| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.242 | | leniency | 0.485 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2183 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "slowly" |
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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) | |
| 72.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2183 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "hulking" | | 2 | "pulse" | | 3 | "silence" | | 4 | "scanned" | | 5 | "perfect" | | 6 | "weight" | | 7 | "familiar" | | 8 | "stomach" | | 9 | "pulsed" | | 10 | "whisper" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 248 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 248 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 273 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2183 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 2071 | | uniqueNames | 9 | | maxNameDensity | 1.11 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Aurora | 23 | | October | 1 | | Eva | 4 | | Heartstone | 3 | | London | 1 | | Cardiff | 2 | | One | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Eva" | | 2 | "Heartstone" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "One" |
| | globalScore | 0.945 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 142 | | 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 | 2 | | per1kWords | 0.916 | | wordCount | 2183 | | matches | | 0 | "Not one sound but many, as if several sets of nails picked at stone beneath he" | | 1 | "not flat black but layers descending far beyond the shallow earth should have a" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 273 | | matches | | 0 | "drilled that lesson" | | 1 | "heard that laugh" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 123 | | mean | 17.75 | | std | 18.49 | | cv | 1.042 | | sampleLengths | | 0 | 12 | | 1 | 68 | | 2 | 1 | | 3 | 12 | | 4 | 79 | | 5 | 12 | | 6 | 20 | | 7 | 6 | | 8 | 80 | | 9 | 3 | | 10 | 8 | | 11 | 73 | | 12 | 9 | | 13 | 2 | | 14 | 45 | | 15 | 6 | | 16 | 62 | | 17 | 17 | | 18 | 30 | | 19 | 3 | | 20 | 3 | | 21 | 20 | | 22 | 32 | | 23 | 11 | | 24 | 10 | | 25 | 23 | | 26 | 34 | | 27 | 8 | | 28 | 6 | | 29 | 4 | | 30 | 2 | | 31 | 13 | | 32 | 13 | | 33 | 45 | | 34 | 8 | | 35 | 3 | | 36 | 6 | | 37 | 8 | | 38 | 53 | | 39 | 5 | | 40 | 33 | | 41 | 8 | | 42 | 2 | | 43 | 1 | | 44 | 13 | | 45 | 2 | | 46 | 1 | | 47 | 32 | | 48 | 10 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 248 | | matches | | 0 | "was tied" | | 1 | "was gone" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 327 | | matches | | 0 | "were dragging" | | 1 | "was speaking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 273 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2074 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 75 | | adverbRatio | 0.03616200578592092 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.00626808100289296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 273 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 273 | | mean | 8 | | std | 6.77 | | cv | 0.846 | | sampleLengths | | 0 | 12 | | 1 | 18 | | 2 | 22 | | 3 | 10 | | 4 | 2 | | 5 | 16 | | 6 | 1 | | 7 | 12 | | 8 | 11 | | 9 | 11 | | 10 | 1 | | 11 | 4 | | 12 | 24 | | 13 | 28 | | 14 | 12 | | 15 | 7 | | 16 | 10 | | 17 | 3 | | 18 | 6 | | 19 | 16 | | 20 | 27 | | 21 | 23 | | 22 | 14 | | 23 | 3 | | 24 | 8 | | 25 | 5 | | 26 | 2 | | 27 | 2 | | 28 | 1 | | 29 | 31 | | 30 | 7 | | 31 | 5 | | 32 | 6 | | 33 | 3 | | 34 | 4 | | 35 | 7 | | 36 | 9 | | 37 | 2 | | 38 | 6 | | 39 | 24 | | 40 | 4 | | 41 | 11 | | 42 | 6 | | 43 | 19 | | 44 | 11 | | 45 | 14 | | 46 | 11 | | 47 | 7 | | 48 | 17 | | 49 | 8 |
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| 48.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.3443223443223443 | | totalSentences | 273 | | uniqueOpeners | 94 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 13 | | totalSentences | 200 | | matches | | 0 | "Too many for October." | | 1 | "Then, from somewhere to her" | | 2 | "Only a thick bank of" | | 3 | "Then another step, close enough" | | 4 | "Only a smooth oval of" | | 5 | "Then the grove breathed." | | 6 | "Then, clear as glass, her" | | 7 | "Instead she forced her fingers" | | 8 | "Only enough to break the" | | 9 | "Too many knuckles." | | 10 | "Then a third, smaller one," | | 11 | "Just that same blank skin" | | 12 | "Then another figure appeared between" |
| | ratio | 0.065 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 200 | | matches | | 0 | "She stopped beneath a crooked" | | 1 | "Her own voice sank into" | | 2 | "She had told herself she" | | 3 | "She had told herself a" | | 4 | "She crossed between the first" | | 5 | "It clung to her face" | | 6 | "She took another step, then" | | 7 | "Their stems bent away from" | | 8 | "She clocked that and kept" | | 9 | "She pulled it free of" | | 10 | "she said to the stone," | | 11 | "Her knees began to ache" | | 12 | "She turned toward the sound." | | 13 | "Her words sounded practical." | | 14 | "Her skin did not believe" | | 15 | "It cut off mid-note, as" | | 16 | "She blinked, and they were" | | 17 | "She backed a step toward" | | 18 | "She brushed it off and" | | 19 | "She had missed the act" |
| | ratio | 0.22 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 200 | | matches | | 0 | "The path through Richmond Park" | | 1 | "The next, the ground softened" | | 2 | "She stopped beneath a crooked" | | 3 | "The screen read 11:14, then" | | 4 | "Her own voice sank into" | | 5 | "Aurora slid the phone into" | | 6 | "Aurora touched the pendant at" | | 7 | "The Heartstone rested against her" | | 8 | "The crimson gem gave off" | | 9 | "She had told herself she" | | 10 | "She had told herself a" | | 11 | "The grove waited." | | 12 | "She crossed between the first" | | 13 | "The air changed at once." | | 14 | "It clung to her face" | | 15 | "The sounds of the park" | | 16 | "Silence arrived so complete it" | | 17 | "Aurora stood still and listened" | | 18 | "She took another step, then" | | 19 | "The clearing spread wider than" |
| | ratio | 0.72 | |
| 25.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 200 | | matches | | 0 | "By the time she righted" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 82 | | technicalSentenceCount | 3 | | matches | | 0 | "White foxgloves, bluebells, buttercups, things she did not know by name, all open under the moon as if they had mistaken midnight for noon." | | 1 | "From that black line came a whispering scrape, as though someone were dragging a comb through dry leaves just under the ground." | | 2 | "The huge shape under the soil changed course at once and pulled away from the red glow in a violent ripple that snapped flower stems in its wake." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 28.79% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "she breathed (breathe)" | | 3 | "Aurora whispered (whisper)" |
| | dialogueSentences | 33 | | tagDensity | 0.212 | | leniency | 0.424 | | rawRatio | 0.571 | | effectiveRatio | 0.242 | |