| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1345 | | 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) | |
| 73.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1345 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "gloom" | | 1 | "etched" | | 2 | "pulsed" | | 3 | "resolved" | | 4 | "pulse" | | 5 | "crystal" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1345 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1130 | | uniqueNames | 8 | | maxNameDensity | 1.59 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 18 | | Richmond | 1 | | Park | 1 | | Heartstone | 2 | | Pendant | 1 | | September | 1 | | Nyx | 11 | | Isolde | 3 |
| | persons | | 0 | "Rory" | | 1 | "Pendant" | | 2 | "Nyx" | | 3 | "Isolde" |
| | places | | | globalScore | 0.704 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.743 | | wordCount | 1345 | | matches | | 0 | "not cut but implied, a paler run of clover" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 23.19 | | std | 21.24 | | cv | 0.916 | | sampleLengths | | 0 | 75 | | 1 | 8 | | 2 | 41 | | 3 | 8 | | 4 | 49 | | 5 | 13 | | 6 | 27 | | 7 | 9 | | 8 | 46 | | 9 | 8 | | 10 | 105 | | 11 | 2 | | 12 | 41 | | 13 | 2 | | 14 | 26 | | 15 | 4 | | 16 | 34 | | 17 | 4 | | 18 | 25 | | 19 | 4 | | 20 | 68 | | 21 | 11 | | 22 | 16 | | 23 | 15 | | 24 | 46 | | 25 | 6 | | 26 | 36 | | 27 | 6 | | 28 | 38 | | 29 | 5 | | 30 | 39 | | 31 | 7 | | 32 | 8 | | 33 | 10 | | 34 | 41 | | 35 | 7 | | 36 | 15 | | 37 | 18 | | 38 | 6 | | 39 | 62 | | 40 | 59 | | 41 | 5 | | 42 | 31 | | 43 | 2 | | 44 | 12 | | 45 | 15 | | 46 | 26 | | 47 | 1 | | 48 | 27 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 189 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1134 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 13 | | adverbRatio | 0.01146384479717813 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.04 | | std | 5.74 | | cv | 0.572 | | sampleLengths | | 0 | 23 | | 1 | 9 | | 2 | 12 | | 3 | 16 | | 4 | 15 | | 5 | 8 | | 6 | 9 | | 7 | 17 | | 8 | 7 | | 9 | 8 | | 10 | 8 | | 11 | 14 | | 12 | 22 | | 13 | 13 | | 14 | 13 | | 15 | 5 | | 16 | 8 | | 17 | 10 | | 18 | 4 | | 19 | 9 | | 20 | 5 | | 21 | 9 | | 22 | 4 | | 23 | 15 | | 24 | 3 | | 25 | 10 | | 26 | 5 | | 27 | 3 | | 28 | 14 | | 29 | 22 | | 30 | 24 | | 31 | 15 | | 32 | 11 | | 33 | 19 | | 34 | 2 | | 35 | 6 | | 36 | 25 | | 37 | 3 | | 38 | 2 | | 39 | 5 | | 40 | 2 | | 41 | 14 | | 42 | 12 | | 43 | 4 | | 44 | 3 | | 45 | 12 | | 46 | 9 | | 47 | 10 | | 48 | 4 | | 49 | 9 |
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| 59.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3880597014925373 | | totalSentences | 134 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 103 | | matches | | 0 | "She stepped past the first" | | 1 | "Their feet left no crush" | | 2 | "They halted at the centre" | | 3 | "Her breath caught." | | 4 | "She thrust her shoulder through" | | 5 | "Their voice brushed the back" | | 6 | "Her boots sank to the" | | 7 | "She followed it." | | 8 | "Her watch face fogged from" | | 9 | "Her grin split wide." | | 10 | "She laid her palm near" | | 11 | "She snatched her hand back." | | 12 | "Her mouth went dry." | | 13 | "She swallowed and tasted flowers." | | 14 | "Her skin held a moonlit" | | 15 | "She wore a dress of" | | 16 | "Her own footprints stamped dark" | | 17 | "She stopped opposite Nyx and" | | 18 | "Her pulse thumped against it." | | 19 | "She turned her lavender gaze" |
| | ratio | 0.214 | |
| 13.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 103 | | matches | | 0 | "The last deer broke from" | | 1 | "Dusk bled through Richmond Park" | | 2 | "The air tasted of wet" | | 3 | "Nyx unfolded from the shadow" | | 4 | "The dark peeled off the" | | 5 | "Violet light kindled where a" | | 6 | "The air grew a degree" | | 7 | "Rory rolled her sleeve back" | | 8 | "The Heartstone Pendant hung against" | | 9 | "She stepped past the first" | | 10 | "Nyx drifted forward without sound." | | 11 | "Their feet left no crush" | | 12 | "They halted at the centre" | | 13 | "The violet glow narrowed." | | 14 | "Rory pushed her hand out." | | 15 | "The air resisted, thick as" | | 16 | "Static bit her palm." | | 17 | "A faint distortion rippled outward" | | 18 | "Her breath caught." | | 19 | "She thrust her shoulder through" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 2 | | matches | | 0 | "The beetle flared its wings, a snap of mica, and launched into the pollen haze with a whirr that rattled Rory's ribs." | | 1 | "She wore a dress of leaf and cobweb that shifted with her breath." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |