| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1031 | | 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) | |
| 41.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1031 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "gleaming" | | 1 | "echo" | | 2 | "depths" | | 3 | "gloom" | | 4 | "velvet" | | 5 | "hulking" | | 6 | "shattered" | | 7 | "jaw clenched" | | 8 | "loomed" | | 9 | "vibrated" | | 10 | "mechanical" | | 11 | "echoed" |
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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 | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "jaw clenched" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 77 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 5 | | totalWords | 1028 | | ratio | 0.005 | | matches | | 0 | "Exit to Platform 2" 2/1/2001, 12:00:00 AM | | 1 | "tick-tick-tick" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 981 | | uniqueNames | 13 | | maxNameDensity | 1.83 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Harlow | 18 | | Quinn | 1 | | Tomás | 1 | | Herrera | 5 | | Northern | 1 | | Line | 1 | | Maglite | 1 | | Morris | 2 | | Tube | 1 | | London | 1 | | Glock | 1 | | Platform | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Line" | | 6 | "Morris" | | 7 | "Glock" |
| | places | | | globalScore | 0.583 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like dry leaves scraping across co" |
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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 | 1028 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.37 | | std | 21.08 | | cv | 0.718 | | sampleLengths | | 0 | 51 | | 1 | 33 | | 2 | 14 | | 3 | 23 | | 4 | 82 | | 5 | 10 | | 6 | 49 | | 7 | 7 | | 8 | 42 | | 9 | 75 | | 10 | 12 | | 11 | 54 | | 12 | 65 | | 13 | 10 | | 14 | 33 | | 15 | 15 | | 16 | 59 | | 17 | 15 | | 18 | 27 | | 19 | 14 | | 20 | 34 | | 21 | 24 | | 22 | 10 | | 23 | 4 | | 24 | 38 | | 25 | 12 | | 26 | 35 | | 27 | 10 | | 28 | 3 | | 29 | 4 | | 30 | 55 | | 31 | 33 | | 32 | 42 | | 33 | 10 | | 34 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 77 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 166 | | matches | | 0 | "was stifling" | | 1 | "was coming" |
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| 39.59% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 83 | | ratio | 0.036 | | matches | | 0 | "The alley dead-ended at a padlocked iron hatch set into the concrete floor—an old access point for the disused Northern Line tunnels." | | 1 | "Her partner's face flashed in her mind—Morris, dead on a cold slab three years ago with no mark on his body, his eyes frozen open in silent shock." | | 2 | "Hawkers shouted in dialects Harlow had never heard in her eighteen years in London—guttural, clicking tongues that made her teeth ache." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 991 | | adjectiveStacks | 1 | | stackExamples | | 0 | "massive, leather-wrapped hand," |
| | adverbCount | 10 | | adverbRatio | 0.010090817356205853 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0030272452068617556 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 12.39 | | std | 6.46 | | cv | 0.521 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 14 | | 3 | 9 | | 4 | 15 | | 5 | 9 | | 6 | 14 | | 7 | 6 | | 8 | 6 | | 9 | 11 | | 10 | 11 | | 11 | 13 | | 12 | 21 | | 13 | 23 | | 14 | 14 | | 15 | 10 | | 16 | 22 | | 17 | 10 | | 18 | 17 | | 19 | 7 | | 20 | 7 | | 21 | 17 | | 22 | 18 | | 23 | 5 | | 24 | 12 | | 25 | 28 | | 26 | 6 | | 27 | 3 | | 28 | 21 | | 29 | 12 | | 30 | 13 | | 31 | 12 | | 32 | 10 | | 33 | 19 | | 34 | 7 | | 35 | 14 | | 36 | 21 | | 37 | 23 | | 38 | 10 | | 39 | 10 | | 40 | 23 | | 41 | 15 | | 42 | 9 | | 43 | 8 | | 44 | 14 | | 45 | 28 | | 46 | 15 | | 47 | 7 | | 48 | 20 | | 49 | 11 |
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| 57.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.37349397590361444 | | totalSentences | 83 | | uniqueOpeners | 31 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | 0 | "Slowly, she raised her left" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 77 | | matches | | 0 | "Her boots splashed through a" | | 1 | "His dark curl-topped head bobbed" | | 2 | "He gripped a leather satchel" | | 3 | "He ducked into a narrow" | | 4 | "Her worn watch scraped against" | | 5 | "He slipped through the gap" | | 6 | "Her partner's face flashed in" | | 7 | "She unclipped her flashlight, held" | | 8 | "She kept her focus on" | | 9 | "He reached into his coat," | | 10 | "He loomed over seven feet" | | 11 | "He extended a massive, leather-wrapped" | | 12 | "She met the iron-masked gaze" | | 13 | "She held it out." | | 14 | "He reached out with two" | | 15 | "She stepped through the archway," |
| | ratio | 0.208 | |
| 18.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 77 | | matches | | 0 | "Rain lashed the cobblestones of" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "Her boots splashed through a" | | 3 | "His dark curl-topped head bobbed" | | 4 | "He gripped a leather satchel" | | 5 | "Harlow's voice cut through the" | | 6 | "The suspect glanced over his" | | 7 | "He ducked into a narrow" | | 8 | "Harlow pressed her hand against" | | 9 | "Her worn watch scraped against" | | 10 | "He slipped through the gap" | | 11 | "The jagged white line of" | | 12 | "Harlow skidded around the corner," | | 13 | "The alley dead-ended at a" | | 14 | "The heavy iron doors swung" | | 15 | "A thick cloud of ozone," | | 16 | "A echo bounced back from" | | 17 | "Harlow reached the lip of" | | 18 | "The beam swallowed the darkness," | | 19 | "Harlow paused at the brink." |
| | ratio | 0.883 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | 0 | "If she went through that" |
| | ratio | 0.013 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 4 | | matches | | 0 | "The stench rising from this hole matched the faint, unnatural perfume that had hung in Morris's apartment the night he died." | | 1 | "Hawkers shouted in dialects Harlow had never heard in her eighteen years in London—guttural, clicking tongues that made her teeth ache." | | 2 | "Stacks of yellowed parchment, jars filled with pale swimming shapes, and wrought-iron cages holding creatures that twitched beneath velvet shrouds littered the …" | | 3 | "A glass jar shattered on the floor, releasing a cloud of luminescent blue dust that hissed against her leather boots." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "a voice hissed (hiss)" | | 1 | "Harlow's voice bellowed (bellow)" |
| | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 1 | | effectiveRatio | 0.364 | |