| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1741 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "gently" |
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| 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) | |
| 79.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1741 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "pulsed" | | 1 | "echo" | | 2 | "flickered" | | 3 | "weight" | | 4 | "could feel" | | 5 | "warmth" | | 6 | "traced" |
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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 | 203 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 203 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 214 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1741 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1690 | | uniqueNames | 31 | | maxNameDensity | 0.47 | | worstName | "Rory" | | maxWindowNameDensity | 1 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Golden | 2 | | Empress | 2 | | Isolde | 1 | | Yu-Fei | 2 | | Deptford | 1 | | Rottweiler | 1 | | Hackney | 1 | | Monopoly | 1 | | Wear | 1 | | Heathrow | 1 | | Kingston | 1 | | Oasis | 1 | | February | 1 | | Tooting | 1 | | High | 1 | | Street | 1 | | Cardiff | 1 | | Whispering | 1 | | Gallery | 1 | | St | 1 | | Paul | 1 | | London | 2 | | Aurora | 1 | | Roehampton | 1 | | Gate | 1 | | Evan | 2 | | Rory | 8 | | Hot | 3 | | Eight | 3 |
| | persons | | 0 | "Yu-Fei" | | 1 | "Rottweiler" | | 2 | "Paul" | | 3 | "Evan" | | 4 | "Rory" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Deptford" | | 3 | "Hackney" | | 4 | "Monopoly" | | 5 | "Heathrow" | | 6 | "Kingston" | | 7 | "Tooting" | | 8 | "High" | | 9 | "Street" | | 10 | "Cardiff" | | 11 | "Whispering" | | 12 | "Gallery" | | 13 | "St" | | 14 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like her mother calling her in for" |
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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 | 1741 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 214 | | matches | | 0 | "insisted that turning" | | 1 | "knew that voice" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 22.91 | | std | 21.05 | | cv | 0.919 | | sampleLengths | | 0 | 9 | | 1 | 66 | | 2 | 66 | | 3 | 9 | | 4 | 3 | | 5 | 4 | | 6 | 2 | | 7 | 5 | | 8 | 22 | | 9 | 3 | | 10 | 6 | | 11 | 43 | | 12 | 39 | | 13 | 6 | | 14 | 3 | | 15 | 89 | | 16 | 25 | | 17 | 6 | | 18 | 18 | | 19 | 7 | | 20 | 77 | | 21 | 19 | | 22 | 37 | | 23 | 66 | | 24 | 46 | | 25 | 37 | | 26 | 5 | | 27 | 33 | | 28 | 5 | | 29 | 7 | | 30 | 10 | | 31 | 3 | | 32 | 40 | | 33 | 20 | | 34 | 47 | | 35 | 19 | | 36 | 9 | | 37 | 26 | | 38 | 63 | | 39 | 7 | | 40 | 11 | | 41 | 32 | | 42 | 6 | | 43 | 37 | | 44 | 8 | | 45 | 1 | | 46 | 29 | | 47 | 15 | | 48 | 49 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 203 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 258 | | matches | | 0 | "was looking" | | 1 | "was leaving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 214 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1695 | | adjectiveStacks | 1 | | stackExamples | | 0 | "warm pressed against her" |
| | adverbCount | 65 | | adverbRatio | 0.038348082595870206 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.010619469026548672 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 214 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 214 | | mean | 8.14 | | std | 7.46 | | cv | 0.917 | | sampleLengths | | 0 | 9 | | 1 | 14 | | 2 | 9 | | 3 | 30 | | 4 | 2 | | 5 | 2 | | 6 | 9 | | 7 | 8 | | 8 | 4 | | 9 | 7 | | 10 | 3 | | 11 | 24 | | 12 | 20 | | 13 | 3 | | 14 | 6 | | 15 | 3 | | 16 | 4 | | 17 | 2 | | 18 | 5 | | 19 | 7 | | 20 | 2 | | 21 | 13 | | 22 | 3 | | 23 | 6 | | 24 | 3 | | 25 | 24 | | 26 | 5 | | 27 | 8 | | 28 | 3 | | 29 | 1 | | 30 | 4 | | 31 | 11 | | 32 | 17 | | 33 | 6 | | 34 | 6 | | 35 | 3 | | 36 | 15 | | 37 | 24 | | 38 | 32 | | 39 | 6 | | 40 | 3 | | 41 | 9 | | 42 | 8 | | 43 | 3 | | 44 | 14 | | 45 | 4 | | 46 | 2 | | 47 | 4 | | 48 | 1 | | 49 | 13 |
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| 55.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 25 | | diversityRatio | 0.40375586854460094 | | totalSentences | 213 | | uniqueOpeners | 86 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 158 | | matches | | 0 | "Faintly, from somewhere deep inside," | | 1 | "Then, from somewhere past the" | | 2 | "Directly behind her." | | 3 | "Instead, she took one step" | | 4 | "Just presence, filling the space" | | 5 | "Then a voice spoke from" |
| | ratio | 0.038 | |
| 80.76% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 158 | | matches | | 0 | "She shifted the insulated bag" | | 1 | "Her phone buzzed." | | 2 | "She answered before the second" | | 3 | "She'd delivered to worse." | | 4 | "She stopped walking." | | 5 | "She had worn it every" | | 6 | "It had never been warm" | | 7 | "She tugged it out from" | | 8 | "she told it" | | 9 | "Her voice sounded wrong." | | 10 | "She had learned to sleep" | | 11 | "She had never once heard" | | 12 | "She kept walking." | | 13 | "Her feet knew the direction" | | 14 | "They were oak, old oak," | | 15 | "It did anyway." | | 16 | "It lay across the grass" | | 17 | "She stepped between two of" | | 18 | "Her ears popped." | | 19 | "Her own voice." |
| | ratio | 0.348 | |
| 83.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 119 | | totalSentences | 158 | | matches | | 0 | "The satnav insisted the customer" | | 1 | "Rory held her phone at" | | 2 | "The blue dot sat squarely" | | 3 | "She shifted the insulated bag" | | 4 | "Egg fried rice." | | 5 | "Someone had paid forty pounds" | | 6 | "The envelope carried a name," | | 7 | "Her phone buzzed." | | 8 | "She answered before the second" | | 9 | "A wok clanged somewhere behind" | | 10 | "The line died." | | 11 | "Rory looked at the screen." | | 12 | "The signal bars had folded" | | 13 | "Emergency calls only." | | 14 | "She'd delivered to worse." | | 15 | "A squat in Deptford with" | | 16 | "A man in Hackney who'd" | | 17 | "A tree was a step" | | 18 | "Something warm pressed against her" | | 19 | "She stopped walking." |
| | ratio | 0.753 | |
| 94.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 158 | | matches | | 0 | "Even here, in the deepest" | | 1 | "Now it had." | | 2 | "As if they'd been looking" |
| | ratio | 0.019 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 4 | | matches | | 0 | "Rory held her phone at arm's length, as if distance might change its mind." | | 1 | "Around both of them stretched the flat grey nothing of Richmond Park, two thousand acres of bracken and hunched oaks and deer that watched the road with wet, pa…" | | 2 | "A crimson stone the size of her thumbnail on a thin silver chain, and a folded slip of paper that said only: Wear it." | | 3 | "She'd tell Yu-Fei the customer hadn't answered, eat the twenty-pound tip that didn't exist, and never, ever take a cash order again." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |