| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.161 | | leniency | 0.323 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1459 | | 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) | |
| 79.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1459 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "footsteps" | | 2 | "velvet" | | 3 | "measured" |
| |
| 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 | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1459 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1114 | | uniqueNames | 12 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 10 | | Tomás | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Tube | 1 | | Nest | 1 | | Rain | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Rain" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" | | 4 | "Nest" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | 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 | 1459 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 136 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 28.61 | | std | 27.05 | | cv | 0.946 | | sampleLengths | | 0 | 81 | | 1 | 34 | | 2 | 2 | | 3 | 21 | | 4 | 3 | | 5 | 11 | | 6 | 4 | | 7 | 89 | | 8 | 60 | | 9 | 8 | | 10 | 48 | | 11 | 32 | | 12 | 2 | | 13 | 41 | | 14 | 13 | | 15 | 30 | | 16 | 9 | | 17 | 13 | | 18 | 55 | | 19 | 57 | | 20 | 41 | | 21 | 3 | | 22 | 4 | | 23 | 10 | | 24 | 73 | | 25 | 33 | | 26 | 3 | | 27 | 7 | | 28 | 54 | | 29 | 92 | | 30 | 3 | | 31 | 42 | | 32 | 1 | | 33 | 13 | | 34 | 13 | | 35 | 35 | | 36 | 3 | | 37 | 3 | | 38 | 50 | | 39 | 49 | | 40 | 10 | | 41 | 56 | | 42 | 21 | | 43 | 7 | | 44 | 106 | | 45 | 46 | | 46 | 8 | | 47 | 3 | | 48 | 24 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 174 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1119 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.01519213583556747 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 10.73 | | std | 8.6 | | cv | 0.801 | | sampleLengths | | 0 | 13 | | 1 | 33 | | 2 | 14 | | 3 | 3 | | 4 | 3 | | 5 | 15 | | 6 | 7 | | 7 | 4 | | 8 | 23 | | 9 | 2 | | 10 | 7 | | 11 | 6 | | 12 | 8 | | 13 | 3 | | 14 | 11 | | 15 | 4 | | 16 | 13 | | 17 | 11 | | 18 | 7 | | 19 | 11 | | 20 | 16 | | 21 | 9 | | 22 | 12 | | 23 | 10 | | 24 | 9 | | 25 | 20 | | 26 | 2 | | 27 | 24 | | 28 | 5 | | 29 | 8 | | 30 | 5 | | 31 | 6 | | 32 | 10 | | 33 | 2 | | 34 | 25 | | 35 | 5 | | 36 | 9 | | 37 | 18 | | 38 | 2 | | 39 | 15 | | 40 | 9 | | 41 | 17 | | 42 | 13 | | 43 | 12 | | 44 | 5 | | 45 | 13 | | 46 | 9 | | 47 | 13 | | 48 | 2 | | 49 | 4 |
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| 71.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4485294117647059 | | totalSentences | 136 | | uniqueOpeners | 61 | |
| 68.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 97 | | matches | | 0 | "Then, under it, the particular" | | 1 | "Then a long exhale of" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 97 | | matches | | 0 | "He ran clean." | | 1 | "He went behind it." | | 2 | "She went in front, close" | | 3 | "He threw a look over" | | 4 | "He broke into a lane" | | 5 | "Her boot found one, rolled," | | 6 | "He took the chain in" | | 7 | "He vanished at the bend." | | 8 | "Her shoulders brushed tile." | | 9 | "She thumbed the pressel, heard" | | 10 | "He backed toward a section" | | 11 | "He pressed the bone into" | | 12 | "He stepped through." | | 13 | "He had counted on the" | | 14 | "His mouth tightened" | | 15 | "She left the blade where" | | 16 | "Her patience did not stay" | | 17 | "She slid the warrant card" |
| | ratio | 0.186 | |
| 63.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 97 | | matches | | 0 | "Rain flattened Camden High Street" | | 1 | "Harlow Quinn ran the crown" | | 2 | "He ran clean." | | 3 | "A man who had spent" | | 4 | "A night bus ploughed the" | | 5 | "He went behind it." | | 6 | "She went in front, close" | | 7 | "He threw a look over" | | 8 | "Rain sat in his short" | | 9 | "The Saint Christopher medallion jumped" | | 10 | "He broke into a lane" | | 11 | "A cook in a doorway" | | 12 | "Herrera's sleeve caught a stack" | | 13 | "Oranges broke loose and ran" | | 14 | "Her boot found one, rolled," | | 15 | "The worn leather watch on" | | 16 | "Water tracked the line of" | | 17 | "The lane pinched between bins" | | 18 | "He took the chain in" | | 19 | "A scar the length of" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.032 | | leniency | 0.065 | | rawRatio | 0 | | effectiveRatio | 0 | |