| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn stood slowly [slowly]" |
| | dialogueSentences | 79 | | tagDensity | 0.342 | | leniency | 0.684 | | rawRatio | 0.037 | | effectiveRatio | 0.025 | |
| 96.70% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1514 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 66.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1514 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "traced" | | 2 | "etched" | | 3 | "perfect" | | 4 | "pulse" | | 5 | "quickened" | | 6 | "resolved" | | 7 | "echoed" |
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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 | 118 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 168 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1512 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 873 | | uniqueNames | 9 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Lock | 2 | | Harlow | 1 | | Quinn | 17 | | Vikram | 1 | | Patel | 16 | | September | 1 | | Fresh | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Vikram" | | 3 | "Patel" | | 4 | "Morris" |
| | places | | | globalScore | 0.526 | | windowScore | 0.5 | |
| 70.63% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 2 | | matches | | 0 | "as if reading her thoughts" | | 1 | "looked like a map" |
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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 | 1512 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 168 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 79 | | mean | 19.14 | | std | 13.47 | | cv | 0.704 | | sampleLengths | | 0 | 23 | | 1 | 36 | | 2 | 29 | | 3 | 33 | | 4 | 4 | | 5 | 14 | | 6 | 29 | | 7 | 18 | | 8 | 38 | | 9 | 6 | | 10 | 12 | | 11 | 36 | | 12 | 10 | | 13 | 21 | | 14 | 32 | | 15 | 5 | | 16 | 16 | | 17 | 20 | | 18 | 27 | | 19 | 2 | | 20 | 13 | | 21 | 1 | | 22 | 19 | | 23 | 32 | | 24 | 27 | | 25 | 2 | | 26 | 36 | | 27 | 3 | | 28 | 17 | | 29 | 40 | | 30 | 24 | | 31 | 10 | | 32 | 2 | | 33 | 48 | | 34 | 39 | | 35 | 27 | | 36 | 35 | | 37 | 4 | | 38 | 3 | | 39 | 42 | | 40 | 21 | | 41 | 2 | | 42 | 2 | | 43 | 31 | | 44 | 6 | | 45 | 1 | | 46 | 38 | | 47 | 2 | | 48 | 45 | | 49 | 25 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 118 | | matches | | 0 | "was buttoned" | | 1 | "been rotated" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 156 | | matches | | 0 | "was waving" | | 1 | "was rising" | | 2 | "was forming" | | 3 | "was holding" | | 4 | "was spinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 168 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 873 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.020618556701030927 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003436426116838488 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 168 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 168 | | mean | 9 | | std | 6.87 | | cv | 0.763 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 9 | | 3 | 7 | | 4 | 16 | | 5 | 13 | | 6 | 17 | | 7 | 5 | | 8 | 11 | | 9 | 4 | | 10 | 12 | | 11 | 2 | | 12 | 4 | | 13 | 20 | | 14 | 5 | | 15 | 6 | | 16 | 12 | | 17 | 5 | | 18 | 11 | | 19 | 3 | | 20 | 4 | | 21 | 10 | | 22 | 1 | | 23 | 4 | | 24 | 6 | | 25 | 12 | | 26 | 12 | | 27 | 14 | | 28 | 10 | | 29 | 2 | | 30 | 8 | | 31 | 15 | | 32 | 6 | | 33 | 9 | | 34 | 2 | | 35 | 1 | | 36 | 8 | | 37 | 4 | | 38 | 8 | | 39 | 5 | | 40 | 7 | | 41 | 9 | | 42 | 15 | | 43 | 5 | | 44 | 27 | | 45 | 2 | | 46 | 13 | | 47 | 1 | | 48 | 2 | | 49 | 17 |
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| 76.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.48214285714285715 | | totalSentences | 168 | | uniqueOpeners | 81 | |
| 34.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 98 | | matches | | 0 | "Too cold for September." |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 98 | | matches | | 0 | "She flashed her badge without" | | 1 | "She stopped at the edge" | | 2 | "His coat was buttoned wrong." | | 3 | "He pointed to the ground" | | 4 | "It fanned out like wings." | | 5 | "She crouched near the shadow's" | | 6 | "She pulled gloves from her" | | 7 | "She approached the support strut" | | 8 | "She leaned closer to examine" | | 9 | "She knelt, careful not to" | | 10 | "She'd seen similar markings before." | | 11 | "She held out her hand" | | 12 | "She slipped the bag into" | | 13 | "She studied the surrounding area" | | 14 | "She pointed to the canal" | | 15 | "She turned to face him" | | 16 | "He knew something." | | 17 | "She pulled out the evidence" | | 18 | "She reached the bridge just" | | 19 | "He sawed through the first" |
| | ratio | 0.224 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 98 | | matches | | 0 | "The body hung upside down" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The uniform at the perimeter" | | 3 | "She flashed her badge without" | | 4 | "She stopped at the edge" | | 5 | "DS Vikram Patel climbed up" | | 6 | "His coat was buttoned wrong." | | 7 | "He pointed to the ground" | | 8 | "Quinn followed his gesture." | | 9 | "It fanned out like wings." | | 10 | "Quinn walked down the embankment." | | 11 | "The mud squelched under her" | | 12 | "Copper and rot." | | 13 | "She crouched near the shadow's" | | 14 | "Quinn stood and pointed to" | | 15 | "She pulled gloves from her" | | 16 | "She approached the support strut" | | 17 | "The kind used in theatre" | | 18 | "She leaned closer to examine" | | 19 | "Patel had followed her down" |
| | ratio | 0.816 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 98 | | matches | | 0 | "As if the body had" | | 1 | "Before Quinn could answer, a" |
| | ratio | 0.02 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "The body hung upside down from the Camden Lock bridge, suspended by cables that shouldn't have held human weight without tearing through flesh." | | 1 | "Inside was a small brass compass, its face etched with symbols that matched the bone disc." | | 2 | "But she was already moving, pulling her phone from her pocket, dialling a number she hadn't used in three years." |
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| 87.96% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 2 | | matches | | 0 | "DS Vikram Patel climbed, his notebook already open" | | 1 | "Patel said, as if reading her thoughts" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 79 | | tagDensity | 0.101 | | leniency | 0.203 | | rawRatio | 0.125 | | effectiveRatio | 0.025 | |