| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "He glanced around [around]" |
| | dialogueSentences | 43 | | tagDensity | 0.488 | | leniency | 0.977 | | rawRatio | 0.048 | | effectiveRatio | 0.047 | |
| 84.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1896 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "quickly" | | 1 | "softly" | | 2 | "slightly" | | 3 | "carefully" | | 4 | "slowly" | | 5 | "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) | |
| 73.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1896 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "familiar" | | 1 | "flicked" | | 2 | "silence" | | 3 | "weight" | | 4 | "tension" | | 5 | "could feel" | | 6 | "measured" |
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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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 131 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1896 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1579 | | uniqueNames | 11 | | maxNameDensity | 0.63 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rhys" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Prague | 1 | | Silas | 9 | | Rory | 10 | | Rhys | 8 | | Eva | 2 | | Cardiff | 2 | | Evan | 1 | | London | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Rhys" | | 5 | "Eva" | | 6 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Cardiff" | | 3 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | glossingSentenceCount | 1 | | matches | | 0 | "as if offering an apology" |
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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 | 1896 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 37.18 | | std | 29.46 | | cv | 0.793 | | sampleLengths | | 0 | 83 | | 1 | 91 | | 2 | 46 | | 3 | 92 | | 4 | 3 | | 5 | 61 | | 6 | 18 | | 7 | 17 | | 8 | 8 | | 9 | 74 | | 10 | 9 | | 11 | 6 | | 12 | 73 | | 13 | 59 | | 14 | 13 | | 15 | 3 | | 16 | 35 | | 17 | 4 | | 18 | 27 | | 19 | 50 | | 20 | 4 | | 21 | 48 | | 22 | 30 | | 23 | 44 | | 24 | 21 | | 25 | 9 | | 26 | 106 | | 27 | 33 | | 28 | 34 | | 29 | 1 | | 30 | 59 | | 31 | 96 | | 32 | 8 | | 33 | 6 | | 34 | 46 | | 35 | 7 | | 36 | 3 | | 37 | 73 | | 38 | 29 | | 39 | 30 | | 40 | 14 | | 41 | 26 | | 42 | 56 | | 43 | 6 | | 44 | 45 | | 45 | 24 | | 46 | 53 | | 47 | 4 | | 48 | 76 | | 49 | 78 |
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| 99.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 131 | | matches | | 0 | "being asked" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 278 | | matches | | 0 | "was already reaching" | | 1 | "was taking" | | 2 | "wasn’t passing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 96.46% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 999 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.044044044044044044 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007007007007007007 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 12.31 | | std | 10.07 | | cv | 0.818 | | sampleLengths | | 0 | 26 | | 1 | 27 | | 2 | 8 | | 3 | 3 | | 4 | 19 | | 5 | 8 | | 6 | 9 | | 7 | 16 | | 8 | 31 | | 9 | 27 | | 10 | 16 | | 11 | 12 | | 12 | 7 | | 13 | 11 | | 14 | 11 | | 15 | 31 | | 16 | 14 | | 17 | 11 | | 18 | 25 | | 19 | 3 | | 20 | 14 | | 21 | 1 | | 22 | 4 | | 23 | 1 | | 24 | 37 | | 25 | 4 | | 26 | 4 | | 27 | 4 | | 28 | 10 | | 29 | 14 | | 30 | 3 | | 31 | 7 | | 32 | 1 | | 33 | 22 | | 34 | 9 | | 35 | 16 | | 36 | 16 | | 37 | 11 | | 38 | 9 | | 39 | 6 | | 40 | 6 | | 41 | 35 | | 42 | 9 | | 43 | 23 | | 44 | 7 | | 45 | 9 | | 46 | 8 | | 47 | 27 | | 48 | 8 | | 49 | 12 |
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| 49.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.33766233766233766 | | totalSentences | 154 | | uniqueOpeners | 52 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 115 | | matches | | 0 | "Then he turned." | | 1 | "Instead she looked at the" | | 2 | "Perhaps he did." | | 3 | "Then the moment passed, as" | | 4 | "Then he stepped out into" | | 5 | "Somewhere above them, in the" |
| | ratio | 0.052 | |
| 49.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 115 | | matches | | 0 | "He didn’t ask." | | 1 | "His silver signet ring flashed" | | 2 | "He knew better than to" | | 3 | "She had just lifted the" | | 4 | "He was tall, leaner than" | | 5 | "His hair, once a perpetual" | | 6 | "He ordered a whisky without" | | 7 | "She had kept walking." | | 8 | "His eyes found her." | | 9 | "He said it quietly, as" | | 10 | "She set the glass down" | | 11 | "He crossed the space between" | | 12 | "He took the stool beside" | | 13 | "He set it down without" | | 14 | "He moved down the bar," | | 15 | "He looked expensive and tired" | | 16 | "He searched for the word" | | 17 | "He glanced around the bar," | | 18 | "She could feel the small" | | 19 | "She kept her sleeve down." |
| | ratio | 0.426 | |
| 42.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 115 | | matches | | 0 | "The rain had followed her" | | 1 | "Rory slid onto the stool" | | 2 | "Silas was already reaching for" | | 3 | "He didn’t ask." | | 4 | "The dark ale landed in" | | 5 | "The Raven’s Nest held its" | | 6 | "The green neon from the" | | 7 | "Silas moved with that slight" | | 8 | "His silver signet ring flashed" | | 9 | "Hazel eyes flicked to her," | | 10 | "He knew better than to" | | 11 | "She had just lifted the" | | 12 | "A gust of wet air," | | 13 | "He was tall, leaner than" | | 14 | "His hair, once a perpetual" | | 15 | "He ordered a whisky without" | | 16 | "Rory felt the recognition like" | | 17 | "The last time she had" | | 18 | "She had kept walking." | | 19 | "His eyes found her." |
| | ratio | 0.835 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 115 | | matches | (empty) | | ratio | 0 | |
| 15.31% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 10 | | matches | | 0 | "Black-and-white photographs watched from between them: men in overcoats on rain-slick streets, a woman laughing with a cigarette, a city square that might have …" | | 1 | "He ordered a whisky without looking around, voice low and certain, the kind that expected to be heard in rooms with better lighting than this." | | 2 | "The last time she had seen him he had been shouting her name across a rain-lashed courtyard outside the law building, trying to make her stay for one more pint,…" | | 3 | "She could feel the small crescent scar on her left wrist against the cool wood of the bar, the one from the childhood fall that had never quite faded." | | 4 | "It was crowded with the versions of themselves they had left in Cardiff: the girl who underlined case law in different colours, the boy who believed the system …" | | 5 | "She thought of Evan, of the way the apartment had smelled of his aftershave and her own fear, of the night she had packed a single bag while he slept and taken …" | | 6 | "The question came out carefully, as if he had been turning it over for the last ten minutes." | | 7 | "He smiled then, a real one, small and startled, the ghost of the boy who used to steal her highlighter pens and leave terrible jokes in the margins of her notes…" | | 8 | "Somewhere above them, in the flat that smelled of old wood and the faint spice of the restaurant downstairs, her life waited: the next delivery, the next quiet …" | | 9 | "Outside, London moved on, indifferent and vast, and the rain kept falling as if it had all the time in the world." |
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| 77.38% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 2 | | matches | | 0 | "He said, as if the name itself might be a mistake" | | 1 | "he said, as if offering an apology" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 43 | | tagDensity | 0.256 | | leniency | 0.512 | | rawRatio | 0.091 | | effectiveRatio | 0.047 | |