| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.207 | | leniency | 0.414 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1227 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | 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) | |
| 30.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1227 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "gloom" | | 1 | "standard" | | 2 | "traced" | | 3 | "etched" | | 4 | "weight" | | 5 | "trembled" | | 6 | "silence" | | 7 | "measured" | | 8 | "wavered" | | 9 | "flickered" | | 10 | "footsteps" | | 11 | "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 | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1227 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 800 | | uniqueNames | 6 | | maxNameDensity | 1.63 | | worstName | "Patel" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Patel" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Patel | 13 | | Chelsea | 1 | | Harlow | 10 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Patel" | | 3 | "Chelsea" | | 4 | "Harlow" |
| | places | (empty) | | globalScore | 0.688 | | windowScore | 0.5 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 2 | | matches | | 0 | "as though tracking movement rather than direction" | | 1 | "as though counting steps she had not yet taken" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.815 | | wordCount | 1227 | | matches | | 0 | "not north but directly at the body" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 33.16 | | std | 24.27 | | cv | 0.732 | | sampleLengths | | 0 | 66 | | 1 | 41 | | 2 | 6 | | 3 | 11 | | 4 | 43 | | 5 | 59 | | 6 | 57 | | 7 | 7 | | 8 | 7 | | 9 | 81 | | 10 | 10 | | 11 | 38 | | 12 | 8 | | 13 | 20 | | 14 | 46 | | 15 | 97 | | 16 | 3 | | 17 | 4 | | 18 | 28 | | 19 | 12 | | 20 | 53 | | 21 | 15 | | 22 | 57 | | 23 | 2 | | 24 | 14 | | 25 | 42 | | 26 | 20 | | 27 | 52 | | 28 | 18 | | 29 | 47 | | 30 | 5 | | 31 | 65 | | 32 | 25 | | 33 | 34 | | 34 | 25 | | 35 | 61 | | 36 | 48 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 139 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 803 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.0361145703611457 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007471980074719801 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 11.47 | | std | 8.96 | | cv | 0.782 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 20 | | 3 | 8 | | 4 | 3 | | 5 | 18 | | 6 | 15 | | 7 | 8 | | 8 | 6 | | 9 | 11 | | 10 | 7 | | 11 | 36 | | 12 | 2 | | 13 | 18 | | 14 | 9 | | 15 | 9 | | 16 | 4 | | 17 | 17 | | 18 | 5 | | 19 | 22 | | 20 | 14 | | 21 | 9 | | 22 | 7 | | 23 | 7 | | 24 | 7 | | 25 | 9 | | 26 | 18 | | 27 | 5 | | 28 | 18 | | 29 | 17 | | 30 | 14 | | 31 | 2 | | 32 | 8 | | 33 | 7 | | 34 | 6 | | 35 | 15 | | 36 | 10 | | 37 | 8 | | 38 | 3 | | 39 | 17 | | 40 | 8 | | 41 | 22 | | 42 | 6 | | 43 | 10 | | 44 | 7 | | 45 | 15 | | 46 | 8 | | 47 | 13 | | 48 | 2 | | 49 | 15 |
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| 68.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.42990654205607476 | | totalSentences | 107 | | uniqueOpeners | 46 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 74 | | matches | | 0 | "Her leather watch strap chafed" | | 1 | "She ignored him." | | 2 | "She lifted the lapel gently" | | 3 | "She moved the beam lower." | | 4 | "She traced one with her" | | 5 | "She slipped it into an" | | 6 | "She photographed it with her" | | 7 | "They worked in silence for" | | 8 | "She crouched, measured it with" | | 9 | "He joined her." | | 10 | "She pointed at the print." | | 11 | "She ignored it." | | 12 | "She shook her head." | | 13 | "She said nothing for three" | | 14 | "She turned the torch off." | | 15 | "She walked back to the" | | 16 | "She lifted the edge again" | | 17 | "She brushed a sample into" | | 18 | "She took one more photograph," | | 19 | "It pointed straight at the" |
| | ratio | 0.27 | |
| 0.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 74 | | matches | | 0 | "Harlow Quinn strode past the" | | 1 | "Her leather watch strap chafed" | | 2 | "The abandoned Tube station beneath" | | 3 | "She ignored him." | | 4 | "The body lay half-curled against" | | 5 | "DS Patel crouched beside it," | | 6 | "Gravel crunched under her boots" | | 7 | "Patel straightened, brushing dust from" | | 8 | "The woman's coat was expensive" | | 9 | "She lifted the lapel gently" | | 10 | "The lining showed no tags," | | 11 | "Someone had removed them." | | 12 | "She moved the beam lower." | | 13 | "The edges were clean, almost" | | 14 | "She traced one with her" | | 15 | "The mark felt warm even" | | 16 | "Patel stood and swept his" | | 17 | "The beam found old posters" | | 18 | "Something glinted between the rails." | | 19 | "Harlow crossed to it and" |
| | ratio | 0.919 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "Harlow Quinn strode past the police tape that sagged across the entrance to the forgotten platform." | | 1 | "The woman's coat was expensive wool, the kind that belonged above ground in Chelsea rather than beneath it." | | 2 | "The tread was distinctive: a narrow heel and a sole pattern that matched none of the standard issue boots of the attending officers." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.069 | | leniency | 0.138 | | rawRatio | 0 | | effectiveRatio | 0 | |