| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1291 | | totalAiIsmAdverbs | 1 | | 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) | |
| 53.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1291 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "fractured" | | 1 | "weight" | | 2 | "vibrated" | | 3 | "echoed" | | 4 | "pulsed" | | 5 | "silence" | | 6 | "flickered" | | 7 | "traced" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "knuckles turned white" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 211 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 211 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 211 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 18 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1291 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 53.18% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1291 | | uniqueNames | 14 | | maxNameDensity | 1.94 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | London | 2 | | Quinn | 1 | | Camden | 2 | | Town | 1 | | Tube | 1 | | Road | 1 | | Veil | 2 | | Market | 1 | | Morris | 6 | | Harlow | 25 | | Saint | 1 | | Christopher | 1 | | Thames | 1 | | You | 7 |
| | persons | | 0 | "Quinn" | | 1 | "Veil" | | 2 | "Market" | | 3 | "Morris" | | 4 | "Harlow" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "You" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "Town" | | 3 | "Road" | | 4 | "Thames" |
| | globalScore | 0.532 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 1 | | matches | | 0 | "graffiti that seemed to shift in the periphery of her vision" |
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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 | 1291 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 211 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 16.55 | | std | 15.84 | | cv | 0.957 | | sampleLengths | | 0 | 63 | | 1 | 44 | | 2 | 1 | | 3 | 9 | | 4 | 46 | | 5 | 19 | | 6 | 59 | | 7 | 25 | | 8 | 11 | | 9 | 2 | | 10 | 15 | | 11 | 62 | | 12 | 26 | | 13 | 5 | | 14 | 7 | | 15 | 16 | | 16 | 6 | | 17 | 30 | | 18 | 13 | | 19 | 39 | | 20 | 9 | | 21 | 3 | | 22 | 36 | | 23 | 3 | | 24 | 27 | | 25 | 5 | | 26 | 41 | | 27 | 14 | | 28 | 4 | | 29 | 12 | | 30 | 37 | | 31 | 5 | | 32 | 49 | | 33 | 35 | | 34 | 3 | | 35 | 8 | | 36 | 3 | | 37 | 13 | | 38 | 9 | | 39 | 12 | | 40 | 4 | | 41 | 15 | | 42 | 9 | | 43 | 12 | | 44 | 4 | | 45 | 9 | | 46 | 7 | | 47 | 39 | | 48 | 4 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 211 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 211 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1291 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.013942680092951201 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0038729666924864447 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 211 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 211 | | mean | 6.12 | | std | 3.37 | | cv | 0.551 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 14 | | 3 | 4 | | 4 | 13 | | 5 | 4 | | 6 | 9 | | 7 | 5 | | 8 | 10 | | 9 | 11 | | 10 | 5 | | 11 | 1 | | 12 | 9 | | 13 | 5 | | 14 | 12 | | 15 | 14 | | 16 | 15 | | 17 | 4 | | 18 | 6 | | 19 | 9 | | 20 | 4 | | 21 | 13 | | 22 | 5 | | 23 | 18 | | 24 | 4 | | 25 | 15 | | 26 | 17 | | 27 | 2 | | 28 | 6 | | 29 | 4 | | 30 | 7 | | 31 | 1 | | 32 | 1 | | 33 | 3 | | 34 | 10 | | 35 | 2 | | 36 | 5 | | 37 | 4 | | 38 | 14 | | 39 | 3 | | 40 | 5 | | 41 | 7 | | 42 | 10 | | 43 | 5 | | 44 | 6 | | 45 | 3 | | 46 | 11 | | 47 | 5 | | 48 | 10 | | 49 | 5 |
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| 36.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 29 | | diversityRatio | 0.2037914691943128 | | totalSentences | 211 | | uniqueOpeners | 43 | |
| 17.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 196 | | matches | | | ratio | 0.005 | |
| 58.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 79 | | totalSentences | 196 | | matches | | 0 | "Her coat weighed heavy on" | | 1 | "She did not slow." | | 2 | "She checked her wrist." | | 3 | "They scrambled up a rusted" | | 4 | "She reached the landing just" | | 5 | "She pulled herself up and" | | 6 | "She stepped into a tunnel." | | 7 | "They pulled out a small" | | 8 | "She knew this place." | | 9 | "It moved every full moon." | | 10 | "She remembered DS Morris three" | | 11 | "He had chased a lead" | | 12 | "He had not come back." | | 13 | "She knew better." | | 14 | "It landed with a clatter." | | 15 | "You cannot cross without it." | | 16 | "Her boots crunched on the" | | 17 | "She kept her eyes on" | | 18 | "I do not need your" | | 19 | "They backed toward the wooden" |
| | ratio | 0.403 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 185 | | totalSentences | 196 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Harlow Quinn sprinted, her boots" | | 2 | "Her coat weighed heavy on" | | 3 | "She did not slow." | | 4 | "The figure ahead darted between" | | 5 | "She checked her wrist." | | 6 | "The worn leather strap of" | | 7 | "Time mattered less than distance." | | 8 | "The suspect vanished into a" | | 9 | "Harlow turned the corner, her" | | 10 | "Steam rose from her lips." | | 11 | "The shout tore from her" | | 12 | "The figure did not stop." | | 13 | "They scrambled up a rusted" | | 14 | "Harlow followed, her movements precise," | | 15 | "She reached the landing just" | | 16 | "Harlow grabbed the ladder." | | 17 | "She pulled herself up and" | | 18 | "The air changed instantly." | | 19 | "The smell of rain and" |
| | ratio | 0.944 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 196 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow Quinn sprinted, her boots splashing through puddles that reflected the fractured light of streetlamps." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |