| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva stepped back [back]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 46 | | tagDensity | 0.37 | | leniency | 0.739 | | rawRatio | 0.118 | | effectiveRatio | 0.087 | |
| 85.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1388 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1388 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 43 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 43 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 90 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1396 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 8.80% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 602 | | uniqueNames | 6 | | maxNameDensity | 2.82 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 16 | | Rory | 17 | | Ptolemy | 2 | | Evan | 1 | | Cardiff | 1 | | Shoreditch | 1 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Ptolemy" | | 3 | "Evan" |
| | places | | | globalScore | 0.088 | | windowScore | 0.333 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like two pencils" | | 1 | "d she hated it visibly, jaw tightening" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.716 | | wordCount | 1396 | | matches | | 0 | "not an apology, but it was there" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 32.47 | | std | 34.94 | | cv | 1.076 | | sampleLengths | | 0 | 52 | | 1 | 7 | | 2 | 44 | | 3 | 2 | | 4 | 11 | | 5 | 68 | | 6 | 5 | | 7 | 6 | | 8 | 40 | | 9 | 12 | | 10 | 80 | | 11 | 6 | | 12 | 4 | | 13 | 62 | | 14 | 9 | | 15 | 3 | | 16 | 13 | | 17 | 57 | | 18 | 5 | | 19 | 35 | | 20 | 14 | | 21 | 37 | | 22 | 42 | | 23 | 14 | | 24 | 2 | | 25 | 6 | | 26 | 108 | | 27 | 34 | | 28 | 3 | | 29 | 111 | | 30 | 15 | | 31 | 5 | | 32 | 3 | | 33 | 110 | | 34 | 3 | | 35 | 11 | | 36 | 73 | | 37 | 141 | | 38 | 3 | | 39 | 36 | | 40 | 44 | | 41 | 35 | | 42 | 25 |
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| 88.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 43 | | matches | | |
| 16.51% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 109 | | matches | | 0 | "was grudging" | | 1 | "was buying" | | 2 | "was leaving" |
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| 22.13% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 71 | | ratio | 0.042 | | matches | | 0 | "Rory laughed — it came out wrong, too sharp." | | 1 | "\"I'm very good at taking things apart. It's the putting back together I'm bad at.\" She crossed the room — three steps, the flat was that small — and stopped close enough that Rory could see the exhaustion bruised under her eyes, could smell old coffee and the lavender soap she still used, the one from the shop in Shoreditch that had closed years ago and that she hoarded like contraband." | | 2 | "Rory laughed — properly this time, helpless, wet at the edges." |
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| 97.08% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 600 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.043333333333333335 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.01 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 19.66 | | std | 20.38 | | cv | 1.037 | | sampleLengths | | 0 | 52 | | 1 | 7 | | 2 | 3 | | 3 | 41 | | 4 | 2 | | 5 | 11 | | 6 | 21 | | 7 | 11 | | 8 | 36 | | 9 | 5 | | 10 | 6 | | 11 | 21 | | 12 | 14 | | 13 | 5 | | 14 | 12 | | 15 | 4 | | 16 | 3 | | 17 | 36 | | 18 | 37 | | 19 | 6 | | 20 | 4 | | 21 | 39 | | 22 | 23 | | 23 | 2 | | 24 | 7 | | 25 | 3 | | 26 | 13 | | 27 | 16 | | 28 | 8 | | 29 | 28 | | 30 | 5 | | 31 | 5 | | 32 | 35 | | 33 | 9 | | 34 | 5 | | 35 | 37 | | 36 | 7 | | 37 | 20 | | 38 | 15 | | 39 | 7 | | 40 | 7 | | 41 | 2 | | 42 | 6 | | 43 | 17 | | 44 | 91 | | 45 | 6 | | 46 | 22 | | 47 | 6 | | 48 | 3 | | 49 | 44 |
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| 58.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4084507042253521 | | totalSentences | 71 | | uniqueOpeners | 29 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 34 | | matches | | 0 | "Somewhere below, the curry house" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 34 | | matches | | 0 | "Her hair was pinned up" | | 1 | "It never changed." | | 2 | "She set the takeaway down" | | 3 | "She did this when she" | | 4 | "She'd done it the night" | | 5 | "She bent and scratched behind" | | 6 | "Her wrist ached, the crescent" | | 7 | "She crossed the room —" | | 8 | "She shrugged off her jacket" |
| | ratio | 0.265 | |
| 4.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 31 | | totalSentences | 34 | | matches | | 0 | "The third deadbolt turned with" | | 1 | "Eva leaned against the doorframe," | | 2 | "Her hair was pinned up" | | 3 | "Eva stepped back, holding the" | | 4 | "The offer was grudging and" | | 5 | "Rory came in before either" | | 6 | "The flat hadn't changed." | | 7 | "It never changed." | | 8 | "She set the takeaway down" | | 9 | "Eva closed the door, shot" | | 10 | "Eva peeled off her glasses," | | 11 | "She did this when she" | | 12 | "She'd done it the night" | | 13 | "Rory laughed — it came" | | 14 | "The rain ticked against the" | | 15 | "Ptolemy descended from his book-pile," | | 16 | "She bent and scratched behind" | | 17 | "Eva's voice cracked on the" | | 18 | "Her wrist ached, the crescent" | | 19 | "Eva went still." |
| | ratio | 0.912 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 34 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 16 | | technicalSentenceCount | 1 | | matches | | 0 | "Behind her, Ptolemy sat on a stack of fluvial geomorphology, tail curled around his feet, watching the corridor with the flat accusation of a cat who had not be…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.217 | | leniency | 0.435 | | rawRatio | 0 | | effectiveRatio | 0 | |