2 | | 30 | 11 | | 31 | 14 | | 32 | 14 | | 33 | 10 | | 34 | 15 | | 35 | 3 | | 36 | 11 | | 37 | 4 | | 38 | 8 | | 39 | 5 | | 40 | 6 | | 41 | 3 | | 42 | 8 | | 43 | 14 | | 44 | 8 | | 45 | 23 | | 46 | 2 | | 47 | 11 | | 48 | 6 | | 49 | 14 |
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| 54.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.34057971014492755 | | totalSentences | 138 | | uniqueOpeners | 47 | |
| 57.97% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 115 | | matches | | 0 | "Then she had spotted the" | | 1 | "Then he fed something small" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 115 | | matches | | 0 | "He stopped beneath the awning" | | 1 | "He glanced back." | | 2 | "His wet curls clung to" | | 3 | "She had waited until he" | | 4 | "He tore free and kept" | | 5 | "Her shoes struck broken glass." | | 6 | "His hand went to his" | | 7 | "He had stopped." | | 8 | "He stepped off the pavement" | | 9 | "She caught herself against the" | | 10 | "He grabbed the bars." | | 11 | "He twisted through." | | 12 | "She tried the handle." | | 13 | "It held firm." | | 14 | "She put it into the" | | 15 | "Her radio crackled when she" | | 16 | "She tried again and caught" | | 17 | "She could wait on the" | | 18 | "She still remembered the blank" | | 19 | "She had spent months pulling" |
| | ratio | 0.261 | |
| 64.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 115 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "He stopped beneath the awning" | | 2 | "Quinn lifted her warrant card." | | 3 | "Quinn shoved the card into" | | 4 | "A horn blasted against her" | | 5 | "Herrera cleared a stack of" | | 6 | "He glanced back." | | 7 | "His wet curls clung to" | | 8 | "Herrera had left through the" | | 9 | "She had waited until he" | | 10 | "He tore free and kept" | | 11 | "Quinn took the corner wide," | | 12 | "Her shoes struck broken glass." | | 13 | "His hand went to his" | | 14 | "Herrera pushed through a gap" | | 15 | "Quinn caught the chain at" | | 16 | "The lane ended at a" | | 17 | "Herrera entered the crowd with" | | 18 | "A woman in a clear" | | 19 | "Quinn turned sideways, slipped past" |
| | ratio | 0.791 | |
| 43.48% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 115 | | matches | | 0 | "Now his jacket caught on" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 1 | | matches | | 0 | "The entrance lay behind her, past the man with the knife and the stairwell that had swallowed her radio signal." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.045 | | leniency | 0.091 | | rawRatio | 0 | | effectiveRatio | 0 | |