| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.176 | | leniency | 0.353 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 878 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 71.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 878 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "familiar" | | 1 | "silence" | | 2 | "depths" | | 3 | "glinting" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 27 | | totalWords | 878 | | ratio | 0.031 | | matches | | 0 | "Eighteen years on the force, Harlow. Eighteen years of following evidence through doors you shouldn't open." | | 1 | "This is how Morris died. Following something strange into somewhere dark." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 782 | | uniqueNames | 14 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Webb" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 3 | | Camden | 1 | | Town | 1 | | Webb | 6 | | Glock | 1 | | Tube | 2 | | London | 1 | | Harlow | 1 | | Morris | 2 | | Greek | 1 | | Latin | 1 | | Quinn | 7 |
| | persons | | 0 | "Nest" | | 1 | "Webb" | | 2 | "Tube" | | 3 | "Morris" | | 4 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Camden" | | 3 | "Town" | | 4 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 98.98% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a disused platform" |
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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 | 878 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 20.9 | | std | 15.3 | | cv | 0.732 | | sampleLengths | | 0 | 19 | | 1 | 45 | | 2 | 3 | | 3 | 6 | | 4 | 50 | | 5 | 47 | | 6 | 21 | | 7 | 10 | | 8 | 12 | | 9 | 18 | | 10 | 31 | | 11 | 36 | | 12 | 1 | | 13 | 4 | | 14 | 48 | | 15 | 25 | | 16 | 30 | | 17 | 16 | | 18 | 2 | | 19 | 34 | | 20 | 34 | | 21 | 17 | | 22 | 11 | | 23 | 5 | | 24 | 36 | | 25 | 27 | | 26 | 21 | | 27 | 5 | | 28 | 59 | | 29 | 16 | | 30 | 31 | | 31 | 2 | | 32 | 4 | | 33 | 11 | | 34 | 26 | | 35 | 6 | | 36 | 28 | | 37 | 40 | | 38 | 8 | | 39 | 15 | | 40 | 4 | | 41 | 14 |
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| 89.13% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 87 | | matches | | 0 | "been locked" | | 1 | "been transformed" | | 2 | "were filled" | | 3 | "was gone" |
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| 55.07% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 138 | | matches | | 0 | "was already hauling" | | 1 | "were drawing" | | 2 | "was still carrying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 155 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.01935483870967742 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0064516129032258064 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 8.61 | | std | 6.34 | | cv | 0.737 | | sampleLengths | | 0 | 19 | | 1 | 12 | | 2 | 15 | | 3 | 18 | | 4 | 3 | | 5 | 3 | | 6 | 3 | | 7 | 2 | | 8 | 23 | | 9 | 2 | | 10 | 23 | | 11 | 28 | | 12 | 9 | | 13 | 10 | | 14 | 4 | | 15 | 2 | | 16 | 4 | | 17 | 11 | | 18 | 10 | | 19 | 6 | | 20 | 6 | | 21 | 18 | | 22 | 13 | | 23 | 6 | | 24 | 12 | | 25 | 14 | | 26 | 22 | | 27 | 1 | | 28 | 4 | | 29 | 11 | | 30 | 11 | | 31 | 2 | | 32 | 5 | | 33 | 19 | | 34 | 5 | | 35 | 9 | | 36 | 2 | | 37 | 5 | | 38 | 4 | | 39 | 4 | | 40 | 3 | | 41 | 23 | | 42 | 6 | | 43 | 10 | | 44 | 2 | | 45 | 12 | | 46 | 4 | | 47 | 5 | | 48 | 13 | | 49 | 21 |
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| 83.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5490196078431373 | | totalSentences | 102 | | uniqueOpeners | 56 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "Just a blackened handle worn" | | 1 | "Still delivering his message to" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 72 | | matches | | 0 | "He moved through the narrow" | | 1 | "He didn't stop." | | 2 | "They never stopped." | | 3 | "She'd seen it tucked under" | | 4 | "She knew that face." | | 5 | "He laughed, a wet, breathless" | | 6 | "It swung open, hinges freshly" | | 7 | "He dropped into darkness." | | 8 | "She checked her watch." | | 9 | "She pushed the door open." | | 10 | "He'd come this way." | | 11 | "She pushed through the beads." | | 12 | "she said, her voice sliding" | | 13 | "Her nails were long, filed" |
| | ratio | 0.194 | |
| 98.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 72 | | matches | | 0 | "The envelope slipped from the" | | 1 | "Quinn gained two strides, her" | | 2 | "The man ahead had a" | | 3 | "He moved through the narrow" | | 4 | "He didn't stop." | | 5 | "They never stopped." | | 6 | "She'd seen it tucked under" | | 7 | "The same seal she'd found" | | 8 | "The chase led north, away" | | 9 | "The man glanced back, his" | | 10 | "She knew that face." | | 11 | "He laughed, a wet, breathless" | | 12 | "Webb turned sharply, disappearing through" | | 13 | "It swung open, hinges freshly" | | 14 | "Quinn followed, her hand moving" | | 15 | "The gate led to a" | | 16 | "He dropped into darkness." | | 17 | "Quinn reached the hatch, her" | | 18 | "Steel rungs led down to" | | 19 | "The air rising from below" |
| | ratio | 0.722 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 72 | | matches | | 0 | "Because the last person I" | | 1 | "By the time her feet" |
| | ratio | 0.028 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "The envelope slipped from the suspect's hand, spinning end over end through the rain before slapping against wet asphalt." | | 1 | "Something older, scratched deep into the brick by someone who knew exactly what they were drawing." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice sliding like oil over gravel" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |