| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 27 | | tagDensity | 0.407 | | leniency | 0.815 | | rawRatio | 0.091 | | effectiveRatio | 0.074 | |
| 88.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1283 | | totalAiIsmAdverbs | 3 | | 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) | |
| 37.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1283 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "glint" | | 1 | "familiar" | | 2 | "beacon" | | 3 | "maw" | | 4 | "flickered" | | 5 | "electric" | | 6 | "pulse" | | 7 | "cacophony" | | 8 | "velvet" | | 9 | "vibrated" | | 10 | "fleeting" | | 11 | "lurch" | | 12 | "lurching" | | 13 | "echoing" | | 14 | "whisper" | | 15 | "dancing" |
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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 | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 103 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1280 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1022 | | uniqueNames | 16 | | maxNameDensity | 1.27 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 13 | | Dean | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | London | 1 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Met | 1 | | Police | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 7 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Police" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Soho" | | 3 | "London" |
| | globalScore | 0.864 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 43.75% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.563 | | wordCount | 1280 | | matches | | 0 | "not electric, but the uneven pulse of torches" | | 1 | "not a voice, but a recording played backwards, a buzz of static" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 30.48 | | std | 21.71 | | cv | 0.712 | | sampleLengths | | 0 | 70 | | 1 | 56 | | 2 | 51 | | 3 | 56 | | 4 | 7 | | 5 | 67 | | 6 | 59 | | 7 | 3 | | 8 | 62 | | 9 | 8 | | 10 | 3 | | 11 | 61 | | 12 | 59 | | 13 | 4 | | 14 | 23 | | 15 | 48 | | 16 | 6 | | 17 | 28 | | 18 | 5 | | 19 | 29 | | 20 | 7 | | 21 | 26 | | 22 | 4 | | 23 | 29 | | 24 | 7 | | 25 | 25 | | 26 | 24 | | 27 | 3 | | 28 | 46 | | 29 | 30 | | 30 | 3 | | 31 | 33 | | 32 | 43 | | 33 | 41 | | 34 | 4 | | 35 | 36 | | 36 | 45 | | 37 | 5 | | 38 | 56 | | 39 | 52 | | 40 | 35 | | 41 | 21 |
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| 95.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 103 | | matches | | 0 | "was gone" | | 1 | "was gone" | | 2 | "were hidden" |
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| 35.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 162 | | matches | | 0 | "wasn't heading" | | 1 | "was circling" | | 2 | "was heading" | | 3 | "was indeed moving" |
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| 96.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 122 | | ratio | 0.016 | | matches | | 0 | "Ahead, the fleeing figure in the dark hoodie—a ghost in the downpour—vaulted a overflowing bin with fluid ease." | | 1 | "Beyond it, light flickered—not electric, but the uneven pulse of torches and lanterns." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 167 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.023952095808383235 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 10.49 | | std | 6.4 | | cv | 0.61 | | sampleLengths | | 0 | 8 | | 1 | 19 | | 2 | 18 | | 3 | 17 | | 4 | 5 | | 5 | 3 | | 6 | 22 | | 7 | 11 | | 8 | 2 | | 9 | 11 | | 10 | 10 | | 11 | 8 | | 12 | 20 | | 13 | 11 | | 14 | 3 | | 15 | 3 | | 16 | 6 | | 17 | 11 | | 18 | 6 | | 19 | 10 | | 20 | 4 | | 21 | 1 | | 22 | 13 | | 23 | 7 | | 24 | 4 | | 25 | 7 | | 26 | 22 | | 27 | 17 | | 28 | 16 | | 29 | 12 | | 30 | 9 | | 31 | 8 | | 32 | 13 | | 33 | 14 | | 34 | 15 | | 35 | 3 | | 36 | 18 | | 37 | 17 | | 38 | 9 | | 39 | 18 | | 40 | 8 | | 41 | 3 | | 42 | 7 | | 43 | 15 | | 44 | 12 | | 45 | 14 | | 46 | 13 | | 47 | 14 | | 48 | 10 | | 49 | 13 |
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| 54.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.38524590163934425 | | totalSentences | 122 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 95 | | matches | | 0 | "He’d been at" | | 1 | "She’d shown her warrant card," | | 2 | "She sidestepped, her shoulder brushing" | | 3 | "He cut across Dean Street," | | 4 | "She gained two metres." | | 5 | "He wasn't heading away from" | | 6 | "He was circling back." | | 7 | "She descended, one hand skimming" | | 8 | "It was a cacophony of" | | 9 | "She pushed into the crowd," | | 10 | "Her target’s dark hoodie was" | | 11 | "She lost sight, gained it," | | 12 | "He looked out of place" | | 13 | "He tucked the vial into" | | 14 | "He didn’t phrase it as" | | 15 | "He gestured vaguely at the" | | 16 | "His voice dropped, losing its" | | 17 | "He looked over her shoulder," | | 18 | "It wasn’t her suspect." | | 19 | "It was something else wearing" |
| | ratio | 0.274 | |
| 23.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 95 | | matches | | 0 | "The rain turned the alley" | | 1 | "Quinn followed, her jaw set," | | 2 | "This wasn't a suspect anymore." | | 3 | "This was prey." | | 4 | "He’d been at" | | 5 | "Quinn, two stools down, had" | | 6 | "Something small, bone-white, passed with" | | 7 | "She’d shown her warrant card," | | 8 | "The hoodie ducked left, skidding" | | 9 | "Quinn rounded it just as" | | 10 | "She sidestepped, her shoulder brushing" | | 11 | "Amateurs made noise." | | 12 | "Professionals made choices." | | 13 | "This one was somewhere in" | | 14 | "He cut across Dean Street," | | 15 | "Headlights smeared across his reflective" | | 16 | "Quinn’s lungs burned, a familiar" | | 17 | "She gained two metres." | | 18 | "The green neon sign of" | | 19 | "He wasn't heading away from" |
| | ratio | 0.874 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 79.37% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 4 | | matches | | 0 | "She sidestepped, her shoulder brushing the damp brick, never breaking stride." | | 1 | "The hoodie plunged down a steep, narrow staircase between a kebab shop and a launderette, a staircase that shouldn’t have been there." | | 2 | "The entrance was a black maw, exhaling warm, stale air that smelled of rust and ozone." | | 3 | "A sound came from it, not a voice, but a recording played backwards, a buzz of static and distorted syllables that scraped at the inside of Quinn’s skull." |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "He looked over, his expression tightening" | | 1 | "Tomás murmured, his hand closing around her elbow" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "Tomás murmured (murmur)" |
| | dialogueSentences | 27 | | tagDensity | 0.074 | | leniency | 0.148 | | rawRatio | 0.5 | | effectiveRatio | 0.074 | |