| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 99 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2146 | | 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) | |
| 65.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2146 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "eyebrow" | | 2 | "weight" | | 3 | "flicked" | | 4 | "unreadable" | | 5 | "tracing" | | 6 | "silence" | | 7 | "pulse" | | 8 | "warmth" |
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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 | 178 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 178 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 259 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2146 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 105 | | wordCount | 1668 | | uniqueNames | 10 | | maxNameDensity | 3.18 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Rory" | | discoveredNames | | Carter | 1 | | Cardiff | 2 | | Eva | 32 | | Raven | 1 | | Nest | 1 | | Rory | 53 | | University | 1 | | Evan | 5 | | London | 1 | | Silas | 8 |
| | persons | | 0 | "Carter" | | 1 | "Eva" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Rory" | | 5 | "Evan" | | 6 | "Silas" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like the kind of place that kept s" | | 1 | "looked like a door closing" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.466 | | wordCount | 2146 | | matches | | 0 | "not just at the polish and the new geometry of her face, but at the tiredness gathered beneath it, carefully hidden" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 259 | | matches | | 0 | "heard that voice" | | 1 | "had that talent" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 151 | | mean | 14.21 | | std | 17.21 | | cv | 1.211 | | sampleLengths | | 0 | 3 | | 1 | 40 | | 2 | 15 | | 3 | 9 | | 4 | 11 | | 5 | 59 | | 6 | 86 | | 7 | 4 | | 8 | 10 | | 9 | 1 | | 10 | 79 | | 11 | 21 | | 12 | 57 | | 13 | 10 | | 14 | 33 | | 15 | 4 | | 16 | 3 | | 17 | 5 | | 18 | 5 | | 19 | 84 | | 20 | 4 | | 21 | 9 | | 22 | 3 | | 23 | 8 | | 24 | 9 | | 25 | 24 | | 26 | 5 | | 27 | 7 | | 28 | 32 | | 29 | 4 | | 30 | 5 | | 31 | 3 | | 32 | 4 | | 33 | 11 | | 34 | 34 | | 35 | 6 | | 36 | 10 | | 37 | 1 | | 38 | 1 | | 39 | 24 | | 40 | 6 | | 41 | 15 | | 42 | 34 | | 43 | 6 | | 44 | 33 | | 45 | 3 | | 46 | 4 | | 47 | 6 | | 48 | 10 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 178 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 258 | | matches | | 0 | "was doing" | | 1 | "was already moving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 259 | | ratio | 0 | | matches | (empty) | |
| 98.96% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1675 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 69 | | adverbRatio | 0.04119402985074627 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.007164179104477612 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 259 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 259 | | mean | 8.29 | | std | 7.3 | | cv | 0.881 | | sampleLengths | | 0 | 3 | | 1 | 17 | | 2 | 23 | | 3 | 15 | | 4 | 9 | | 5 | 11 | | 6 | 17 | | 7 | 42 | | 8 | 19 | | 9 | 8 | | 10 | 6 | | 11 | 12 | | 12 | 41 | | 13 | 3 | | 14 | 1 | | 15 | 6 | | 16 | 4 | | 17 | 1 | | 18 | 24 | | 19 | 18 | | 20 | 6 | | 21 | 11 | | 22 | 20 | | 23 | 21 | | 24 | 36 | | 25 | 21 | | 26 | 10 | | 27 | 6 | | 28 | 10 | | 29 | 17 | | 30 | 4 | | 31 | 3 | | 32 | 5 | | 33 | 5 | | 34 | 10 | | 35 | 5 | | 36 | 2 | | 37 | 3 | | 38 | 3 | | 39 | 7 | | 40 | 31 | | 41 | 8 | | 42 | 15 | | 43 | 4 | | 44 | 9 | | 45 | 3 | | 46 | 2 | | 47 | 6 | | 48 | 9 | | 49 | 11 |
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| 41.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.2857142857142857 | | totalSentences | 259 | | uniqueOpeners | 74 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 142 | | matches | | 0 | "Then London had swallowed everything" | | 1 | "Somewhere near the back, a" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 142 | | matches | | 0 | "She set the glass down" | | 1 | "She laughed before she could" | | 2 | "She had cut her hair" | | 3 | "Her coat was stiff and" | | 4 | "Her shoulders felt harder under" | | 5 | "They held each other for" | | 6 | "His hazel eyes flicked from" | | 7 | "he said, and moved down" | | 8 | "She held it up." | | 9 | "They lurked there, but they" | | 10 | "Her gaze did not move" | | 11 | "He knew better than to" | | 12 | "Her reflection looked back at" | | 13 | "She looked at Eva again," | | 14 | "She knew the rhythm of" | | 15 | "They never arrived alone." | | 16 | "They entered the room with" | | 17 | "He rested both hands on" | | 18 | "Her thumb brushed the edge." | | 19 | "It showed a man in" |
| | ratio | 0.197 | |
| 16.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 142 | | matches | | 0 | "Aurora Carter froze with a" | | 1 | "A familiar voice, roughened by" | | 2 | "She set the glass down" | | 3 | "She laughed before she could" | | 4 | "The last time she’d heard" | | 5 | "Eva stood near the dartboard" | | 6 | "She had cut her hair" | | 7 | "Copper rings flashed from one" | | 8 | "A pale line cut through" | | 9 | "Rory had to look twice" | | 10 | "Eva’s mouth pulled to one" | | 11 | "Silas, wiping a glass behind" | | 12 | "The green neon sign outside" | | 13 | "The Raven’s Nest had always" | | 14 | "Rory slid off her stool" | | 15 | "Eva smelled of cold air" | | 16 | "Rory stopped short, uncertain whether" | | 17 | "Eva chose for both of" | | 18 | "Her coat was stiff and" | | 19 | "Her shoulders felt harder under" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 6 | | matches | | 0 | "The last time she’d heard that voice, it had come from a freezing flat in Cardiff with the radiator clanking like a bad throat and two girls with chipped nail v…" | | 1 | "Eva stood near the dartboard in a black coat that looked expensive enough to be stolen rather than bought." | | 2 | "The Raven’s Nest had always looked like the kind of place that kept secrets in the joints of its furniture." | | 3 | "Not the old wild certainty that had once made Eva drag Rory onto train platforms at midnight and insist they could catch any life they wanted if they ran fast e…" | | 4 | "Eva had been the first person Rory told about Cardiff University, the second person she told about Evan, the first person who had looked at Rory’s bruises and n…" | | 5 | "Evan with the same neat cruelty in his face that had once made Rory feel small enough to live inside a pocket." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 99 | | tagDensity | 0.162 | | leniency | 0.323 | | rawRatio | 0 | | effectiveRatio | 0 | |