| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1358 | | 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) | |
| 44.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1358 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "glint" | | 1 | "structure" | | 2 | "loomed" | | 3 | "gloom" | | 4 | "maw" | | 5 | "tension" | | 6 | "echoed" | | 7 | "vibrated" | | 8 | "chill" | | 9 | "cataloged" | | 10 | "etched" | | 11 | "palpable" | | 12 | "could feel" | | 13 | "pulse" | | 14 | "chaotic" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1358 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1356 | | uniqueNames | 14 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Town | 1 | | Harlow | 1 | | Quinn | 11 | | London | 1 | | Tomás | 3 | | Herrera | 6 | | Soho | 2 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | North | 1 | | Morris | 6 | | Met | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Met" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "London" | | 3 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1358 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 97.59% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 48.5 | | std | 23.84 | | cv | 0.492 | | sampleLengths | | 0 | 97 | | 1 | 14 | | 2 | 65 | | 3 | 63 | | 4 | 11 | | 5 | 48 | | 6 | 28 | | 7 | 61 | | 8 | 109 | | 9 | 65 | | 10 | 60 | | 11 | 49 | | 12 | 25 | | 13 | 61 | | 14 | 38 | | 15 | 13 | | 16 | 64 | | 17 | 48 | | 18 | 24 | | 19 | 42 | | 20 | 44 | | 21 | 65 | | 22 | 54 | | 23 | 40 | | 24 | 16 | | 25 | 30 | | 26 | 43 | | 27 | 81 |
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| 91.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 131 | | matches | | 0 | "been consumed" | | 1 | "were rusted" | | 2 | "been cataloged" | | 3 | "was made" | | 4 | "was gone" | | 5 | "was written" |
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| 82.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 226 | | matches | | 0 | "was running" | | 1 | "was chasing" | | 2 | "wasn't chasing" | | 3 | "was walking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 131 | | ratio | 0.008 | | matches | | 0 | "She wasn't chasing a criminal tonight; she was chasing a ghost." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1359 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.024282560706401765 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0051508462104488595 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 10.37 | | std | 7.07 | | cv | 0.682 | | sampleLengths | | 0 | 17 | | 1 | 6 | | 2 | 31 | | 3 | 18 | | 4 | 25 | | 5 | 14 | | 6 | 3 | | 7 | 4 | | 8 | 19 | | 9 | 7 | | 10 | 24 | | 11 | 3 | | 12 | 5 | | 13 | 11 | | 14 | 3 | | 15 | 1 | | 16 | 11 | | 17 | 37 | | 18 | 11 | | 19 | 15 | | 20 | 19 | | 21 | 5 | | 22 | 9 | | 23 | 16 | | 24 | 4 | | 25 | 8 | | 26 | 2 | | 27 | 9 | | 28 | 18 | | 29 | 5 | | 30 | 13 | | 31 | 14 | | 32 | 5 | | 33 | 15 | | 34 | 15 | | 35 | 5 | | 36 | 24 | | 37 | 18 | | 38 | 11 | | 39 | 16 | | 40 | 5 | | 41 | 6 | | 42 | 21 | | 43 | 22 | | 44 | 2 | | 45 | 1 | | 46 | 8 | | 47 | 4 | | 48 | 3 | | 49 | 6 |
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| 32.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.24427480916030533 | | totalSentences | 131 | | uniqueOpeners | 32 | |
| 54.20% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 123 | | matches | | 0 | "Just the old infrastructure of" | | 1 | "Too many syllables, too many" |
| | ratio | 0.016 | |
| 2.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 67 | | totalSentences | 123 | | matches | | 0 | "Her boots hit the ground" | | 1 | "She wiped it away with" | | 2 | "She checked her wrist." | | 3 | "She couldn't afford to waste" | | 4 | "She had a suspect, a" | | 5 | "He was running." | | 6 | "She rounded the corner, boots" | | 7 | "He moved with a frantic," | | 8 | "She caught a glimpse of" | | 9 | "He turned right, towards the" | | 10 | "He didn't look back." | | 11 | "He vanished into the darkness" | | 12 | "She stood in the mouth" | | 13 | "It felt pressurized, as if" | | 14 | "She looked at her boots," | | 15 | "He had been chasing the" | | 16 | "She wasn't chasing a criminal" | | 17 | "She crouched, checking the fence." | | 18 | "She holstered her service weapon" | | 19 | "Her lungs burned." |
| | ratio | 0.545 | |
| 12.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 123 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn didn't slow" | | 2 | "Her boots hit the ground" | | 3 | "Water plastered her closely cropped" | | 4 | "She wiped it away with" | | 5 | "Quinn didn't hesitate." | | 6 | "She checked her wrist." | | 7 | "The worn leather strap of" | | 8 | "She couldn't afford to waste" | | 9 | "She had a suspect, a" | | 10 | "He was running." | | 11 | "That was the first mistake." | | 12 | "She rounded the corner, boots" | | 13 | "He moved with a frantic," | | 14 | "She caught a glimpse of" | | 15 | "He turned right, towards the" | | 16 | "The structure loomed out of" | | 17 | "The wind howled down the" | | 18 | "This wasn't on her manifest." | | 19 | "Camden North was a ghost" |
| | ratio | 0.894 | |
| 81.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 123 | | matches | | 0 | "If she followed him, she" | | 1 | "If she turned back now," |
| | ratio | 0.016 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 6 | | matches | | 0 | "Her boots hit the ground with military precision, the thud of her left heel striking before her right, a rhythm that had kept her alive through eighteen years o…" | | 1 | "Water plastered her closely cropped salt-and-pepper hair to her scalp, running into her brown eyes, stinging the corners." | | 2 | "She caught a glimpse of the olive skin of his face as he ducked his head, and the silver glint of a Saint Christopher medallion swinging against his chest, catc…" | | 3 | "It felt pressurized, as if the atmosphere itself had shifted beneath the surface." | | 4 | "The tracks were rusted over, choked with weeds that glowed faintly in the dark, bioluminescent strains that hadn't been cataloged in any botanical registry." | | 5 | "She raised her flashlight, the beam cutting through the crowd of beings and men who stopped, heads turning as she passed." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 1 | | effectiveRatio | 1 | |