| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.073 | | leniency | 0.146 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1674 | | 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) | |
| 70.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1674 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "calculating" | | 1 | "silence" | | 2 | "pulsed" | | 3 | "scanned" | | 4 | "velvet" | | 5 | "flickered" | | 6 | "weight" | | 7 | "porcelain" | | 8 | "shattered" |
| |
| 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 | 193 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 193 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 231 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1674 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 31.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1481 | | uniqueNames | 8 | | maxNameDensity | 2.36 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 35 | | Brixton | 1 | | Morris | 2 | | Old | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Brixton" | | 4 | "Old" |
| | globalScore | 0.318 | | windowScore | 0.333 | |
| 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 | 1 | | per1kWords | 0.597 | | wordCount | 1674 | | matches | | 0 | "not at anyone but into the floor" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 231 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 146 | | mean | 11.47 | | std | 12.8 | | cv | 1.117 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 67 | | 3 | 11 | | 4 | 10 | | 5 | 31 | | 6 | 2 | | 7 | 4 | | 8 | 33 | | 9 | 2 | | 10 | 23 | | 11 | 51 | | 12 | 14 | | 13 | 2 | | 14 | 38 | | 15 | 9 | | 16 | 4 | | 17 | 2 | | 18 | 3 | | 19 | 21 | | 20 | 2 | | 21 | 36 | | 22 | 6 | | 23 | 29 | | 24 | 21 | | 25 | 7 | | 26 | 5 | | 27 | 2 | | 28 | 2 | | 29 | 4 | | 30 | 39 | | 31 | 7 | | 32 | 45 | | 33 | 29 | | 34 | 4 | | 35 | 7 | | 36 | 3 | | 37 | 6 | | 38 | 3 | | 39 | 6 | | 40 | 41 | | 41 | 4 | | 42 | 19 | | 43 | 4 | | 44 | 13 | | 45 | 7 | | 46 | 4 | | 47 | 30 | | 48 | 3 | | 49 | 2 |
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| 99.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 193 | | matches | | 0 | "been painted" | | 1 | "were filed" | | 2 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 264 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 231 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1485 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.020202020202020204 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00404040404040404 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 231 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 231 | | mean | 7.25 | | std | 4.65 | | cv | 0.642 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 13 | | 3 | 10 | | 4 | 23 | | 5 | 21 | | 6 | 1 | | 7 | 2 | | 8 | 2 | | 9 | 6 | | 10 | 10 | | 11 | 11 | | 12 | 4 | | 13 | 3 | | 14 | 13 | | 15 | 2 | | 16 | 4 | | 17 | 9 | | 18 | 2 | | 19 | 1 | | 20 | 21 | | 21 | 2 | | 22 | 5 | | 23 | 10 | | 24 | 8 | | 25 | 16 | | 26 | 3 | | 27 | 15 | | 28 | 8 | | 29 | 9 | | 30 | 14 | | 31 | 2 | | 32 | 11 | | 33 | 7 | | 34 | 8 | | 35 | 12 | | 36 | 9 | | 37 | 4 | | 38 | 2 | | 39 | 3 | | 40 | 4 | | 41 | 7 | | 42 | 10 | | 43 | 2 | | 44 | 15 | | 45 | 7 | | 46 | 14 | | 47 | 6 | | 48 | 15 | | 49 | 4 |
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| 51.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3246753246753247 | | totalSentences | 231 | | uniqueOpeners | 75 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 174 | | matches | | 0 | "Only water answered." | | 1 | "Only a smear of black" | | 2 | "Then the suspect shouted from" | | 3 | "Somewhere ahead, a bell rang" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 174 | | matches | | 0 | "He ran beneath the awnings," | | 1 | "He had stolen it from" | | 2 | "She ignored it." | | 3 | "Her boots struck puddles hard" | | 4 | "His face looked narrow and" | | 5 | "He cut across the road" | | 6 | "She flashed her warrant card" | | 7 | "She caught the metallic taste" | | 8 | "Her left wrist knocked against" | | 9 | "She reached the gate." | | 10 | "Her boots slid on the" | | 11 | "Her training demanded backup." | | 12 | "She lifted the radio closer." | | 13 | "She had spent eighteen years" | | 14 | "His voice bounced through the" | | 15 | "She moved fast, pistol raised," | | 16 | "She crouched beside it." | | 17 | "It reflected no light." | | 18 | "She heard voices." | | 19 | "His grey coat dripped onto" |
| | ratio | 0.241 | |
| 23.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 152 | | totalSentences | 174 | | matches | | 0 | "Rain sheeted across Camden High" | | 1 | "Detective Harlow Quinn kept the" | | 2 | "He ran beneath the awnings," | | 3 | "A canvas satchel slapped against" | | 4 | "He had stolen it from" | | 5 | "Quinn had arrived in time" | | 6 | "The ring flashed whenever the" | | 7 | "Quinn pushed through a knot" | | 8 | "Someone shouted after her." | | 9 | "She ignored it." | | 10 | "Her boots struck puddles hard" | | 11 | "The suspect glanced back." | | 12 | "His face looked narrow and" | | 13 | "He cut across the road" | | 14 | "A cab clipped her coat." | | 15 | "The driver leaned out and" | | 16 | "She flashed her warrant card" | | 17 | "The suspect vaulted a low" | | 18 | "Quinn’s lungs burned." | | 19 | "She caught the metallic taste" |
| | ratio | 0.874 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 174 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.073 | | leniency | 0.146 | | rawRatio | 0.333 | | effectiveRatio | 0.049 | |