| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 98.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2922 | | totalAiIsmAdverbs | 1 | | 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) | |
| 86.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2922 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flicker" | | 1 | "flicked" | | 2 | "weight" | | 3 | "dance" | | 4 | "silence" | | 5 | "structure" |
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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 | 394 | | matches | (empty) | |
| 92.10% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 14 | | hedgeCount | 0 | | narrationSentences | 394 | | filterMatches | | 0 | "know" | | 1 | "think" | | 2 | "feel" | | 3 | "look" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 394 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2914 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 121 | | wordCount | 2912 | | uniqueNames | 18 | | maxNameDensity | 1.61 | | worstName | "You" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Rory | 27 | | Eva | 20 | | Silas | 9 | | Docklands | 1 | | London | 2 | | Year | 1 | | Four | 1 | | Bristol | 1 | | Canary | 1 | | Wharf | 1 | | Camden | 1 | | Friday | 1 | | Paddington | 1 | | Baileys | 1 | | Carter | 1 | | River | 1 | | You | 47 | | Don | 4 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Silas" | | 3 | "Docklands" | | 4 | "Carter" | | 5 | "River" | | 6 | "You" |
| | places | | 0 | "London" | | 1 | "Bristol" | | 2 | "Canary" | | 3 | "Paddington" |
| | globalScore | 0.693 | | windowScore | 0.5 | |
| 61.17% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 197 | | glossingSentenceCount | 7 | | matches | | 0 | "as if greeting it" | | 1 | "looked like a boy at the end of a match" | | 2 | "felt like an extra in a terrible drama" | | 3 | "tasted like a night bus" | | 4 | "as if covering a window" | | 5 | "looked like water and photos had been glu" | | 6 | "looked like they’d push back if you smudg" |
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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 | 2914 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 394 | | matches | | 0 | "admit that I" | | 1 | "know that I" | | 2 | "did that yourself" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 210 | | mean | 13.88 | | std | 17.44 | | cv | 1.257 | | sampleLengths | | 0 | 81 | | 1 | 92 | | 2 | 53 | | 3 | 7 | | 4 | 1 | | 5 | 36 | | 6 | 1 | | 7 | 13 | | 8 | 6 | | 9 | 6 | | 10 | 82 | | 11 | 2 | | 12 | 2 | | 13 | 23 | | 14 | 3 | | 15 | 3 | | 16 | 5 | | 17 | 8 | | 18 | 61 | | 19 | 4 | | 20 | 5 | | 21 | 2 | | 22 | 7 | | 23 | 2 | | 24 | 41 | | 25 | 13 | | 26 | 5 | | 27 | 7 | | 28 | 3 | | 29 | 16 | | 30 | 10 | | 31 | 17 | | 32 | 19 | | 33 | 3 | | 34 | 3 | | 35 | 23 | | 36 | 25 | | 37 | 3 | | 38 | 6 | | 39 | 3 | | 40 | 31 | | 41 | 2 | | 42 | 9 | | 43 | 3 | | 44 | 5 | | 45 | 10 | | 46 | 20 | | 47 | 5 | | 48 | 5 | | 49 | 50 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 394 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 620 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 394 | | ratio | 0.01 | | matches | | 0 | "She felt it—the prickle, the one where time folded and brought a smell with it." | | 1 | "Her friend—old friend, former co-conspirator of stolen bus rides and swapped essays—wore a suit the colour of wet slate." | | 2 | "The sign—she nodded up at the green stamp over the door—dragged me." | | 3 | "I sent a card when your gran—" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2920 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 89 | | adverbRatio | 0.03047945205479452 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.002054794520547945 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 394 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 394 | | mean | 7.4 | | std | 5.8 | | cv | 0.784 | | sampleLengths | | 0 | 24 | | 1 | 7 | | 2 | 11 | | 3 | 16 | | 4 | 3 | | 5 | 20 | | 6 | 8 | | 7 | 15 | | 8 | 22 | | 9 | 2 | | 10 | 21 | | 11 | 24 | | 12 | 3 | | 13 | 7 | | 14 | 6 | | 15 | 15 | | 16 | 10 | | 17 | 12 | | 18 | 5 | | 19 | 2 | | 20 | 1 | | 21 | 15 | | 22 | 6 | | 23 | 15 | | 24 | 1 | | 25 | 13 | | 26 | 6 | | 27 | 6 | | 28 | 19 | | 29 | 12 | | 30 | 8 | | 31 | 6 | | 32 | 7 | | 33 | 6 | | 34 | 15 | | 35 | 9 | | 36 | 2 | | 37 | 2 | | 38 | 13 | | 39 | 10 | | 40 | 2 | | 41 | 1 | | 42 | 3 | | 43 | 5 | | 44 | 8 | | 45 | 13 | | 46 | 6 | | 47 | 10 | | 48 | 15 | | 49 | 17 |
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| 35.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 57 | | diversityRatio | 0.24619289340101522 | | totalSentences | 394 | | uniqueOpeners | 97 | |
| 39.80% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 335 | | matches | | 0 | "Then I stopped shaking and" | | 1 | "Then I went home and" | | 2 | "Of course you do." | | 3 | "Light under the door." |
| | ratio | 0.012 | |
| 3.88% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 181 | | totalSentences | 335 | | matches | | 0 | "Her wrist lay palm-up, scar" | | 1 | "He slid a pint along," | | 2 | "He didn’t need to speak" | | 3 | "He turned, knee stiff, to" | | 4 | "She felt it—the prickle, the" | | 5 | "She looked and found the" | | 6 | "I found a paper it" | | 7 | "Her friend—old friend, former co-conspirator" | | 8 | "She’d had a laugh that" | | 9 | "She put her bag on" | | 10 | "I live upstairs." | | 11 | "It’s close enough on nights" | | 12 | "He didn’t walk over right" | | 13 | "His eyes travelled once from" | | 14 | "He leaned in when he" | | 15 | "He kept his ring finger" | | 16 | "He retreated with a murmur" | | 17 | "You stopped with the streaks." | | 18 | "You’re still doing…?" | | 19 | "I make London smaller on" |
| | ratio | 0.54 | |
| 33.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 286 | | totalSentences | 335 | | matches | | 0 | "The green hum above the" | | 1 | "Maps yellowed with thumbprints climbed" | | 2 | "Photographs in black and white" | | 3 | "Rory leaned on the wood" | | 4 | "Tea gone cool." | | 5 | "Her wrist lay palm-up, scar" | | 6 | "Silas worked the pump with" | | 7 | "The silver ring on his" | | 8 | "He slid a pint along," | | 9 | "He didn’t need to speak" | | 10 | "He turned, knee stiff, to" | | 11 | "The door gave." | | 12 | "Rory didn’t look up straight" | | 13 | "She felt it—the prickle, the" | | 14 | "Laughter that used to bend" | | 15 | "Rory set the mug down." | | 16 | "The voice came from a" | | 17 | "Lux fabric, vowel edges ironed" | | 18 | "She looked and found the" | | 19 | "A pause opened the length" |
| | ratio | 0.854 | |
| 14.93% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 335 | | matches | | 0 | "Because it feels important to" |
| | ratio | 0.003 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 107 | | technicalSentenceCount | 4 | | matches | | 0 | "Rory lifted her face and ran her gaze over Eva’s eyes for the pupil that used to widen when she lied." | | 1 | "She placed her palm over the scar on her own wrist as if covering a window." | | 2 | "Some days I think I’m a structure that could take weight." | | 3 | "He left her standing there with the old photographs on all sides and the green leaking through the door’s glass, and the card grew warm where her fingers held i…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |