| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1988 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 72.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1988 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "warmth" | | 1 | "traced" | | 2 | "pulse" | | 3 | "dancing" | | 4 | "pulsed" | | 5 | "silence" | | 6 | "whisper" | | 7 | "loomed" | | 8 | "throbbed" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "let out a breath" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "let out a breath" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 233 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 233 | | filterMatches | (empty) | | hedgeMatches | | 0 | "managed to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 241 | | 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 | 1988 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1953 | | uniqueNames | 9 | | maxNameDensity | 0.92 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Park | 1 | | London | 3 | | Grove | 5 | | Heartstone | 1 | | Lovely | 1 | | Yu-Fei | 1 | | Aurora | 18 | | Welsh | 1 | | Flowers | 3 |
| | persons | | 0 | "Grove" | | 1 | "Yu-Fei" | | 2 | "Aurora" | | 3 | "Flowers" |
| | places | | 0 | "Park" | | 1 | "London" | | 2 | "Lovely" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 135 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.503 | | wordCount | 1988 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 241 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 115 | | mean | 17.29 | | std | 19.01 | | cv | 1.1 | | sampleLengths | | 0 | 20 | | 1 | 10 | | 2 | 112 | | 3 | 13 | | 4 | 12 | | 5 | 8 | | 6 | 21 | | 7 | 49 | | 8 | 1 | | 9 | 11 | | 10 | 69 | | 11 | 52 | | 12 | 18 | | 13 | 21 | | 14 | 7 | | 15 | 58 | | 16 | 4 | | 17 | 34 | | 18 | 8 | | 19 | 4 | | 20 | 6 | | 21 | 6 | | 22 | 2 | | 23 | 30 | | 24 | 5 | | 25 | 70 | | 26 | 9 | | 27 | 2 | | 28 | 43 | | 29 | 12 | | 30 | 6 | | 31 | 40 | | 32 | 9 | | 33 | 9 | | 34 | 3 | | 35 | 61 | | 36 | 5 | | 37 | 1 | | 38 | 1 | | 39 | 10 | | 40 | 13 | | 41 | 3 | | 42 | 41 | | 43 | 3 | | 44 | 6 | | 45 | 28 | | 46 | 11 | | 47 | 3 | | 48 | 7 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 233 | | matches | | 0 | "been packed" | | 1 | "Being pinned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 299 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 241 | | ratio | 0 | | matches | (empty) | |
| 97.50% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 280 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.04285714285714286 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0035714285714285713 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 241 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 241 | | mean | 8.25 | | std | 6.63 | | cv | 0.804 | | sampleLengths | | 0 | 20 | | 1 | 6 | | 2 | 4 | | 3 | 23 | | 4 | 5 | | 5 | 13 | | 6 | 20 | | 7 | 8 | | 8 | 15 | | 9 | 2 | | 10 | 1 | | 11 | 4 | | 12 | 21 | | 13 | 13 | | 14 | 2 | | 15 | 2 | | 16 | 8 | | 17 | 3 | | 18 | 5 | | 19 | 5 | | 20 | 16 | | 21 | 8 | | 22 | 2 | | 23 | 4 | | 24 | 28 | | 25 | 7 | | 26 | 1 | | 27 | 11 | | 28 | 19 | | 29 | 8 | | 30 | 12 | | 31 | 10 | | 32 | 10 | | 33 | 1 | | 34 | 9 | | 35 | 17 | | 36 | 35 | | 37 | 18 | | 38 | 21 | | 39 | 7 | | 40 | 5 | | 41 | 9 | | 42 | 9 | | 43 | 7 | | 44 | 15 | | 45 | 13 | | 46 | 4 | | 47 | 20 | | 48 | 2 | | 49 | 3 |
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| 50.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 21 | | diversityRatio | 0.3541666666666667 | | totalSentences | 240 | | uniqueOpeners | 85 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 13 | | totalSentences | 189 | | matches | | 0 | "Then the pendant had started" | | 1 | "Then it had warmed in" | | 2 | "Somewhere beyond the ring of" | | 3 | "Just the silver chain tucked" | | 4 | "Then, from the left, a" | | 5 | "Too many hands, rubbing together." | | 6 | "Then, very soft, very close," | | 7 | "Then from the right." | | 8 | "Then near the stones at" | | 9 | "Too many joints." | | 10 | "Just the suggestion of eyes" | | 11 | "Just outside the torch beam" | | 12 | "Then the black patch in" |
| | ratio | 0.069 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 189 | | matches | | 0 | "It did the opposite." | | 1 | "Her own voice landed badly." | | 2 | "She pulled her phone from" | | 3 | "She checked the time out" | | 4 | "She slid the phone away" | | 5 | "You walked between the wrong" | | 6 | "You saw a woman in" | | 7 | "He had pushed a paper" | | 8 | "She took three careful steps" | | 9 | "She let out a breath" | | 10 | "Her foot crushed a fallen" | | 11 | "She swallowed and moved on." | | 12 | "she whispered, the words sounding" | | 13 | "Her fingers tightened around the" | | 14 | "She had spent weeks pretending" | | 15 | "Her beam from the torch" | | 16 | "She kept the torch up." | | 17 | "Her scalp prickled." | | 18 | "She stared at that stone" | | 19 | "She hated that." |
| | ratio | 0.243 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 135 | | totalSentences | 189 | | matches | | 0 | "Aurora slipped through the last" | | 1 | "That warmth should have steadied" | | 2 | "It did the opposite." | | 3 | "Richmond Park lay behind her" | | 4 | "Here the night looked older." | | 5 | "The trees pressed close around" | | 6 | "The ancient oaks stood like" | | 7 | "Wildflowers crowded the ground in" | | 8 | "Poppies with black hearts." | | 9 | "Flowers that had no business" | | 10 | "Aurora stopped just inside the" | | 11 | "A low pulse, steady as" | | 12 | "Her own voice landed badly." | | 13 | "The clearing took it and" | | 14 | "She pulled her phone from" | | 15 | "The screen reflected her face" | | 16 | "She checked the time out" | | 17 | "She slid the phone away" | | 18 | "The stories about the Grove" | | 19 | "You walked between the wrong" |
| | ratio | 0.714 | |
| 52.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 189 | | matches | | 0 | "Now that paper twist sat" | | 1 | "Whoever had given it to" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 4 | | matches | | 0 | "Flowers that had no business sharing the same soil, let alone blooming under a moon that looked thin enough to snap." | | 1 | "He had pushed a paper twist of it across the counter as if he’d been passing soy sauce." | | 2 | "Light moved inside it now, slow and thick, as if something on the other side had opened an eye." | | 3 | "Flowers bent as if something moved through them in looping paths, quick and silent." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "she whispered, the words sounding absurd in her own ears" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.667 | | effectiveRatio | 0.333 | |