| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.057 | | leniency | 0.114 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2720 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 77.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2720 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "familiar" | | 1 | "silence" | | 2 | "footsteps" | | 3 | "trembled" | | 4 | "pulsed" | | 5 | "pulse" | | 6 | "measured" | | 7 | "traced" | | 8 | "weight" |
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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 | 346 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 346 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 379 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2718 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 80 | | wordCount | 2572 | | uniqueNames | 17 | | maxNameDensity | 1.79 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Aurora | 1 | | Richmond | 1 | | Park | 1 | | Golden | 1 | | Empress | 1 | | Heartstone | 6 | | Grove | 6 | | Yu-Fei | 1 | | Heathrow | 1 | | Rory | 46 | | One | 4 | | Jennifer | 1 | | Carter | 1 | | Welsh | 1 | | November | 1 | | Enough | 3 | | Step | 4 |
| | persons | | 0 | "Heartstone" | | 1 | "Yu-Fei" | | 2 | "Rory" | | 3 | "One" | | 4 | "Jennifer" | | 5 | "Carter" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Golden" | | 3 | "Heathrow" | | 4 | "November" |
| | globalScore | 0.606 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 210 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared closer than it should have, its hollow seam turned towards her" |
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| 89.62% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 1.104 | | wordCount | 2718 | | matches | | 0 | "not wet or solid but crowded" | | 1 | "Not as Jennifer Carter sounded now, but as she had during Rory’s childhood: warmer, younger, carryin" | | 2 | "not gripping from behind but reaching out from within the passage" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 379 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 246 | | mean | 11.05 | | std | 12.18 | | cv | 1.103 | | sampleLengths | | 0 | 26 | | 1 | 8 | | 2 | 5 | | 3 | 5 | | 4 | 3 | | 5 | 54 | | 6 | 9 | | 7 | 34 | | 8 | 6 | | 9 | 2 | | 10 | 2 | | 11 | 9 | | 12 | 2 | | 13 | 57 | | 14 | 4 | | 15 | 2 | | 16 | 75 | | 17 | 9 | | 18 | 10 | | 19 | 2 | | 20 | 10 | | 21 | 8 | | 22 | 2 | | 23 | 21 | | 24 | 20 | | 25 | 11 | | 26 | 6 | | 27 | 49 | | 28 | 8 | | 29 | 11 | | 30 | 25 | | 31 | 1 | | 32 | 2 | | 33 | 10 | | 34 | 9 | | 35 | 18 | | 36 | 24 | | 37 | 12 | | 38 | 3 | | 39 | 36 | | 40 | 4 | | 41 | 3 | | 42 | 17 | | 43 | 7 | | 44 | 42 | | 45 | 2 | | 46 | 36 | | 47 | 5 | | 48 | 6 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 346 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 443 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 379 | | ratio | 0.005 | | matches | | 0 | "The city still existed beyond the park walls—traffic, sirens, aircraft slipping towards Heathrow—but none of it reached her." | | 1 | "Bark covered their surfaces; severed limbs protruded like worn handles." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2578 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 69 | | adverbRatio | 0.026764934057408846 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0031031807602792862 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 379 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 379 | | mean | 7.17 | | std | 4.87 | | cv | 0.679 | | sampleLengths | | 0 | 19 | | 1 | 7 | | 2 | 8 | | 3 | 5 | | 4 | 5 | | 5 | 3 | | 6 | 9 | | 7 | 13 | | 8 | 15 | | 9 | 17 | | 10 | 6 | | 11 | 2 | | 12 | 1 | | 13 | 2 | | 14 | 2 | | 15 | 15 | | 16 | 15 | | 17 | 4 | | 18 | 2 | | 19 | 2 | | 20 | 2 | | 21 | 7 | | 22 | 2 | | 23 | 2 | | 24 | 6 | | 25 | 14 | | 26 | 20 | | 27 | 7 | | 28 | 3 | | 29 | 7 | | 30 | 4 | | 31 | 2 | | 32 | 19 | | 33 | 20 | | 34 | 9 | | 35 | 6 | | 36 | 21 | | 37 | 9 | | 38 | 10 | | 39 | 2 | | 40 | 5 | | 41 | 5 | | 42 | 8 | | 43 | 2 | | 44 | 21 | | 45 | 7 | | 46 | 9 | | 47 | 4 | | 48 | 6 | | 49 | 5 |
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| 45.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.2864721485411141 | | totalSentences | 377 | | uniqueOpeners | 108 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 297 | | matches | | 0 | "Just directions drawn in green" | | 1 | "More like an ember taking" | | 2 | "Twice she looked back." | | 3 | "Then again, close to her" | | 4 | "Then she stumbled into open" | | 5 | "Then its forelegs." | | 6 | "Then dozens rang beneath the" | | 7 | "Bright blue eyes opened." | | 8 | "Only Rory, the stars and" | | 9 | "Then one beside the nearest" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 297 | | matches | | 0 | "She watched until the bend" | | 1 | "He had pointed at the" | | 2 | "She also remembered the message" | | 3 | "Her voice fell flat against" | | 4 | "Its silver chain snagged her" | | 5 | "She knew the park well" | | 6 | "Her torch carved out a" | | 7 | "She folded it and kept" | | 8 | "Her breath smoked beneath the" | | 9 | "It occupied it." | | 10 | "She advanced, keeping the torch" | | 11 | "She went left." | | 12 | "Her head drooped." | | 13 | "She did not look back" | | 14 | "She crouched and picked up" | | 15 | "It hit a trunk." | | 16 | "Her torch swept across brambles" | | 17 | "Her own voice came back" | | 18 | "She backed away." | | 19 | "Her phone read 11:57." |
| | ratio | 0.172 | |
| 79.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 226 | | totalSentences | 297 | | matches | | 0 | "The taxi left Aurora at" | | 1 | "She watched until the bend" | | 2 | "The driver had refused to" | | 3 | "He had pointed at the" | | 4 | "A chain hung through them," | | 5 | "Rory remembered the same entrance" | | 6 | "She also remembered the message" | | 7 | "The heather had bloomed on" | | 8 | "Rory checked her phone." | | 9 | "Her voice fell flat against" | | 10 | "Halfway over, the pendant beneath" | | 11 | "Its silver chain snagged her" | | 12 | "A dull light moved in" | | 13 | "Rory tucked it away." | | 14 | "The mapped route followed a" | | 15 | "She knew the park well" | | 16 | "The sky pressed low and" | | 17 | "Trees stood in dense ranks," | | 18 | "Her torch carved out a" | | 19 | "The chain hung as before." |
| | ratio | 0.761 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 297 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 88 | | technicalSentenceCount | 3 | | matches | | 0 | "Not as Jennifer Carter sounded now, but as she had during Rory’s childhood: warmer, younger, carrying the Welsh cadence that sharpened whenever she called from …" | | 1 | "The bowl could not have been more than a few inches deep, but the face floated far below, pale and distorted by a depth that had no place inside the stone." | | 2 | "A ripple spread out from the bowl, crossing stone and earth as if the whole clearing lay beneath a thin skin of water." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 92.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "the voice murmured (murmur)" |
| | dialogueSentences | 35 | | tagDensity | 0.057 | | leniency | 0.114 | | rawRatio | 1 | | effectiveRatio | 0.114 | |