| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said quietly [quietly]" | | 1 | "The word tasted like [like]" |
| | dialogueSentences | 54 | | tagDensity | 0.426 | | leniency | 0.852 | | rawRatio | 0.087 | | effectiveRatio | 0.074 | |
| 87.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1203 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slowly" | | 1 | "barely above a whisper" | | 2 | "carefully" |
| |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 58.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1203 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "silk" | | 1 | "navigated" | | 2 | "flicker" | | 3 | "silence" | | 4 | "traced" | | 5 | "whisper" | | 6 | "weight" | | 7 | "constructed" | | 8 | "wavered" | | 9 | "trembled" |
| |
| 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 | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1194 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 683 | | uniqueNames | 11 | | maxNameDensity | 3.07 | | worstName | "Megan" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Megan" | | discoveredNames | | Rory | 17 | | Friday | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Eva | 1 | | Davies | 1 | | Soho | 1 | | Megan | 21 | | Silas | 3 | | Bowie | 1 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Davies" | | 3 | "Megan" | | 4 | "Silas" | | 5 | "Bowie" |
| | places | | 0 | "Friday" | | 1 | "Raven" | | 2 | "London" | | 3 | "Soho" |
| | globalScore | 0 | | windowScore | 0 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like old wood and spilled lager" | | 1 | "quite parse" |
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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 | 1194 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 22.11 | | std | 16.42 | | cv | 0.742 | | sampleLengths | | 0 | 62 | | 1 | 41 | | 2 | 2 | | 3 | 20 | | 4 | 31 | | 5 | 11 | | 6 | 19 | | 7 | 13 | | 8 | 16 | | 9 | 57 | | 10 | 27 | | 11 | 9 | | 12 | 3 | | 13 | 13 | | 14 | 35 | | 15 | 41 | | 16 | 19 | | 17 | 5 | | 18 | 23 | | 19 | 11 | | 20 | 33 | | 21 | 6 | | 22 | 34 | | 23 | 10 | | 24 | 43 | | 25 | 5 | | 26 | 16 | | 27 | 10 | | 28 | 33 | | 29 | 30 | | 30 | 38 | | 31 | 32 | | 32 | 25 | | 33 | 5 | | 34 | 2 | | 35 | 21 | | 36 | 4 | | 37 | 19 | | 38 | 3 | | 39 | 37 | | 40 | 25 | | 41 | 2 | | 42 | 71 | | 43 | 6 | | 44 | 47 | | 45 | 14 | | 46 | 4 | | 47 | 14 | | 48 | 35 | | 49 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 24.56% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 114 | | matches | | 0 | "was watching" | | 1 | "was watching" | | 2 | "weren't saying" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 92 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 687 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.034934497816593885 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011644832605531296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 12.98 | | std | 10.53 | | cv | 0.812 | | sampleLengths | | 0 | 25 | | 1 | 37 | | 2 | 23 | | 3 | 18 | | 4 | 2 | | 5 | 13 | | 6 | 7 | | 7 | 16 | | 8 | 15 | | 9 | 11 | | 10 | 7 | | 11 | 12 | | 12 | 13 | | 13 | 6 | | 14 | 10 | | 15 | 27 | | 16 | 30 | | 17 | 6 | | 18 | 12 | | 19 | 9 | | 20 | 9 | | 21 | 3 | | 22 | 12 | | 23 | 1 | | 24 | 5 | | 25 | 30 | | 26 | 33 | | 27 | 8 | | 28 | 16 | | 29 | 3 | | 30 | 5 | | 31 | 12 | | 32 | 11 | | 33 | 7 | | 34 | 2 | | 35 | 2 | | 36 | 26 | | 37 | 7 | | 38 | 6 | | 39 | 25 | | 40 | 9 | | 41 | 10 | | 42 | 2 | | 43 | 9 | | 44 | 16 | | 45 | 3 | | 46 | 13 | | 47 | 3 | | 48 | 2 | | 49 | 16 |
| |
| 96.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.6304347826086957 | | totalSentences | 92 | | uniqueOpeners | 58 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Then she raised her hand" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 55 | | matches | | 0 | "She'd come down for a" | | 1 | "She claimed her usual spot" | | 2 | "She pulled off her helmet," | | 3 | "He set the glass in" | | 4 | "he said quietly" | | 5 | "She wore a cream blazer" | | 6 | "Their eyes met across the" | | 7 | "She set the glass down" | | 8 | "She navigated the crowd with" | | 9 | "She set her bag on" | | 10 | "She gave him the smallest" | | 11 | "Her fingers tightened around the" |
| | ratio | 0.218 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 55 | | matches | | 0 | "The bass-heavy thrum of some" | | 1 | "She'd come down for a" | | 2 | "She claimed her usual spot" | | 3 | "Silas caught her eye from" | | 4 | "She pulled off her helmet," | | 5 | "He set the glass in" | | 6 | "The signet ring on his" | | 7 | "he said quietly" | | 8 | "Rory's hand froze halfway to" | | 9 | "Something in his tone made" | | 10 | "Rory turned slowly on her" | | 11 | "The crowd shifted, and through" | | 12 | "Megan Davies sat alone at" | | 13 | "She wore a cream blazer" | | 14 | "Their eyes met across the" | | 15 | "Megan's face shifted through surprise," | | 16 | "Rory picked up her whisky" | | 17 | "She set the glass down" | | 18 | "Megan stood, smoothing her skirt." | | 19 | "She navigated the crowd with" |
| | ratio | 0.927 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 5 | | matches | | 0 | "She'd come down for a quick drink before collapsing into bed, her legs aching from twelve hours of weaving through London traffic on Eva's ancient bicycle, deli…" | | 1 | "Megan Davies sat alone at a corner table, her champagne-blonde hair cut in one of those sharp bobs that cost more than Rory made in a week." | | 2 | "She navigated the crowd with the same careful precision she'd used walking home from pubs twelve years ago, counting her steps, monitoring her posture, always a…" | | 3 | "Silas placed the second whisky in front of Rory and gave her a look that said he was watching, that he'd step in if needed." | | 4 | "The tennis bracelet at her wrist that caught the light when she moved." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.037 | | leniency | 0.074 | | rawRatio | 0 | | effectiveRatio | 0 | |