| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1419 | | 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) | |
| 33.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1419 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "fractured" | | 1 | "measured" | | 2 | "weight" | | 3 | "silence" | | 4 | "shattered" | | 5 | "flickered" | | 6 | "footsteps" | | 7 | "echoed" | | 8 | "indexed" | | 9 | "scanning" | | 10 | "gloom" | | 11 | "clenching" | | 12 | "pulse" | | 13 | "velvet" | | 14 | "traced" | | 15 | "tension" | | 16 | "charged" |
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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 | 147 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 147 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1419 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1419 | | uniqueNames | 13 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Quinn | 14 | | Met | 1 | | Herrera | 10 | | Camden | 4 | | High | 1 | | Street | 1 | | Morris | 3 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Road | 1 | | Detective | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Detective" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Road" |
| | globalScore | 1 | | windowScore | 1 | |
| 98.45% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 2 | | matches | | 0 | "shadows that seemed to breathe" | | 1 | "smelled like copper and fennel and impossi" |
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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 | 1419 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 147 | | matches | | 0 | "understood that hesitation" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 70.95 | | std | 41.11 | | cv | 0.579 | | sampleLengths | | 0 | 113 | | 1 | 59 | | 2 | 69 | | 3 | 122 | | 4 | 8 | | 5 | 75 | | 6 | 113 | | 7 | 12 | | 8 | 96 | | 9 | 104 | | 10 | 95 | | 11 | 93 | | 12 | 107 | | 13 | 114 | | 14 | 6 | | 15 | 117 | | 16 | 48 | | 17 | 13 | | 18 | 35 | | 19 | 20 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 147 | | matches | | 0 | "were stained" | | 1 | "been carved" | | 2 | "was frayed" |
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| 93.33% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 250 | | matches | | 0 | "was carrying" | | 1 | "was drowning" | | 2 | "was trying" | | 3 | "was listening" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 147 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1425 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 38 | | adverbRatio | 0.02666666666666667 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.009122807017543859 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 9.65 | | std | 6.79 | | cv | 0.704 | | sampleLengths | | 0 | 19 | | 1 | 8 | | 2 | 13 | | 3 | 20 | | 4 | 16 | | 5 | 2 | | 6 | 14 | | 7 | 21 | | 8 | 4 | | 9 | 10 | | 10 | 18 | | 11 | 11 | | 12 | 5 | | 13 | 11 | | 14 | 14 | | 15 | 12 | | 16 | 10 | | 17 | 9 | | 18 | 3 | | 19 | 21 | | 20 | 7 | | 21 | 11 | | 22 | 9 | | 23 | 6 | | 24 | 15 | | 25 | 2 | | 26 | 1 | | 27 | 7 | | 28 | 4 | | 29 | 10 | | 30 | 18 | | 31 | 32 | | 32 | 5 | | 33 | 1 | | 34 | 1 | | 35 | 1 | | 36 | 15 | | 37 | 14 | | 38 | 5 | | 39 | 11 | | 40 | 16 | | 41 | 14 | | 42 | 10 | | 43 | 9 | | 44 | 12 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 14 | | 49 | 3 |
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| 64.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4489795918367347 | | totalSentences | 147 | | uniqueOpeners | 66 | |
| 78.13% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 128 | | matches | | 0 | "Instead, a heavy archway had" | | 1 | "Definitely off the books." | | 2 | "Then the canvas drapes parted." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 128 | | matches | | 0 | "She moved with military precision," | | 1 | "He ran with the loose-limbed" | | 2 | "Her lungs burned but she" | | 3 | "She checked the sweep of" | | 4 | "He ducked into a narrow" | | 5 | "He kicked open a sagging" | | 6 | "She descended fast, counting landings," | | 7 | "It was the same wrongness" | | 8 | "She pushed the memory down." | | 9 | "She kept her finger indexed" | | 10 | "Her brown eyes adjusted to" | | 11 | "He stood before a section" | | 12 | "Their flames were too still," | | 13 | "He reached into his pocket" | | 14 | "She holstered her weapon but" | | 15 | "She'd heard the name whispered" | | 16 | "She knew they did." | | 17 | "She produced a similar bone" | | 18 | "Her training laid out the" | | 19 | "It was the doctrine that" |
| | ratio | 0.281 | |
| 77.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 128 | | matches | | 0 | "Rain fell in hard, diagonal" | | 1 | "Harlow Quinn ran through it" | | 2 | "She moved with military precision," | | 3 | "He ran with the loose-limbed" | | 4 | "Quinn closed the distance." | | 5 | "Her lungs burned but she" | | 6 | "The worn leather strap of" | | 7 | "She checked the sweep of" | | 8 | "The city was drowning, and" | | 9 | "He ducked into a narrow" | | 10 | "Quinn followed, her hand dropping" | | 11 | "Water cascaded from rusted fire" | | 12 | "The alley stank of wet" | | 13 | "Herrera didn't slow." | | 14 | "He kicked open a sagging" | | 15 | "Quinn hit the threshold a" | | 16 | "She descended fast, counting landings," | | 17 | "The air changed as she" | | 18 | "The rain's roar faded, replaced" | | 19 | "Something else underneath it, faint" |
| | ratio | 0.766 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 128 | | matches | | 0 | "Even from thirty feet away," | | 1 | "If she waited, he'd disappear" | | 2 | "If she called it in," |
| | ratio | 0.023 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 7 | | matches | | 0 | "He ran with the loose-limbed urgency of a man who knew exactly what he was carrying and exactly who wanted it." | | 1 | "The same scent that had no business in a police report, no name in a forensics ledger, and no explanation that hadn't gotten her quietly reassigned to desk duty…" | | 2 | "Emergency lighting flickered in erratic pulses, casting long, jittery shadows that seemed to breathe." | | 3 | "Their flames were too still, too blue, casting a cold luminescence that made the air shimmer." | | 4 | "An underground exchange that moved with the lunar cycle, currently squatting in the guts of Camden's dead station." | | 5 | "The murmur of voices speaking in cadences that snagged on the ear, syllables sliding into each other like oil on water." | | 6 | "She let her bearing do the talking, the rigid, uncompromising posture of a woman who had spent two decades walking into rooms where people wanted her dead." |
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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 | |