| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1445 | | 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) | |
| 44.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1445 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulsed" | | 1 | "throb" | | 2 | "echo" | | 3 | "measured" | | 4 | "weight" | | 5 | "throbbed" | | 6 | "footfall" | | 7 | "whisper" | | 8 | "flickered" | | 9 | "sentinel" | | 10 | "silence" | | 11 | "pulse" | | 12 | "churned" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 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 | 223 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 8 | | totalWords | 1445 | | ratio | 0.006 | | matches | | 0 | "Midnight. The Grove. Come alone. Bring the stone." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1398 | | uniqueNames | 14 | | maxNameDensity | 0.93 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Grove" | | discoveredNames | | Grove | 5 | | Rory | 13 | | Park | 1 | | Heathrow | 1 | | Isolde | 1 | | November | 1 | | London | 2 | | Heartstone | 3 | | Pendant | 1 | | Yu-Fei | 1 | | Dymas | 1 | | Empress | 1 | | Six | 2 | | Grass | 3 |
| | persons | | 0 | "Grove" | | 1 | "Rory" | | 2 | "Isolde" | | 3 | "Heartstone" | | 4 | "Pendant" | | 5 | "Yu-Fei" |
| | places | | 0 | "Park" | | 1 | "Heathrow" | | 2 | "London" | | 3 | "Dymas" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.692 | | wordCount | 1445 | | matches | | 0 | "not to moon but to her" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 223 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 22.94 | | std | 17.14 | | cv | 0.747 | | sampleLengths | | 0 | 13 | | 1 | 57 | | 2 | 53 | | 3 | 25 | | 4 | 8 | | 5 | 32 | | 6 | 24 | | 7 | 3 | | 8 | 40 | | 9 | 1 | | 10 | 42 | | 11 | 10 | | 12 | 54 | | 13 | 7 | | 14 | 11 | | 15 | 12 | | 16 | 30 | | 17 | 18 | | 18 | 6 | | 19 | 14 | | 20 | 31 | | 21 | 2 | | 22 | 1 | | 23 | 38 | | 24 | 9 | | 25 | 72 | | 26 | 15 | | 27 | 12 | | 28 | 2 | | 29 | 3 | | 30 | 9 | | 31 | 37 | | 32 | 15 | | 33 | 29 | | 34 | 13 | | 35 | 41 | | 36 | 9 | | 37 | 44 | | 38 | 6 | | 39 | 19 | | 40 | 3 | | 41 | 40 | | 42 | 50 | | 43 | 17 | | 44 | 7 | | 45 | 26 | | 46 | 33 | | 47 | 24 | | 48 | 39 | | 49 | 33 |
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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 | 231 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 223 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 185 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.021621621621621623 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 223 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 223 | | mean | 6.48 | | std | 5.54 | | cv | 0.855 | | sampleLengths | | 0 | 13 | | 1 | 5 | | 2 | 22 | | 3 | 12 | | 4 | 2 | | 5 | 2 | | 6 | 14 | | 7 | 6 | | 8 | 4 | | 9 | 6 | | 10 | 5 | | 11 | 12 | | 12 | 20 | | 13 | 7 | | 14 | 18 | | 15 | 8 | | 16 | 2 | | 17 | 2 | | 18 | 20 | | 19 | 1 | | 20 | 2 | | 21 | 2 | | 22 | 3 | | 23 | 8 | | 24 | 9 | | 25 | 7 | | 26 | 3 | | 27 | 12 | | 28 | 3 | | 29 | 7 | | 30 | 3 | | 31 | 2 | | 32 | 3 | | 33 | 10 | | 34 | 1 | | 35 | 5 | | 36 | 4 | | 37 | 13 | | 38 | 20 | | 39 | 10 | | 40 | 2 | | 41 | 4 | | 42 | 4 | | 43 | 4 | | 44 | 1 | | 45 | 2 | | 46 | 4 | | 47 | 20 | | 48 | 13 | | 49 | 7 |
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| 56.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.4009009009009009 | | totalSentences | 222 | | uniqueOpeners | 89 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 167 | | matches | | 0 | "Then the stones framed her" | | 1 | "Then another, a few paces" | | 2 | "Then a giggle." | | 3 | "Even the leaves ceased their" | | 4 | "Then the grass by her" | | 5 | "Just the print, then another," | | 6 | "Just beyond the stones." |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 167 | | matches | | 0 | "She had come because the" | | 1 | "She tugged the chain free" | | 2 | "Her voice left no echo." | | 3 | "She shifted her weight." | | 4 | "She checked her phone." | | 5 | "She pushed the phone into" | | 6 | "She took a step toward" | | 7 | "She held still and listened." | | 8 | "Her heel found stone." | | 9 | "She knew that bend." | | 10 | "She had seen it in" | | 11 | "Her whisper shook." | | 12 | "She turned in a tight" | | 13 | "Her breath came short." | | 14 | "She counted the stones to" | | 15 | "She was sure of it." | | 16 | "You did not cross them" | | 17 | "She stood inside the circle" | | 18 | "She had crossed." | | 19 | "It came from overhead, though" |
| | ratio | 0.222 | |
| 88.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 167 | | matches | | 0 | "The Grove swallowed sound the" | | 1 | "Richmond Park lay behind her." | | 2 | "Traffic on the A308 still" | | 3 | "Wildflowers bloomed year-round in the" | | 4 | "That was the rule." | | 5 | "That was what Isolde told" | | 6 | "Rory stared at them now." | | 7 | "The Heartstone Pendant rested against" | | 8 | "She had come because the" | | 9 | "Yu-Fei would have told her" | | 10 | "Eva would have told her" | | 11 | "Brendan would have quoted statute" | | 12 | "Rory came anyway." | | 13 | "She tugged the chain free" | | 14 | "The stone pulsed." | | 15 | "A slow throb of heat" | | 16 | "A push, pull." | | 17 | "Her voice left no echo." | | 18 | "The trees ate it." | | 19 | "Oaks crowded close, trunks wide" |
| | ratio | 0.743 | |
| 89.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 167 | | matches | | 0 | "Now the inner glow woke." | | 1 | "As if the trees held" | | 2 | "If she stayed till the" |
| | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 2 | | matches | | 0 | "White campion and bluebells open in November, heads up, vivid under moonlight that did not match the moon over London." | | 1 | "The Heartstone swung free on its chain, blazing crimson, painting the whole clearing red, and in that red light she saw them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |