| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1357 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "completely" | | 2 | "quickly" | | 3 | "slightly" |
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| 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) | |
| 29.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1357 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "familiar" | | 1 | "footsteps" | | 2 | "lilt" | | 3 | "scanned" | | 4 | "flickered" | | 5 | "depths" | | 6 | "warmth" | | 7 | "unreadable" | | 8 | "implication" | | 9 | "could feel" | | 10 | "scanning" | | 11 | "racing" | | 12 | "whisper" | | 13 | "weight" | | 14 | "silence" | | 15 | "unspoken" | | 16 | "wavering" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "The weight of those words" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 104 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 10 | | totalWords | 1351 | | ratio | 0.007 | | matches | | 0 | "Unless you're just the delivery girl." | | 1 | "He knows you're here." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 978 | | uniqueNames | 9 | | maxNameDensity | 1.94 | | worstName | "Laila" | | maxWindowNameDensity | 5 | | worstWindowName | "Laila" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Rory | 9 | | Harder | 1 | | Welsh | 1 | | Laila | 19 | | Cardiff | 1 | | Silas | 10 | | Aurora | 15 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Laila" | | 3 | "Silas" | | 4 | "Aurora" |
| | places | | | globalScore | 0.529 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like an actress in a play she hadn" |
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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 | 1351 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 32.95 | | std | 19.9 | | cv | 0.604 | | sampleLengths | | 0 | 93 | | 1 | 38 | | 2 | 23 | | 3 | 59 | | 4 | 41 | | 5 | 27 | | 6 | 87 | | 7 | 74 | | 8 | 14 | | 9 | 14 | | 10 | 15 | | 11 | 47 | | 12 | 26 | | 13 | 13 | | 14 | 34 | | 15 | 27 | | 16 | 37 | | 17 | 34 | | 18 | 7 | | 19 | 21 | | 20 | 21 | | 21 | 46 | | 22 | 17 | | 23 | 25 | | 24 | 13 | | 25 | 30 | | 26 | 34 | | 27 | 37 | | 28 | 26 | | 29 | 58 | | 30 | 58 | | 31 | 15 | | 32 | 24 | | 33 | 19 | | 34 | 26 | | 35 | 29 | | 36 | 25 | | 37 | 25 | | 38 | 57 | | 39 | 31 | | 40 | 4 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 104 | | matches | | 0 | "was sharpened" | | 1 | "was gone" | | 2 | "was meant" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 165 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 980 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 20 | | adverbRatio | 0.02040816326530612 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009183673469387756 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 10.64 | | std | 6.94 | | cv | 0.652 | | sampleLengths | | 0 | 23 | | 1 | 23 | | 2 | 18 | | 3 | 12 | | 4 | 1 | | 5 | 3 | | 6 | 13 | | 7 | 25 | | 8 | 5 | | 9 | 5 | | 10 | 3 | | 11 | 13 | | 12 | 10 | | 13 | 8 | | 14 | 1 | | 15 | 4 | | 16 | 13 | | 17 | 13 | | 18 | 17 | | 19 | 3 | | 20 | 23 | | 21 | 1 | | 22 | 17 | | 23 | 5 | | 24 | 22 | | 25 | 6 | | 26 | 9 | | 27 | 5 | | 28 | 12 | | 29 | 15 | | 30 | 15 | | 31 | 12 | | 32 | 13 | | 33 | 10 | | 34 | 13 | | 35 | 1 | | 36 | 1 | | 37 | 13 | | 38 | 16 | | 39 | 20 | | 40 | 10 | | 41 | 4 | | 42 | 9 | | 43 | 5 | | 44 | 11 | | 45 | 4 | | 46 | 8 | | 47 | 20 | | 48 | 4 | | 49 | 4 |
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| 50.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3464566929133858 | | totalSentences | 127 | | uniqueOpeners | 44 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Then, just as quickly, it" |
| | ratio | 0.011 | |
| 76.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 95 | | matches | | 0 | "It was a smell she’d" | | 1 | "She spotted him immediately, a" | | 2 | "He wasn’t alone." | | 3 | "She didn’t want to interrupt." | | 4 | "She just wanted a whiskey." | | 5 | "It had been years." | | 6 | "She froze, her hand hovering" | | 7 | "It couldn’t be." | | 8 | "She turned slowly, her gaze" | | 9 | "She was taller now, or" | | 10 | "Her frame was lean, almost" | | 11 | "She moved with a predatory" | | 12 | "He took a slow sip" | | 13 | "He didn't introduce them." | | 14 | "He didn't need to." | | 15 | "She felt like an actress" | | 16 | "She could feel Silas’s gaze" | | 17 | "It wasn't a compliment." | | 18 | "It was an assessment." | | 19 | "She was on autopilot, her" |
| | ratio | 0.358 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 95 | | matches | | 0 | "The green neon of The" | | 1 | "Rory pushed the heavy oak" | | 2 | "It was a smell she’d" | | 3 | "She spotted him immediately, a" | | 4 | "He wasn’t alone." | | 5 | "A woman sat opposite him" | | 6 | "Rory hung her damp jacket" | | 7 | "She didn’t want to interrupt." | | 8 | "She just wanted a whiskey." | | 9 | "A large one." | | 10 | "Silas’s voice, a low rumble" | | 11 | "The name hit Rory like" | | 12 | "It had been years." | | 13 | "A lifetime ago, in a" | | 14 | "She froze, her hand hovering" | | 15 | "The woman in the booth" | | 16 | "It couldn’t be." | | 17 | "The voice was the same," | | 18 | "The soft Welsh lilt was" | | 19 | "Rory’s heart hammered against her" |
| | ratio | 0.937 | |
| 52.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 95 | | matches | | 0 | "Before she could bring it" |
| | ratio | 0.011 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "She moved with a predatory grace that was completely alien to the girl Rory remembered." | | 1 | "The girl who’d helped Rory pack her single bag when she fled Cardiff." | | 2 | "She was on autopilot, her mind racing, trying to reconcile the ghost from her past with the sharp, dangerous woman sitting across from her." |
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| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 4 | | matches | | 0 | "Laila said, her eyes scanning Aurora’s face, her simple jeans and jumper" | | 1 | "Aurora shot back, her defensiveness flaring" | | 2 | "Aurora said, her voice barely a whisper" | | 3 | "Silas replied, his voice gentle" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Laila corrected (correct)" | | 1 | "Laila repeated (repeat)" |
| | dialogueSentences | 45 | | tagDensity | 0.156 | | leniency | 0.311 | | rawRatio | 0.286 | | effectiveRatio | 0.089 | |