| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1884 | | 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) | |
| 57.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1884 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulse" | | 1 | "pulsed" | | 2 | "weight" | | 3 | "silence" | | 4 | "footsteps" | | 5 | "footfall" | | 6 | "whisper" | | 7 | "trembled" | | 8 | "perfect" | | 9 | "warmth" | | 10 | "familiar" | | 11 | "fluttered" | | 12 | "lurched" |
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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 | 217 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 217 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 229 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1884 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1819 | | uniqueNames | 11 | | maxNameDensity | 0.77 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | London | 2 | | Grove | 4 | | Yu-Fei | 3 | | Aurora | 14 | | Empress | 1 | | Silence | 1 | | Heartstone | 3 | | Cardiff | 2 | | One | 4 |
| | persons | | 0 | "Grove" | | 1 | "Yu-Fei" | | 2 | "Aurora" | | 3 | "Silence" | | 4 | "Heartstone" | | 5 | "One" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 131 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 93.84% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.062 | | wordCount | 1884 | | matches | | 0 | "not a circle but a corridor" | | 1 | "not pulsing now but pulling, a live thing desperate for the dark in the tree" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 229 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 112 | | mean | 16.82 | | std | 19.15 | | cv | 1.139 | | sampleLengths | | 0 | 16 | | 1 | 57 | | 2 | 76 | | 3 | 6 | | 4 | 8 | | 5 | 12 | | 6 | 75 | | 7 | 15 | | 8 | 81 | | 9 | 52 | | 10 | 3 | | 11 | 44 | | 12 | 12 | | 13 | 7 | | 14 | 4 | | 15 | 2 | | 16 | 32 | | 17 | 17 | | 18 | 4 | | 19 | 2 | | 20 | 52 | | 21 | 8 | | 22 | 11 | | 23 | 6 | | 24 | 31 | | 25 | 25 | | 26 | 2 | | 27 | 6 | | 28 | 6 | | 29 | 5 | | 30 | 42 | | 31 | 8 | | 32 | 52 | | 33 | 15 | | 34 | 7 | | 35 | 31 | | 36 | 7 | | 37 | 24 | | 38 | 3 | | 39 | 4 | | 40 | 17 | | 41 | 7 | | 42 | 32 | | 43 | 5 | | 44 | 18 | | 45 | 10 | | 46 | 1 | | 47 | 8 | | 48 | 12 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 296 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 229 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 343 | | adjectiveStacks | 1 | | stackExamples | | 0 | "beside low white stars" |
| | adverbCount | 11 | | adverbRatio | 0.03206997084548105 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.011661807580174927 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 229 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 229 | | mean | 8.23 | | std | 6.64 | | cv | 0.807 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 36 | | 3 | 6 | | 4 | 26 | | 5 | 30 | | 6 | 14 | | 7 | 2 | | 8 | 2 | | 9 | 2 | | 10 | 8 | | 11 | 10 | | 12 | 2 | | 13 | 11 | | 14 | 10 | | 15 | 4 | | 16 | 5 | | 17 | 14 | | 18 | 8 | | 19 | 15 | | 20 | 8 | | 21 | 15 | | 22 | 7 | | 23 | 31 | | 24 | 11 | | 25 | 11 | | 26 | 21 | | 27 | 15 | | 28 | 13 | | 29 | 24 | | 30 | 3 | | 31 | 7 | | 32 | 3 | | 33 | 15 | | 34 | 8 | | 35 | 11 | | 36 | 6 | | 37 | 6 | | 38 | 7 | | 39 | 2 | | 40 | 1 | | 41 | 1 | | 42 | 2 | | 43 | 7 | | 44 | 3 | | 45 | 5 | | 46 | 3 | | 47 | 14 | | 48 | 4 | | 49 | 1 |
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| 43.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.31877729257641924 | | totalSentences | 229 | | uniqueOpeners | 73 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 179 | | matches | | 0 | "Then she crossed the line" | | 1 | "Only the soft scrape of" | | 2 | "Only moss and a layer" | | 3 | "Only dark trunks, pale flowers," | | 4 | "Only the standing stones, pale" | | 5 | "Then, from far off and" | | 6 | "Then came the clink of" | | 7 | "Then she saw the details" |
| | ratio | 0.045 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 179 | | matches | | 0 | "She caught the silver chain" | | 1 | "She had walked it, crossed" | | 2 | "She had heard tyres on" | | 3 | "she said, mostly to hear" | | 4 | "She loosened her grip on" | | 5 | "She tucked one piece into" | | 6 | "She walked on." | | 7 | "She steadied it." | | 8 | "Their roots lifted from the" | | 9 | "Their bark folded in ridges" | | 10 | "Her hand brushed her left" | | 11 | "She turned the torch." | | 12 | "She walked again, slower this" | | 13 | "Her phone torch lit drifting" | | 14 | "Her own boot marks sank" | | 15 | "She saw no face, only" | | 16 | "Her mouth dried." | | 17 | "She swallowed and kept moving." | | 18 | "Her boots stuck for half" | | 19 | "She found the second half" |
| | ratio | 0.257 | |
| 80.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 136 | | totalSentences | 179 | | matches | | 0 | "Aurora ducked between the ancient" | | 1 | "The deep crimson gem struck" | | 2 | "She caught the silver chain" | | 3 | "She had walked it, crossed" | | 4 | "She had heard tyres on" | | 5 | "she said, mostly to hear" | | 6 | "The torch beam skimmed over" | | 7 | "Foxgloves rose beside low white" | | 8 | "Poppies leaned against bluebells." | | 9 | "The clearing spread wider than" | | 10 | "She loosened her grip on" | | 11 | "Tonight it had burned from" | | 12 | "Answers lay somewhere in front" | | 13 | "Aurora took a folded till" | | 14 | "She tucked one piece into" | | 15 | "She walked on." | | 16 | "The beam shook once across" | | 17 | "She steadied it." | | 18 | "The trees here carried age" | | 19 | "Their roots lifted from the" |
| | ratio | 0.76 | |
| 27.93% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 179 | | matches | | 0 | "Either way, standing above" |
| | ratio | 0.006 | |
| 92.56% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 5 | | matches | | 0 | "She caught the silver chain in her fist and kept moving, one hand out, phone torch low, boots sinking into turf that looked clipped and groomed until she steppe…" | | 1 | "She had heard tyres on wet road, the far hiss of London traffic, one late laugh from a cyclist who had no business cutting through a park at this hour." | | 2 | "She tucked one piece into a crack in the nearest stone, pressing it under rough bark that felt more like old skin than wood." | | 3 | "Standing in front of it now felt less like discovery and more like walking into a memory that had waited for her." | | 4 | "The left wrist bore a crescent scar, but the curve faced the wrong way, as if somebody had copied it from memory and laid it on backwards." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.368 | | leniency | 0.737 | | rawRatio | 0 | | effectiveRatio | 0 | |