| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said slowly [slowly]" |
| | dialogueSentences | 29 | | tagDensity | 0.345 | | leniency | 0.69 | | rawRatio | 0.1 | | effectiveRatio | 0.069 | |
| 81.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1097 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "gently" | | 2 | "quickly" | | 3 | "slowly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 90.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1097 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1101 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.78% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 735 | | uniqueNames | 11 | | maxNameDensity | 1.22 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 9 | | Tube | 1 | | Address | 1 | | Reyes | 2 | | Marcus | 1 | | Whitfield | 5 | | Old | 1 | | Eva | 1 | | January | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Reyes" | | 4 | "Marcus" | | 5 | "Whitfield" | | 6 | "Eva" |
| | places | (empty) | | globalScore | 0.888 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1101 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 64 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 36.7 | | std | 26.92 | | cv | 0.734 | | sampleLengths | | 0 | 45 | | 1 | 42 | | 2 | 23 | | 3 | 3 | | 4 | 12 | | 5 | 39 | | 6 | 80 | | 7 | 20 | | 8 | 26 | | 9 | 35 | | 10 | 30 | | 11 | 28 | | 12 | 44 | | 13 | 15 | | 14 | 66 | | 15 | 11 | | 16 | 12 | | 17 | 87 | | 18 | 11 | | 19 | 66 | | 20 | 17 | | 21 | 84 | | 22 | 48 | | 23 | 94 | | 24 | 22 | | 25 | 1 | | 26 | 60 | | 27 | 3 | | 28 | 65 | | 29 | 12 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 46 | | matches | | 0 | "been rigged" | | 1 | "been positioned" |
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| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 126 | | matches | | 0 | "was looking" | | 1 | "was already thinking" |
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| 98.21% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 64 | | ratio | 0.016 | | matches | | 0 | "She was looking past him, at a faint discolouration in the tile dust near the platform edge — a scuff, circular, about the width of a large coin, and beside it another, and another, spaced with the same unnerving regularity as the symbols on the wall." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 737 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.033921302578018994 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.012211668928086838 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 17.2 | | std | 12.87 | | cv | 0.748 | | sampleLengths | | 0 | 24 | | 1 | 21 | | 2 | 24 | | 3 | 6 | | 4 | 12 | | 5 | 16 | | 6 | 7 | | 7 | 3 | | 8 | 12 | | 9 | 39 | | 10 | 23 | | 11 | 11 | | 12 | 29 | | 13 | 17 | | 14 | 4 | | 15 | 16 | | 16 | 17 | | 17 | 9 | | 18 | 4 | | 19 | 31 | | 20 | 26 | | 21 | 4 | | 22 | 25 | | 23 | 3 | | 24 | 16 | | 25 | 28 | | 26 | 15 | | 27 | 40 | | 28 | 26 | | 29 | 4 | | 30 | 7 | | 31 | 3 | | 32 | 9 | | 33 | 49 | | 34 | 3 | | 35 | 20 | | 36 | 15 | | 37 | 11 | | 38 | 55 | | 39 | 7 | | 40 | 4 | | 41 | 17 | | 42 | 36 | | 43 | 30 | | 44 | 18 | | 45 | 18 | | 46 | 30 | | 47 | 5 | | 48 | 46 | | 49 | 2 |
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| 95.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.625 | | totalSentences | 64 | | uniqueOpeners | 40 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 42 | | matches | | 0 | "Just those strange round marks," |
| | ratio | 0.024 | |
| 67.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 42 | | matches | | 0 | "His eyes were open." | | 1 | "His skin had a grey" | | 2 | "He looked up when Quinn's" | | 3 | "She lifted one gently with" | | 4 | "She let the hand rest" | | 5 | "She crossed to them." | | 6 | "She'd seen symbols like these" | | 7 | "She'd filed it away instead," | | 8 | "She said it without heat," | | 9 | "She gestured, and Whitfield's torch" | | 10 | "His legs were together, his" | | 11 | "She was looking past him," | | 12 | "she said slowly" | | 13 | "She stood, joints protesting from" | | 14 | "She was already thinking of" | | 15 | "She just wasn't ready to" |
| | ratio | 0.381 | |
| 19.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 42 | | matches | | 0 | "Rain came down in sheets" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The old Tube entrance yawned" | | 3 | "Someone had cut through the" | | 4 | "The chain lay coiled at" | | 5 | "PC Address, young, wet through," | | 6 | "Quinn went down the stairs" | | 7 | "The platform opened up beneath" | | 8 | "Emergency lighting had been rigged," | | 9 | "Thomas Reyes, forty-four, reported missing" | | 10 | "His eyes were open." | | 11 | "His skin had a grey" | | 12 | "DS Marcus Whitfield crouched near" | | 13 | "He looked up when Quinn's" | | 14 | "Quinn knelt on the opposite" | | 15 | "Whitfield's jaw tightened, that particular" | | 16 | "She lifted one gently with" | | 17 | "She let the hand rest" | | 18 | "She crossed to them." | | 19 | "Whitfield followed, reluctant, his torch" |
| | ratio | 0.881 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 42 | | matches | | 0 | "Whoever made these had done" |
| | ratio | 0.024 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn ducked under it without breaking stride, her boots splashing through a puddle gone black with oil and ash." | | 1 | "His legs were together, his arms out at precise angles, his head tipped back to face the ceiling as if he'd been positioned for something to look down on him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.138 | | leniency | 0.276 | | rawRatio | 0 | | effectiveRatio | 0 | |