| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.114 | | leniency | 0.227 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1769 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 85.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1769 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "structure" | | 2 | "flickered" | | 3 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 164 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 164 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 203 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1766 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1569 | | uniqueNames | 7 | | maxNameDensity | 1.91 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 30 | | Camden | 2 | | Morris | 3 | | Tube | 1 | | Town | 1 | | Northern | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | | globalScore | 0.544 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 121 | | 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 | 1766 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 203 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 103 | | mean | 17.15 | | std | 16.38 | | cv | 0.956 | | sampleLengths | | 0 | 28 | | 1 | 2 | | 2 | 29 | | 3 | 74 | | 4 | 28 | | 5 | 4 | | 6 | 5 | | 7 | 41 | | 8 | 6 | | 9 | 4 | | 10 | 8 | | 11 | 5 | | 12 | 5 | | 13 | 42 | | 14 | 25 | | 15 | 40 | | 16 | 3 | | 17 | 6 | | 18 | 7 | | 19 | 42 | | 20 | 27 | | 21 | 3 | | 22 | 15 | | 23 | 36 | | 24 | 12 | | 25 | 37 | | 26 | 11 | | 27 | 51 | | 28 | 7 | | 29 | 50 | | 30 | 15 | | 31 | 3 | | 32 | 46 | | 33 | 3 | | 34 | 3 | | 35 | 24 | | 36 | 5 | | 37 | 10 | | 38 | 60 | | 39 | 31 | | 40 | 18 | | 41 | 14 | | 42 | 21 | | 43 | 3 | | 44 | 41 | | 45 | 52 | | 46 | 28 | | 47 | 2 | | 48 | 4 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 164 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 262 | | matches | | |
| 72.48% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 3 | | flaggedSentences | 5 | | totalSentences | 203 | | ratio | 0.025 | | matches | | 0 | "His shoulder clipped the van’s mirror; it snapped back with a metallic crack." | | 1 | "Its face had cracked years ago; she had kept it because it still marked the hours." | | 2 | "She read the name—Morris—and felt the old case room return for a breath: an evidence bag split at the seam, a desk lamp left burning, a partner who hadn’t come home." | | 3 | "Fear had changed its shape; it no longer pointed at her." | | 4 | "The air changed at once—warmer, thick with incense, oil, and damp wool." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1573 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.017800381436745075 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006357279084551812 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 203 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 203 | | mean | 8.7 | | std | 5.27 | | cv | 0.605 | | sampleLengths | | 0 | 10 | | 1 | 18 | | 2 | 2 | | 3 | 11 | | 4 | 13 | | 5 | 5 | | 6 | 13 | | 7 | 4 | | 8 | 32 | | 9 | 13 | | 10 | 12 | | 11 | 12 | | 12 | 16 | | 13 | 4 | | 14 | 5 | | 15 | 19 | | 16 | 5 | | 17 | 17 | | 18 | 6 | | 19 | 4 | | 20 | 8 | | 21 | 5 | | 22 | 5 | | 23 | 15 | | 24 | 7 | | 25 | 20 | | 26 | 8 | | 27 | 17 | | 28 | 4 | | 29 | 6 | | 30 | 15 | | 31 | 15 | | 32 | 3 | | 33 | 6 | | 34 | 3 | | 35 | 4 | | 36 | 10 | | 37 | 16 | | 38 | 16 | | 39 | 9 | | 40 | 7 | | 41 | 11 | | 42 | 3 | | 43 | 9 | | 44 | 3 | | 45 | 3 | | 46 | 8 | | 47 | 11 | | 48 | 17 | | 49 | 5 |
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| 48.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3103448275862069 | | totalSentences | 203 | | uniqueOpeners | 63 | |
| 20.96% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 159 | | matches | | 0 | "Somewhere below, a door creaked," |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 159 | | matches | | 0 | "His shoulder clipped the van’s" | | 1 | "He never looked behind him." | | 2 | "She shortened her stride." | | 3 | "He had kept his answers" | | 4 | "Her radio rasped against her" | | 5 | "She pushed the transmit button" | | 6 | "He planted a foot on" | | 7 | "Her coat snagged on a" | | 8 | "She tore free, dropped into" | | 9 | "He glanced back." | | 10 | "His eyes widened." | | 11 | "He turned and bolted." | | 12 | "Its face had cracked years" | | 13 | "She checked it now by" | | 14 | "He reached the opposite kerb" | | 15 | "She waited half a beat," | | 16 | "He had dropped something." | | 17 | "She read the name—Morris—and felt" | | 18 | "She closed her fist around" | | 19 | "He looked over his shoulder." |
| | ratio | 0.289 | |
| 7.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 159 | | matches | | 0 | "Rain struck the pavement hard" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "The suspect cut between a" | | 3 | "His shoulder clipped the van’s" | | 4 | "He never looked behind him." | | 5 | "Quinn vaulted a puddle, landed" | | 6 | "She shortened her stride." | | 7 | "The man wore a dark" | | 8 | "He had kept his answers" | | 9 | "A cyclist swore as the" | | 10 | "Quinn followed, flashing her badge" | | 11 | "The driver shut the door." | | 12 | "The suspect struck the pavement" | | 13 | "Rainwater poured from a drainpipe." | | 14 | "Quinn reached the passage in" | | 15 | "Her radio rasped against her" | | 16 | "She pushed the transmit button" | | 17 | "The suspect slipped through a" | | 18 | "Quinn hit it with her" | | 19 | "The man had already reached" |
| | ratio | 0.906 | |
| 31.45% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 159 | | matches | | | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 76 | | technicalSentenceCount | 1 | | matches | | 0 | "She read the name—Morris—and felt the old case room return for a breath: an evidence bag split at the seam, a desk lamp left burning, a partner who hadn’t come …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.068 | | leniency | 0.136 | | rawRatio | 0 | | effectiveRatio | 0 | |