| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1283 | | 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) | |
| 84.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1283 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "footsteps" | | 1 | "could feel" | | 2 | "flicked" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1283 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 807 | | uniqueNames | 13 | | maxNameDensity | 0.87 | | worstName | "Dai" | | maxWindowNameDensity | 2 | | worstWindowName | "Dai" | | discoveredNames | | Tuesday | 1 | | Raven | 1 | | Nest | 1 | | Dai | 7 | | Cardiff | 2 | | Silas | 5 | | London | 1 | | Queen | 1 | | Street | 1 | | Twice | 1 | | Soho | 2 | | Evan | 2 | | Rory | 5 |
| | persons | | 0 | "Raven" | | 1 | "Dai" | | 2 | "Silas" | | 3 | "Queen" | | 4 | "Evan" | | 5 | "Rory" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Street" | | 3 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 97.92% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared behind the bar the way he always appeared, without announcement, polishing a glass that was already clean" |
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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 | 1283 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 112 | | matches | | 0 | "was that kind" | | 1 | "learned that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 23.33 | | std | 22.6 | | cv | 0.969 | | sampleLengths | | 0 | 5 | | 1 | 84 | | 2 | 9 | | 3 | 8 | | 4 | 61 | | 5 | 8 | | 6 | 4 | | 7 | 33 | | 8 | 7 | | 9 | 32 | | 10 | 8 | | 11 | 4 | | 12 | 52 | | 13 | 21 | | 14 | 8 | | 15 | 18 | | 16 | 1 | | 17 | 15 | | 18 | 7 | | 19 | 32 | | 20 | 39 | | 21 | 5 | | 22 | 13 | | 23 | 4 | | 24 | 65 | | 25 | 5 | | 26 | 57 | | 27 | 4 | | 28 | 11 | | 29 | 50 | | 30 | 2 | | 31 | 1 | | 32 | 1 | | 33 | 17 | | 34 | 3 | | 35 | 38 | | 36 | 50 | | 37 | 7 | | 38 | 47 | | 39 | 7 | | 40 | 47 | | 41 | 7 | | 42 | 65 | | 43 | 66 | | 44 | 9 | | 45 | 72 | | 46 | 15 | | 47 | 14 | | 48 | 51 | | 49 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 146 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 482 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.029045643153526972 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008298755186721992 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 11.46 | | std | 11.12 | | cv | 0.971 | | sampleLengths | | 0 | 5 | | 1 | 51 | | 2 | 12 | | 3 | 8 | | 4 | 7 | | 5 | 6 | | 6 | 7 | | 7 | 2 | | 8 | 6 | | 9 | 2 | | 10 | 15 | | 11 | 1 | | 12 | 1 | | 13 | 29 | | 14 | 15 | | 15 | 5 | | 16 | 3 | | 17 | 4 | | 18 | 19 | | 19 | 14 | | 20 | 3 | | 21 | 4 | | 22 | 14 | | 23 | 7 | | 24 | 3 | | 25 | 8 | | 26 | 8 | | 27 | 4 | | 28 | 11 | | 29 | 3 | | 30 | 6 | | 31 | 4 | | 32 | 19 | | 33 | 9 | | 34 | 3 | | 35 | 18 | | 36 | 6 | | 37 | 2 | | 38 | 10 | | 39 | 8 | | 40 | 1 | | 41 | 11 | | 42 | 4 | | 43 | 7 | | 44 | 22 | | 45 | 10 | | 46 | 20 | | 47 | 8 | | 48 | 11 | | 49 | 5 |
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| 77.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5 | | totalSentences | 112 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 62 | | matches | | 0 | "Somewhere behind her she heard" | | 1 | "Somewhere a bottle met a" | | 2 | "Instead she said," |
| | ratio | 0.048 | |
| 65.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 62 | | matches | | 0 | "He'd turned at the sound" | | 1 | "It was that kind of" | | 2 | "He set down his glass" | | 3 | "He slid off the stool" | | 4 | "His hazel eyes moved between" | | 5 | "She perched on the stool" | | 6 | "He didn't ask what she" | | 7 | "He turned his twice before" | | 8 | "He set the glass down" | | 9 | "He leaned closer, and she" | | 10 | "Her fingers found the crescent" | | 11 | "She could feel his attention" | | 12 | "He turned the glass again" | | 13 | "She looked at him then," | | 14 | "He said it fast, the" | | 15 | "He lifted the glass with" | | 16 | "He said it flat, factual," | | 17 | "He laughed, and for one" | | 18 | "He turned to face her" | | 19 | "His eyes flicked to the" |
| | ratio | 0.387 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 62 | | matches | | 0 | "The whiskey glass stopped mid-pour." | | 1 | "Rory had come down the" | | 2 | "The green neon from the" | | 3 | "He'd turned at the sound" | | 4 | "Everyone turned at footsteps in" | | 5 | "It was that kind of" | | 6 | "He set down his glass" | | 7 | "The crate suddenly weighed nothing" | | 8 | "He slid off the stool" | | 9 | "The Dai she remembered from" | | 10 | "This man moved like someone" | | 11 | "Silas appeared behind the bar" | | 12 | "His hazel eyes moved between" | | 13 | "Dai's hand found her elbow," | | 14 | "The nails were bitten down" | | 15 | "That was new." | | 16 | "She perched on the stool" | | 17 | "He didn't ask what she" | | 18 | "Some things hadn't changed." | | 19 | "Dai had always ordered for" |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 6.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 4 | | matches | | 0 | "Rory had come down the stairs with an empty crate and a full bladder, planning nothing more complicated than her usual Tuesday, and now she stood in the doorway…" | | 1 | "Silas appeared behind the bar the way he always appeared, without announcement, polishing a glass that was already clean." | | 2 | "Rory signalled Silas for two more, and he poured them without a word, sliding them across with the precision of a man who had spent decades reading rooms and kn…" | | 3 | "Outside, the neon hummed green against the window, and the night crowd of Soho drifted past without knowing or caring that two people inside were holding five y…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 64 | | tagDensity | 0.141 | | leniency | 0.281 | | rawRatio | 0.111 | | effectiveRatio | 0.031 | |