| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Aurora looked directly [directly]" |
| | dialogueSentences | 39 | | tagDensity | 0.487 | | leniency | 0.974 | | rawRatio | 0.053 | | effectiveRatio | 0.051 | |
| 89.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1420 | | totalAiIsmAdverbs | 3 | | 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) | |
| 61.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1420 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "tracing" | | 1 | "depths" | | 2 | "scanned" | | 3 | "processed" | | 4 | "familiar" | | 5 | "lilt" | | 6 | "eyebrow" | | 7 | "warmth" | | 8 | "vibrated" | | 9 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 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 | 0 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1420 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1009 | | uniqueNames | 13 | | maxNameDensity | 1.68 | | worstName | "Evan" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 11 | | Blackwood | 1 | | London | 1 | | Carter | 1 | | Soho | 2 | | Welsh | 1 | | Evan | 17 | | Aurora | 15 | | Wardour | 1 | | Street | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Carter" | | 5 | "Evan" | | 6 | "Aurora" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "Cardiff" |
| | globalScore | 0.658 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed entirely irrelevant to the sheer physical presence he projected" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.704 | | wordCount | 1420 | | matches | | 0 | "not the human rights stuff we used to talk about in the Cardiff union building, but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 31.56 | | std | 19.84 | | cv | 0.629 | | sampleLengths | | 0 | 86 | | 1 | 54 | | 2 | 28 | | 3 | 56 | | 4 | 16 | | 5 | 1 | | 6 | 60 | | 7 | 1 | | 8 | 30 | | 9 | 48 | | 10 | 36 | | 11 | 11 | | 12 | 25 | | 13 | 19 | | 14 | 31 | | 15 | 40 | | 16 | 36 | | 17 | 48 | | 18 | 6 | | 19 | 49 | | 20 | 7 | | 21 | 30 | | 22 | 46 | | 23 | 15 | | 24 | 24 | | 25 | 70 | | 26 | 49 | | 27 | 18 | | 28 | 19 | | 29 | 4 | | 30 | 34 | | 31 | 14 | | 32 | 37 | | 33 | 41 | | 34 | 51 | | 35 | 42 | | 36 | 11 | | 37 | 2 | | 38 | 8 | | 39 | 49 | | 40 | 54 | | 41 | 28 | | 42 | 29 | | 43 | 8 | | 44 | 49 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 84 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 172 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 838 | | adjectiveStacks | 3 | | stackExamples | | 0 | "cold, diesel-scented Soho" | | 1 | "thick, curly brown hair" | | 2 | "single, grey-streaked eyebrow" |
| | adverbCount | 23 | | adverbRatio | 0.027446300715990454 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00954653937947494 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 13.52 | | std | 7.72 | | cv | 0.571 | | sampleLengths | | 0 | 23 | | 1 | 21 | | 2 | 28 | | 3 | 14 | | 4 | 16 | | 5 | 10 | | 6 | 28 | | 7 | 7 | | 8 | 21 | | 9 | 13 | | 10 | 23 | | 11 | 20 | | 12 | 3 | | 13 | 13 | | 14 | 1 | | 15 | 2 | | 16 | 7 | | 17 | 17 | | 18 | 28 | | 19 | 6 | | 20 | 1 | | 21 | 17 | | 22 | 13 | | 23 | 25 | | 24 | 23 | | 25 | 16 | | 26 | 20 | | 27 | 11 | | 28 | 5 | | 29 | 11 | | 30 | 9 | | 31 | 8 | | 32 | 11 | | 33 | 19 | | 34 | 12 | | 35 | 22 | | 36 | 18 | | 37 | 12 | | 38 | 12 | | 39 | 4 | | 40 | 8 | | 41 | 4 | | 42 | 30 | | 43 | 14 | | 44 | 3 | | 45 | 3 | | 46 | 15 | | 47 | 34 | | 48 | 7 | | 49 | 8 |
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| 41.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3047619047619048 | | totalSentences | 105 | | uniqueOpeners | 32 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 76 | | matches | | 0 | "Only when Aurora nodded again" | | 1 | "Slowly, Evan lowered his hand." |
| | ratio | 0.026 | |
| 51.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 76 | | matches | | 0 | "His left leg, stiff from" | | 1 | "She stared into the amber" | | 2 | "He had thick, curly brown" | | 3 | "He scanned the dim interior," | | 4 | "His breath hitched." | | 5 | "He took three fast strides" | | 6 | "Her grip tightened around the" | | 7 | "She slowly turned her head," | | 8 | "He settled for gripping the" | | 9 | "His voice carried the familiar," | | 10 | "He didn't look at Evan," | | 11 | "Her voice lacked the warmth" | | 12 | "He pulled it back, shoving" | | 13 | "He looked down at his" | | 14 | "She took a sip of" | | 15 | "He slid onto the stool" | | 16 | "He gestured to Silas." | | 17 | "He held Evan’s gaze for" | | 18 | "He wiped his mouth with" | | 19 | "She leaned back, creating a" |
| | ratio | 0.421 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 76 | | matches | | 0 | "Neon green light from the" | | 1 | "The Raven's Nest smelled of" | | 2 | "His left leg, stiff from" | | 3 | "Aurora Carter sat three stools" | | 4 | "She stared into the amber" | | 5 | "The shoulder-length black hair she" | | 6 | "The brass bells above the" | | 7 | "A gust of cold, diesel-scented" | | 8 | "A man stepped through the" | | 9 | "He had thick, curly brown" | | 10 | "He scanned the dim interior," | | 11 | "His breath hitched." | | 12 | "He took three fast strides" | | 13 | "Her grip tightened around the" | | 14 | "She slowly turned her head," | | 15 | "The softness of the cheeks" | | 16 | "Evan stopped two feet away," | | 17 | "He settled for gripping the" | | 18 | "His voice carried the familiar," | | 19 | "Silas set the polished highball" |
| | ratio | 0.921 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 76 | | matches | | 0 | "Now, it just looked messy." | | 1 | "Before his fingers could make" |
| | ratio | 0.026 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "He had thick, curly brown hair plastered to his forehead and a wide, boyish face that didn't belong in a place this tired." | | 1 | "The softness of the cheeks was gone, replaced by a sharp jawline and the faint, permanent creases of someone who spent too much time squinting at computer scree…" | | 2 | "His voice was quiet, but it filled the room, carrying the weight of a man who had commanded far worse rooms than this one." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, his voice barely audible over the hum" |
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| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "Aurora repeated (repeat)" | | 1 | "he whispered (whisper)" | | 2 | "Evan muttered (mutter)" |
| | dialogueSentences | 39 | | tagDensity | 0.256 | | leniency | 0.513 | | rawRatio | 0.3 | | effectiveRatio | 0.154 | |