| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1771 | | 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) | |
| 74.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1771 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "pulse" | | 1 | "vibrated" | | 2 | "stomach" | | 3 | "warmth" | | 4 | "grave" | | 5 | "weight" | | 6 | "measured" | | 7 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 240 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 240 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 253 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1770 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1716 | | uniqueNames | 15 | | maxNameDensity | 1.46 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 3 | | Park | 3 | | Heartstone | 5 | | Yu-Fei | 1 | | Hel | 1 | | Silas | 2 | | Aurora | 25 | | December | 1 | | Cardiff | 1 | | Carter | 1 | | Irish | 1 | | Rory | 1 | | One | 5 | | Three | 3 | | Tap | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Yu-Fei" | | 2 | "Silas" | | 3 | "Aurora" | | 4 | "Carter" | | 5 | "Rory" | | 6 | "One" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" |
| | globalScore | 0.772 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 127 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beside the first" |
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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 | 1770 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 253 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 163 | | mean | 10.86 | | std | 11.72 | | cv | 1.08 | | sampleLengths | | 0 | 14 | | 1 | 8 | | 2 | 61 | | 3 | 3 | | 4 | 10 | | 5 | 10 | | 6 | 61 | | 7 | 3 | | 8 | 6 | | 9 | 14 | | 10 | 8 | | 11 | 39 | | 12 | 5 | | 13 | 51 | | 14 | 15 | | 15 | 9 | | 16 | 3 | | 17 | 20 | | 18 | 2 | | 19 | 27 | | 20 | 9 | | 21 | 5 | | 22 | 26 | | 23 | 6 | | 24 | 47 | | 25 | 8 | | 26 | 7 | | 27 | 1 | | 28 | 9 | | 29 | 5 | | 30 | 7 | | 31 | 8 | | 32 | 20 | | 33 | 8 | | 34 | 31 | | 35 | 7 | | 36 | 8 | | 37 | 3 | | 38 | 31 | | 39 | 4 | | 40 | 3 | | 41 | 3 | | 42 | 4 | | 43 | 3 | | 44 | 4 | | 45 | 5 | | 46 | 26 | | 47 | 23 | | 48 | 6 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 240 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 284 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 253 | | ratio | 0.008 | | matches | | 0 | "One second it had worried at the branches overhead; the next, every leaf held still." | | 1 | "Blue, brown, green, grey—human colours set in wet, black centres." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1719 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Blue, brown, green, grey—human" |
| | adverbCount | 48 | | adverbRatio | 0.027923211169284468 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004072134962187318 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 253 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 253 | | mean | 7 | | std | 4.95 | | cv | 0.708 | | sampleLengths | | 0 | 14 | | 1 | 5 | | 2 | 3 | | 3 | 19 | | 4 | 12 | | 5 | 7 | | 6 | 23 | | 7 | 3 | | 8 | 10 | | 9 | 10 | | 10 | 6 | | 11 | 22 | | 12 | 17 | | 13 | 16 | | 14 | 2 | | 15 | 1 | | 16 | 6 | | 17 | 14 | | 18 | 4 | | 19 | 1 | | 20 | 3 | | 21 | 8 | | 22 | 7 | | 23 | 24 | | 24 | 5 | | 25 | 21 | | 26 | 8 | | 27 | 8 | | 28 | 14 | | 29 | 15 | | 30 | 9 | | 31 | 3 | | 32 | 5 | | 33 | 15 | | 34 | 2 | | 35 | 6 | | 36 | 16 | | 37 | 1 | | 38 | 4 | | 39 | 9 | | 40 | 5 | | 41 | 1 | | 42 | 1 | | 43 | 8 | | 44 | 16 | | 45 | 6 | | 46 | 4 | | 47 | 6 | | 48 | 17 | | 49 | 7 |
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| 44.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.28063241106719367 | | totalSentences | 253 | | uniqueOpeners | 71 | |
| 81.70% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 204 | | matches | | 0 | "Then, as she crossed the" | | 1 | "Then it rose again." | | 2 | "Then a third." | | 3 | "Then every flower folded shut." | | 4 | "Even the dark beyond the" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 204 | | matches | | 0 | "It had thinned." | | 1 | "Her phone showed one bar," | | 2 | "It warmed near the thing" | | 3 | "She had come because the" | | 4 | "Her first instinct had been" | | 5 | "Her second had been to" | | 6 | "He would have locked the" | | 7 | "She had not shown him." | | 8 | "She had spent too long" | | 9 | "He had never needed to" | | 10 | "He could turn a room" | | 11 | "Her trainers sank into wet" | | 12 | "Their colours held in the" | | 13 | "They were not tall." | | 14 | "Their surfaces looked polished by" | | 15 | "Her voice travelled no farther" | | 16 | "She waited for an answer." | | 17 | "She caught only a smear" | | 18 | "She took a step into" | | 19 | "Her phone vibrated." |
| | ratio | 0.27 | |
| 33.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 174 | | totalSentences | 204 | | matches | | 0 | "Aurora left the service road" | | 1 | "The city had not vanished." | | 2 | "It had thinned." | | 3 | "Traffic hissed somewhere beyond the" | | 4 | "A plane blinked red behind" | | 5 | "Her phone showed one bar," | | 6 | "The screen reflected her face" | | 7 | "The Heartstone lay beneath her" | | 8 | "That alone had made her" | | 9 | "The pendant had never felt" | | 10 | "It warmed near the thing" | | 11 | "Tonight it had gone hard" | | 12 | "A small, deliberate knock against" | | 13 | "She had come because the" | | 14 | "Her first instinct had been" | | 15 | "Her second had been to" | | 16 | "He would have locked the" | | 17 | "She had not shown him." | | 18 | "She had spent too long" | | 19 | "Evan had made an art" |
| | ratio | 0.853 | |
| 24.51% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 204 | | matches | | 0 | "Now the dark trees swallowed" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 2 | | matches | | 0 | "Black rings twisted through the wood, closing around knots that resembled shut eyes." | | 1 | "Aurora saw no room beyond the opening, no landscape, only a depth of crimson shadow that stretched farther than the clearing could contain." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.333 | | effectiveRatio | 0.2 | |