| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 2 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" | | 1 | "he said quietly [quietly]" |
| | dialogueSentences | 56 | | tagDensity | 0.411 | | leniency | 0.821 | | rawRatio | 0.087 | | effectiveRatio | 0.071 | |
| 81.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 1 |
| | 4 | |
| | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "slowly" | | 3 | "deliberately" | | 4 | "really" |
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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) | |
| 77.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "calculate" | | 2 | "familiar" | | 3 | "silence" | | 4 | "traced" | | 5 | "crystal" |
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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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1337 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 896 | | uniqueNames | 7 | | maxNameDensity | 0.78 | | worstName | "Declan" | | maxWindowNameDensity | 2 | | worstWindowName | "Declan" | | discoveredNames | | London | 1 | | Tuesday | 1 | | Raven | 1 | | Liverpool | 1 | | Silas | 2 | | Declan | 7 | | Rory | 4 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Declan" | | 3 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 4 | | matches | | 0 | "quite fit the Raven’s usual crowd" | | 1 | "tasted like smoke and cheap wine and laug" | | 2 | "Not quite admiration, not quite accusation" | | 3 | "not quite accusation" | | 4 | "looked like he might argue, might push, m" |
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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 | 1337 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 114 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 21.56 | | std | 19.84 | | cv | 0.92 | | sampleLengths | | 0 | 51 | | 1 | 6 | | 2 | 84 | | 3 | 5 | | 4 | 2 | | 5 | 60 | | 6 | 1 | | 7 | 26 | | 8 | 27 | | 9 | 67 | | 10 | 36 | | 11 | 13 | | 12 | 16 | | 13 | 2 | | 14 | 17 | | 15 | 26 | | 16 | 3 | | 17 | 46 | | 18 | 23 | | 19 | 15 | | 20 | 18 | | 21 | 26 | | 22 | 7 | | 23 | 9 | | 24 | 10 | | 25 | 3 | | 26 | 4 | | 27 | 43 | | 28 | 27 | | 29 | 2 | | 30 | 6 | | 31 | 2 | | 32 | 29 | | 33 | 3 | | 34 | 1 | | 35 | 10 | | 36 | 54 | | 37 | 14 | | 38 | 27 | | 39 | 10 | | 40 | 39 | | 41 | 32 | | 42 | 22 | | 43 | 56 | | 44 | 6 | | 45 | 6 | | 46 | 41 | | 47 | 11 | | 48 | 28 | | 49 | 40 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "was testing" | | 1 | "was working" |
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| 92.73% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 114 | | ratio | 0.018 | | matches | | 0 | "Not with age—he wasn’t that old, none of them were that old—but with the weight of years unspooled between them." | | 1 | "The rain had stopped hammering against the windows; the city had settled into its late-night hum." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 372 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.03225806451612903 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002688172043010753 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 11.73 | | std | 9.02 | | cv | 0.769 | | sampleLengths | | 0 | 19 | | 1 | 32 | | 2 | 6 | | 3 | 24 | | 4 | 28 | | 5 | 16 | | 6 | 16 | | 7 | 5 | | 8 | 2 | | 9 | 6 | | 10 | 19 | | 11 | 4 | | 12 | 31 | | 13 | 1 | | 14 | 6 | | 15 | 20 | | 16 | 11 | | 17 | 16 | | 18 | 17 | | 19 | 15 | | 20 | 9 | | 21 | 5 | | 22 | 21 | | 23 | 12 | | 24 | 24 | | 25 | 8 | | 26 | 5 | | 27 | 14 | | 28 | 2 | | 29 | 2 | | 30 | 11 | | 31 | 6 | | 32 | 10 | | 33 | 12 | | 34 | 4 | | 35 | 3 | | 36 | 18 | | 37 | 11 | | 38 | 17 | | 39 | 23 | | 40 | 15 | | 41 | 12 | | 42 | 6 | | 43 | 20 | | 44 | 6 | | 45 | 2 | | 46 | 1 | | 47 | 4 | | 48 | 9 | | 49 | 10 |
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| 56.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3684210526315789 | | totalSentences | 114 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 68 | | matches | | 0 | "She looked up out of" | | 1 | "He was lean, sharp-jawed, with" | | 2 | "His eyes swept the room" | | 3 | "She knew that face." | | 4 | "His voice cracked on the" | | 5 | "She said it flat, testing" | | 6 | "It tasted like smoke and" | | 7 | "He crossed the distance in" | | 8 | "He ran a hand through" | | 9 | "She didn’t have to calculate" | | 10 | "She had counted every one." | | 11 | "He repeated it like he" | | 12 | "he finished, and there was" | | 13 | "She set the rag down" | | 14 | "She poured two whiskeys, the" | | 15 | "He gestured vaguely at the" | | 16 | "He said it like she’d" | | 17 | "She lifted her glass, let" | | 18 | "She’d aimed for that." | | 19 | "She didn’t bother hiding the" |
| | ratio | 0.5 | |
| 33.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 68 | | matches | | 0 | "The rain had settled into" | | 1 | "Rory had been wiping down" | | 2 | "The bell above the door" | | 3 | "She looked up out of" | | 4 | "The man who stepped through" | | 5 | "He was lean, sharp-jawed, with" | | 6 | "His eyes swept the room" | | 7 | "Those eyes landed on her." | | 8 | "Rory’s hand stilled on the" | | 9 | "She knew that face." | | 10 | "His voice cracked on the" | | 11 | "She said it flat, testing" | | 12 | "It tasted like smoke and" | | 13 | "He crossed the distance in" | | 14 | "The crow’s feet at the" | | 15 | "The silver in his stubble." | | 16 | "The way he held himself" | | 17 | "He ran a hand through" | | 18 | "She didn’t have to calculate" | | 19 | "She had counted every one." |
| | ratio | 0.853 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 3 | | matches | | 0 | "The man who stepped through shook water from his coat, a dark wool thing that looked expensive in a way that didn't quite fit the Raven’s usual crowd." | | 1 | "Knew it from another lifetime, from Liverpool street corners and university libraries and a version of herself she’d buried so deep she almost didn’t recognise …" | | 2 | "They drank, and the rain started up again outside, and somewhere in the back room Silas turned up the volume on his ancient radio, filling the space with someth…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 56 | | tagDensity | 0.196 | | leniency | 0.393 | | rawRatio | 0.091 | | effectiveRatio | 0.036 | |