| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1296 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 34.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1296 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "electric" | | 1 | "rhythmic" | | 2 | "tracing" | | 3 | "scanned" | | 4 | "constructed" | | 5 | "warmth" | | 6 | "predator" | | 7 | "intensity" | | 8 | "weight" | | 9 | "charged" | | 10 | "pulsed" | | 11 | "flicker" | | 12 | "perfect" | | 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 | 0 | | narrationSentences | 100 | | matches | (empty) | |
| 57.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 100 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "managed to" | | 2 | "begin to" | | 3 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1294 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1294 | | uniqueNames | 10 | | maxNameDensity | 0.62 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | London | 2 | | Raven | 1 | | Nest | 2 | | Cardiff | 2 | | Silas | 8 | | Evan | 8 | | Rory | 2 | | Aurora | 5 | | Soho | 1 | | You | 7 |
| | persons | | 0 | "Silas" | | 1 | "Evan" | | 2 | "Rory" | | 3 | "Aurora" | | 4 | "You" |
| | places | | 0 | "London" | | 1 | "Raven" | | 2 | "Cardiff" | | 3 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.773 | | wordCount | 1294 | | matches | | 0 | "not with the fear that had once paralyzed her, but with a cold, clear assessment of his limitations" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 100 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 41.74 | | std | 21.47 | | cv | 0.514 | | sampleLengths | | 0 | 68 | | 1 | 78 | | 2 | 60 | | 3 | 1 | | 4 | 65 | | 5 | 11 | | 6 | 72 | | 7 | 14 | | 8 | 58 | | 9 | 26 | | 10 | 58 | | 11 | 22 | | 12 | 73 | | 13 | 34 | | 14 | 27 | | 15 | 21 | | 16 | 50 | | 17 | 42 | | 18 | 59 | | 19 | 49 | | 20 | 35 | | 21 | 25 | | 22 | 62 | | 23 | 38 | | 24 | 58 | | 25 | 12 | | 26 | 63 | | 27 | 8 | | 28 | 39 | | 29 | 23 | | 30 | 43 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 100 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 213 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 100 | | ratio | 0.01 | | matches | | 0 | "She leaned over the counter, the scent of the bar—bitter, aged, and real—filling her lungs." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1298 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.02773497688751926 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023112480739599386 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 12.94 | | std | 6.15 | | cv | 0.475 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 11 | | 3 | 19 | | 4 | 7 | | 5 | 19 | | 6 | 15 | | 7 | 14 | | 8 | 8 | | 9 | 22 | | 10 | 11 | | 11 | 17 | | 12 | 16 | | 13 | 16 | | 14 | 1 | | 15 | 10 | | 16 | 5 | | 17 | 13 | | 18 | 16 | | 19 | 21 | | 20 | 11 | | 21 | 14 | | 22 | 22 | | 23 | 14 | | 24 | 22 | | 25 | 6 | | 26 | 8 | | 27 | 11 | | 28 | 26 | | 29 | 21 | | 30 | 5 | | 31 | 21 | | 32 | 16 | | 33 | 8 | | 34 | 19 | | 35 | 15 | | 36 | 6 | | 37 | 16 | | 38 | 11 | | 39 | 15 | | 40 | 25 | | 41 | 22 | | 42 | 9 | | 43 | 5 | | 44 | 20 | | 45 | 10 | | 46 | 17 | | 47 | 10 | | 48 | 11 | | 49 | 19 |
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| 28.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.28 | | totalSentences | 100 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 96 | | matches | | 0 | "She didn't look up when" | | 1 | "His left leg dragged just" | | 2 | "She had spent months trying" | | 3 | "He wore a coat that" | | 4 | "He scanned the bar with" | | 5 | "She didn't turn at first." | | 6 | "She gripped the edge of" | | 7 | "She looked at Silas, but" | | 8 | "You should have stayed where" | | 9 | "She turned then, meeting the" | | 10 | "He stood too close, invading" | | 11 | "He smelled of damp wool" | | 12 | "I found my own sun," | | 13 | "It burns a lot brighter" | | 14 | "He reached for a stool," | | 15 | "He didn't look at Silas," | | 16 | "She poured a finger of" | | 17 | "It hit his hand with" | | 18 | "He didn't intervene, but his" | | 19 | "He took a sip of" |
| | ratio | 0.583 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 96 | | matches | | 0 | "The green neon sign hummed," | | 1 | "Aurora wiped the condensation from" | | 2 | "She didn't look up when" | | 3 | "The bell gave a single," | | 4 | "Silas leaned against the mahogany," | | 5 | "His left leg dragged just" | | 6 | "Aurora set the glass down," | | 7 | "The wood grain felt rough" | | 8 | "She had spent months trying" | | 9 | "A man peeled himself away" | | 10 | "He wore a coat that" | | 11 | "He scanned the bar with" | | 12 | "The name hit the back" | | 13 | "She didn't turn at first." | | 14 | "She gripped the edge of" | | 15 | "She looked at Silas, but" | | 16 | "The stranger moved forward, his" | | 17 | "You should have stayed where" | | 18 | "She turned then, meeting the" | | 19 | "Evan looked older, the sharpness" |
| | ratio | 0.875 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 96 | | matches | | 0 | "Even in this, this dump," | | 1 | "Before you decided that running" |
| | ratio | 0.021 | |
| 70.39% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 7 | | matches | | 0 | "The green neon sign hummed, a persistent, electric buzz that bled into the damp London air." | | 1 | "The stranger moved forward, his shoes clicking with a sharp, expensive precision that silenced the low hum of the ceiling fans." | | 2 | "He reached for a stool, his frame sprawling across the leather as if he still owned the furniture, the room, and the woman behind the counter." | | 3 | "Or did you just get tired of being the brightest mind in a room filled with people who couldn't spell justice?" | | 4 | "He looked around the Nest, his gaze lingering on the faded maps and the grainy photographs of men who hadn't walked the earth in decades." | | 5 | "She looked at him, not with the fear that had once paralyzed her, but with a cold, clear assessment of his limitations." | | 6 | "Aurora stood still, listening to the rain begin to lash against the windows, a rhythmic drumming that drowned out the city’s distant roar." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |