| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1172 | | 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) | |
| 70.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1172 | | totalAiIsms | 7 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "down her spine" | | 1 | "echoed" | | 2 | "footsteps" | | 3 | "flicked" | | 4 | "weight" | | 5 | "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 | 112 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1172 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 58.30% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1036 | | uniqueNames | 11 | | maxNameDensity | 1.83 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Saint | 2 | | Christopher | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Tomás | 13 | | Quinn | 19 | | East | 1 | | London | 1 | | Spanish | 1 | | Herrera | 1 |
| | persons | | 0 | "Saint" | | 1 | "Christopher" | | 2 | "Tomás" | | 3 | "Quinn" | | 4 | "Herrera" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "East" | | 3 | "London" | | 4 | "Spanish" |
| | globalScore | 0.583 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 1 | | matches | | 0 | "as if deciding whether this interruption would cost her a sale or entertain her for the evening" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1172 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 30.05 | | std | 20.61 | | cv | 0.686 | | sampleLengths | | 0 | 11 | | 1 | 67 | | 2 | 13 | | 3 | 57 | | 4 | 60 | | 5 | 39 | | 6 | 5 | | 7 | 87 | | 8 | 65 | | 9 | 26 | | 10 | 20 | | 11 | 17 | | 12 | 11 | | 13 | 55 | | 14 | 30 | | 15 | 22 | | 16 | 47 | | 17 | 30 | | 18 | 16 | | 19 | 50 | | 20 | 13 | | 21 | 50 | | 22 | 44 | | 23 | 7 | | 24 | 40 | | 25 | 30 | | 26 | 7 | | 27 | 9 | | 28 | 17 | | 29 | 53 | | 30 | 24 | | 31 | 9 | | 32 | 23 | | 33 | 33 | | 34 | 11 | | 35 | 13 | | 36 | 44 | | 37 | 12 | | 38 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 112 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 184 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 120 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1038 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.029865125240847785 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004816955684007707 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 9.77 | | std | 6.79 | | cv | 0.695 | | sampleLengths | | 0 | 11 | | 1 | 17 | | 2 | 16 | | 3 | 17 | | 4 | 7 | | 5 | 1 | | 6 | 9 | | 7 | 13 | | 8 | 7 | | 9 | 8 | | 10 | 19 | | 11 | 4 | | 12 | 9 | | 13 | 10 | | 14 | 2 | | 15 | 8 | | 16 | 16 | | 17 | 18 | | 18 | 6 | | 19 | 10 | | 20 | 4 | | 21 | 2 | | 22 | 13 | | 23 | 14 | | 24 | 6 | | 25 | 5 | | 26 | 16 | | 27 | 15 | | 28 | 14 | | 29 | 19 | | 30 | 23 | | 31 | 13 | | 32 | 5 | | 33 | 3 | | 34 | 25 | | 35 | 3 | | 36 | 16 | | 37 | 3 | | 38 | 23 | | 39 | 7 | | 40 | 8 | | 41 | 5 | | 42 | 5 | | 43 | 12 | | 44 | 4 | | 45 | 7 | | 46 | 19 | | 47 | 10 | | 48 | 7 | | 49 | 9 |
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| 43.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.30833333333333335 | | totalSentences | 120 | | uniqueOpeners | 37 | |
| 32.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 104 | | matches | | 0 | "Then he turned, saw her" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 104 | | matches | | 0 | "She’d known the medallion would" | | 1 | "He ducked into an alley" | | 2 | "She’d chased him from the" | | 3 | "She pulled her coat tighter" | | 4 | "Her boots echoed on metal" | | 5 | "She stepped out, shoulders squared," | | 6 | "His accent flattened the vowels" | | 7 | "She reached into her pocket," | | 8 | "She turned slightly, keeping everyone" | | 9 | "Her fingers brushed the leather" | | 10 | "She caught the name Herrera," | | 11 | "She could push past one" | | 12 | "She could retreat and lose" | | 13 | "She could stand here and" | | 14 | "Her phone now showed a" | | 15 | "She thumbed it on anyway" | | 16 | "She met the taller man’s" | | 17 | "He looked past her to" | | 18 | "He raised his hand, the" | | 19 | "She heard water dripping onto" |
| | ratio | 0.26 | |
| 3.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 104 | | matches | | 0 | "The suspect bolted left through" | | 1 | "Quinn shoved past a stumbling" | | 2 | "The figure ahead cut under" | | 3 | "She’d known the medallion would" | | 4 | "He ducked into an alley" | | 5 | "Quinn followed, shoes sliding on" | | 6 | "The air smelled of rotting" | | 7 | "Tomás glanced back once, expression" | | 8 | "The opening gaped black." | | 9 | "Quinn reached the edge as" | | 10 | "The Saint Christopher glinted once" | | 11 | "Stairs hewn into brick dropped" | | 12 | "Music thumped from somewhere below," | | 13 | "She’d chased him from the" | | 14 | "The market moved every full" | | 15 | "Tonight the moon sat full" | | 16 | "Quinn checked her phone." | | 17 | "She pulled her coat tighter" | | 18 | "The air changed at once," | | 19 | "Her boots echoed on metal" |
| | ratio | 0.913 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "Stalls crowded the old tracks, canvas awnings sagging under rainwater that leaked through cracks above." | | 1 | "Two men stepped from either side of the next stall, both broader than her, both smiling as if the moment had been rehearsed." | | 2 | "The woman who had closed the stall watched Quinn with patient interest, as if deciding whether this interruption would cost her a sale or entertain her for the …" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, voice steady," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |