| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.405 | | leniency | 0.81 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 906 | | totalAiIsmAdverbs | 2 | | 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) | |
| 83.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 906 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "echo" | | 1 | "weight" | | 2 | "fractured" |
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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 | 61 | | matches | (empty) | |
| 72.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 899 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 10.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 645 | | uniqueNames | 12 | | maxNameDensity | 2.79 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Tennison | 9 | | Camden | 2 | | Morris | 3 | | Sulfur | 1 | | London | 1 | | Underground | 1 | | Eva | 1 | | Kowalski | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tennison" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.105 | | windowScore | 0.167 | |
| 38.89% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like carved bone discs" | | 1 | "symbols that seemed to shift when viewed peripherally" |
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| 88.77% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.112 | | wordCount | 899 | | matches | | 0 | "not north but directly at the doorway" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 23.05 | | std | 14.37 | | cv | 0.623 | | sampleLengths | | 0 | 64 | | 1 | 32 | | 2 | 36 | | 3 | 22 | | 4 | 10 | | 5 | 39 | | 6 | 5 | | 7 | 14 | | 8 | 45 | | 9 | 17 | | 10 | 24 | | 11 | 32 | | 12 | 4 | | 13 | 23 | | 14 | 42 | | 15 | 24 | | 16 | 13 | | 17 | 11 | | 18 | 32 | | 19 | 8 | | 20 | 41 | | 21 | 17 | | 22 | 9 | | 23 | 3 | | 24 | 35 | | 25 | 21 | | 26 | 45 | | 27 | 27 | | 28 | 10 | | 29 | 37 | | 30 | 22 | | 31 | 4 | | 32 | 6 | | 33 | 17 | | 34 | 27 | | 35 | 22 | | 36 | 3 | | 37 | 40 | | 38 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 113 | | matches | (empty) | |
| 9.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 86 | | ratio | 0.047 | | matches | | 0 | "Quinn examined the victim's clothes—tailored wool coat, leather shoes that cost more than her monthly salary." | | 1 | "The memory hit like cold water—her partner's vacant eyes, the impossible claw marks, the way the other officers refused to acknowledge what they'd all seen." | | 2 | "The woman's body showed no obvious wounds, but her skin bore strange markings—symbols that seemed to shift when viewed peripherally." | | 3 | "Others matched the carvings she'd seen in evidence photos from Morris's case—photos that had mysteriously vanished from the files." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 650 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.027692307692307693 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.012307692307692308 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 10.45 | | std | 5.69 | | cv | 0.544 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 27 | | 3 | 20 | | 4 | 12 | | 5 | 8 | | 6 | 2 | | 7 | 2 | | 8 | 8 | | 9 | 16 | | 10 | 11 | | 11 | 11 | | 12 | 5 | | 13 | 5 | | 14 | 8 | | 15 | 22 | | 16 | 9 | | 17 | 5 | | 18 | 10 | | 19 | 4 | | 20 | 18 | | 21 | 15 | | 22 | 12 | | 23 | 12 | | 24 | 5 | | 25 | 14 | | 26 | 10 | | 27 | 16 | | 28 | 16 | | 29 | 4 | | 30 | 4 | | 31 | 19 | | 32 | 3 | | 33 | 14 | | 34 | 25 | | 35 | 8 | | 36 | 16 | | 37 | 11 | | 38 | 2 | | 39 | 11 | | 40 | 14 | | 41 | 18 | | 42 | 5 | | 43 | 3 | | 44 | 13 | | 45 | 8 | | 46 | 20 | | 47 | 10 | | 48 | 7 | | 49 | 7 |
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| 95.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.627906976744186 | | totalSentences | 86 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 59 | | matches | | 0 | "She stood, her military bearing" | | 1 | "She pointed to the fractured" | | 2 | "She turned the bag, watching" | | 3 | "His manicured nails and the" | | 4 | "He produced a small leather" | | 5 | "She'd smelled it once before," | | 6 | "She pocketed the map" | | 7 | "She checked her watch again" |
| | ratio | 0.136 | |
| 10.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 59 | | matches | | 0 | "The bone token clattered onto" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "The corpse lay sprawled across" | | 3 | "The forensics officer shifted his" | | 4 | "Quinn's brown eyes tracked the" | | 5 | "The arc suggested impact from" | | 6 | "She stood, her military bearing" | | 7 | "She pointed to the fractured" | | 8 | "Tennison frowned behind his mask." | | 9 | "Quinn turned her attention to" | | 10 | "The victim's fingers still clutched" | | 11 | "Tennison bagged it without looking" | | 12 | "Quinn intercepted the evidence bag," | | 13 | "The compass face bore etchings" | | 14 | "The needle spun lazily, pointing" | | 15 | "She turned the bag, watching" | | 16 | "Tennison scribbled notes" | | 17 | "Quinn examined the victim's clothes—tailored" | | 18 | "His manicured nails and the" | | 19 | "Tennison sighed but complied." |
| | ratio | 0.898 | |
| 84.75% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 59 | | matches | | 0 | "If the markings were accurate," |
| | ratio | 0.017 | |
| 5.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 5 | | matches | | 0 | "Beyond it stretched a corridor that shouldn't exist, lit by gas lamps that hadn't burned in Camden's tunnels for over a century." | | 1 | "The compass face bore etchings that made her vision blur if she stared too long." | | 2 | "Quinn examined the victim's clothes—tailored wool coat, leather shoes that cost more than her monthly salary." | | 3 | "Sulfur and ozone, with an underlying sweetness that reminded Quinn of rotting flowers." | | 4 | "Others matched the carvings she'd seen in evidence photos from Morris's case—photos that had mysteriously vanished from the files." |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "The forensics officer shifted, boots scraping against decades of accumulated grime" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 42 | | tagDensity | 0.024 | | leniency | 0.048 | | rawRatio | 1 | | effectiveRatio | 0.048 | |