| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 551 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "reluctantly" | | count | 1 |
| | 1 | |
| | 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) | |
| 63.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 551 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "flicked" | | 1 | "echoing" | | 2 | "footsteps" | | 3 | "scanning" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 56 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 542 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.02% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 484 | | uniqueNames | 10 | | maxNameDensity | 1.24 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 6 | | Dean | 1 | | Street | 1 | | Tube | 1 | | Londoners | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Tomás" |
| | places | | | globalScore | 0.88 | | windowScore | 1 | |
| 85.90% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a human finger bone" |
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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 | 542 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 18.07 | | std | 16.35 | | cv | 0.905 | | sampleLengths | | 0 | 54 | | 1 | 3 | | 2 | 61 | | 3 | 38 | | 4 | 36 | | 5 | 20 | | 6 | 2 | | 7 | 43 | | 8 | 8 | | 9 | 42 | | 10 | 6 | | 11 | 6 | | 12 | 37 | | 13 | 2 | | 14 | 12 | | 15 | 9 | | 16 | 10 | | 17 | 12 | | 18 | 23 | | 19 | 4 | | 20 | 23 | | 21 | 22 | | 22 | 11 | | 23 | 14 | | 24 | 3 | | 25 | 4 | | 26 | 21 | | 27 | 7 | | 28 | 6 | | 29 | 3 |
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| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 56 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 92 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 64 | | ratio | 0.063 | | matches | | 0 | "The suspect—hood up, shoulders hunched—ducked into an alleyway." | | 1 | "The stairs opened into a cavernous space—an abandoned Tube station, its tiled walls cracked and stained." | | 2 | "She knew him from the files—former paramedic, suspected of supplying underground clinics." | | 3 | "The seated figure laughed—a wet, rattling sound." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 493 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.02231237322515213 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.006085192697768763 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 8.47 | | std | 4.9 | | cv | 0.579 | | sampleLengths | | 0 | 18 | | 1 | 24 | | 2 | 8 | | 3 | 4 | | 4 | 3 | | 5 | 8 | | 6 | 17 | | 7 | 3 | | 8 | 21 | | 9 | 12 | | 10 | 13 | | 11 | 9 | | 12 | 10 | | 13 | 6 | | 14 | 3 | | 15 | 10 | | 16 | 9 | | 17 | 14 | | 18 | 2 | | 19 | 5 | | 20 | 13 | | 21 | 2 | | 22 | 16 | | 23 | 13 | | 24 | 14 | | 25 | 8 | | 26 | 10 | | 27 | 7 | | 28 | 11 | | 29 | 14 | | 30 | 6 | | 31 | 6 | | 32 | 6 | | 33 | 13 | | 34 | 10 | | 35 | 8 | | 36 | 2 | | 37 | 12 | | 38 | 5 | | 39 | 4 | | 40 | 2 | | 41 | 8 | | 42 | 12 | | 43 | 14 | | 44 | 9 | | 45 | 4 | | 46 | 7 | | 47 | 3 | | 48 | 10 | | 49 | 3 |
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| 65.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.421875 | | totalSentences | 64 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 50 | | matches | | 0 | "She followed without hesitation." | | 1 | "Her voice barely carried over" | | 2 | "She grabbed the edge of" | | 3 | "She drew her sidearm, flicked" | | 4 | "She moved fast, pistol raised," | | 5 | "She moved forward, scanning the" | | 6 | "She knew him from the" | | 7 | "she said, low" | | 8 | "He studied her, then jerked" | | 9 | "She didn’t thank him." | | 10 | "She pushed through." | | 11 | "It wasn’t a man." | | 12 | "Its eyes reflected the lantern" | | 13 | "Her hand went to her" |
| | ratio | 0.28 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 50 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn sprinted after" | | 2 | "The suspect—hood up, shoulders hunched—ducked" | | 3 | "She followed without hesitation." | | 4 | "Her voice barely carried over" | | 5 | "The figure vaulted over a" | | 6 | "Quinn didn’t slow." | | 7 | "She grabbed the edge of" | | 8 | "She drew her sidearm, flicked" | | 9 | "The dim interior smelled of" | | 10 | "Water dripped from pipes overhead," | | 11 | "A flickering bulb cast long" | | 12 | "Footsteps pounded ahead." | | 13 | "She moved fast, pistol raised," | | 14 | "The corridor ended at a" | | 15 | "A cold draft rose from" | | 16 | "Protocol said call for backup." | | 17 | "The stairs opened into a" | | 18 | "The air hummed with low" | | 19 | "The suspect was already weaving" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn sprinted after the figure darting between the crowds on Dean Street, her boots splashing through puddles that soaked through her trousers…" | | 1 | "Market stalls lined the platforms, lit by lanterns that cast flickering orange light." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.333 | | effectiveRatio | 0.182 | |