| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 6 | | adverbTags | | 0 | "She asked suddenly [suddenly]" | | 1 | "Calloway said slowly [slowly]" | | 2 | "She said quietly [quietly]" | | 3 | "Quinn said simply [simply]" | | 4 | "He asked finally [finally]" | | 5 | "She said quietly [quietly]" |
| | dialogueSentences | 37 | | tagDensity | 0.514 | | leniency | 1 | | rawRatio | 0.316 | | effectiveRatio | 0.316 | |
| 74.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1189 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "cautiously" | | 1 | "wickedly" | | 2 | "suddenly" | | 3 | "slowly" | | 4 | "softly" |
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| 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) | |
| 36.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1189 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "shattered" | | 1 | "amidst" | | 2 | "eyebrow" | | 3 | "velvet" | | 4 | "etched" | | 5 | "echo" | | 6 | "racing" | | 7 | "newfound" | | 8 | "could feel" | | 9 | "echoing" | | 10 | "whisper" | | 11 | "unreadable" | | 12 | "traced" | | 13 | "silence" |
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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 | 1 | | narrationSentences | 59 | | matches | | |
| 21.79% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "began to" | | 2 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1188 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 72.53% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 839 | | uniqueNames | 8 | | maxNameDensity | 1.55 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 13 | | Calloway | 9 | | London | 1 | | Potts | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Calloway" | | 3 | "Potts" | | 4 | "Market" |
| | places | | | globalScore | 0.725 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.842 | | wordCount | 1188 | | matches | | 0 | "not a young man, but neither was he old enough to merit such violent dispatch" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 91.99% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 31.26 | | std | 14.76 | | cv | 0.472 | | sampleLengths | | 0 | 56 | | 1 | 22 | | 2 | 39 | | 3 | 52 | | 4 | 2 | | 5 | 12 | | 6 | 62 | | 7 | 20 | | 8 | 32 | | 9 | 10 | | 10 | 27 | | 11 | 51 | | 12 | 52 | | 13 | 25 | | 14 | 30 | | 15 | 28 | | 16 | 34 | | 17 | 33 | | 18 | 8 | | 19 | 44 | | 20 | 13 | | 21 | 27 | | 22 | 37 | | 23 | 14 | | 24 | 39 | | 25 | 34 | | 26 | 15 | | 27 | 38 | | 28 | 18 | | 29 | 42 | | 30 | 18 | | 31 | 43 | | 32 | 48 | | 33 | 41 | | 34 | 32 | | 35 | 9 | | 36 | 37 | | 37 | 44 |
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| 81.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 59 | | matches | | 0 | "been caved" | | 1 | "been killed" | | 2 | "was clenched" | | 3 | "were dragged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 155 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 90.99% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 676 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.05029585798816568 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.014792899408284023 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 15.23 | | std | 6.92 | | cv | 0.454 | | sampleLengths | | 0 | 19 | | 1 | 18 | | 2 | 19 | | 3 | 22 | | 4 | 11 | | 5 | 28 | | 6 | 19 | | 7 | 16 | | 8 | 17 | | 9 | 2 | | 10 | 2 | | 11 | 10 | | 12 | 18 | | 13 | 18 | | 14 | 26 | | 15 | 13 | | 16 | 7 | | 17 | 18 | | 18 | 14 | | 19 | 6 | | 20 | 4 | | 21 | 7 | | 22 | 20 | | 23 | 11 | | 24 | 16 | | 25 | 24 | | 26 | 19 | | 27 | 14 | | 28 | 19 | | 29 | 25 | | 30 | 16 | | 31 | 14 | | 32 | 28 | | 33 | 13 | | 34 | 15 | | 35 | 6 | | 36 | 23 | | 37 | 10 | | 38 | 8 | | 39 | 12 | | 40 | 32 | | 41 | 13 | | 42 | 7 | | 43 | 20 | | 44 | 14 | | 45 | 23 | | 46 | 11 | | 47 | 3 | | 48 | 13 | | 49 | 15 |
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| 82.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5 | | totalSentences | 78 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 54.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 58 | | matches | | 0 | "She asked DS Calloway, who'd" | | 1 | "He replied, shaking his shaggy" | | 2 | "Her colleague watched as she" | | 3 | "His skull had been caved" | | 4 | "He raised his hands in" | | 5 | "She didn't respond, turning instead" | | 6 | "She asked suddenly, reaching out" | | 7 | "It was old and tarnished," | | 8 | "She felt a cold tingle" | | 9 | "It couldn't be coincidence, could" | | 10 | "She said quietly, rising to" | | 11 | "She replied, turning to face" | | 12 | "He whispered, his voice strangled" | | 13 | "She could feel it tugging" | | 14 | "She glanced around the market," | | 15 | "She leaned in close, speaking" | | 16 | "She said, her voice barely" | | 17 | "She murmured, her voice like" | | 18 | "It had grown even larger" | | 19 | "She shouted at Calloway as" |
| | ratio | 0.414 | |
| 3.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 58 | | matches | | 0 | "The abandoned Tube station reeked" | | 1 | "Detective Harlow Quinn stepped cautiously" | | 2 | "The crime scene stretched out" | | 3 | "She asked DS Calloway, who'd" | | 4 | "He replied, shaking his shaggy" | | 5 | "Quinn arched an eyebrow, glancing" | | 6 | "Stalls draped in velvet and" | | 7 | "The air hummed with whispered" | | 8 | "Her colleague watched as she" | | 9 | "Reginald Potts was not a" | | 10 | "His skull had been caved" | | 11 | "Quinn muttered, kneeling down to" | | 12 | "Calloway pointed out dryly" | | 13 | "Quinn shot him a sharp" | | 14 | "He raised his hands in" | | 15 | "She didn't respond, turning instead" | | 16 | "Something about the positioning of" | | 17 | "She asked suddenly, reaching out" | | 18 | "It was old and tarnished," | | 19 | "She felt a cold tingle" |
| | ratio | 0.914 | |
| 86.21% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 58 | | matches | | | ratio | 0.017 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn stepped cautiously over the debris-strewn platform, her boots crunching on shattered glass and crumbling plaster." | | 1 | "Quinn arched an eyebrow, glancing around the eerie marketplace that had sprung up in this forgotten corner of London." | | 2 | "As if on cue, the air began to shimmer and writhe, tendrils of shadow reaching out to grasp at the edges of reality." |
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| 46.05% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 3 | | matches | | 0 | "Calloway asked, bewildered" | | 1 | "She replied, turning to face him with a grim smile" | | 2 | "She said, her voice barely a whisper" |
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| 14.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 5 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "He whispered (whisper)" | | 2 | "Calloway hissed (hiss)" | | 3 | "She murmured (murmur)" | | 4 | "She shouted (shout)" |
| | dialogueSentences | 37 | | tagDensity | 0.459 | | leniency | 0.919 | | rawRatio | 0.294 | | effectiveRatio | 0.27 | |