| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1620 | | 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) | |
| 69.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1620 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "pulse" | | 1 | "silence" | | 2 | "weight" | | 3 | "dance" | | 4 | "throbbed" | | 5 | "vibrated" | | 6 | "flickered" | | 7 | "measured" | | 8 | "trembled" |
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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 | 206 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 206 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 224 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1620 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 73.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1510 | | uniqueNames | 9 | | maxNameDensity | 1.52 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 2 | | Park | 2 | | Heartstone | 3 | | Silas | 2 | | Aurora | 23 | | Isolde | 1 | | Cardiff | 1 | | Earth | 1 | | Knock | 3 |
| | persons | | 0 | "Silas" | | 1 | "Aurora" | | 2 | "Isolde" | | 3 | "Knock" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" |
| | globalScore | 0.738 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 115 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared under her flat door that afternoon, folded into a square no bigger than a stamp" | | 1 | "appeared beneath hers at once" |
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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 | 1620 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 224 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 147 | | mean | 11.02 | | std | 11.84 | | cv | 1.075 | | sampleLengths | | 0 | 30 | | 1 | 44 | | 2 | 4 | | 3 | 8 | | 4 | 3 | | 5 | 40 | | 6 | 19 | | 7 | 11 | | 8 | 18 | | 9 | 11 | | 10 | 8 | | 11 | 10 | | 12 | 49 | | 13 | 2 | | 14 | 6 | | 15 | 2 | | 16 | 28 | | 17 | 2 | | 18 | 31 | | 19 | 4 | | 20 | 3 | | 21 | 4 | | 22 | 7 | | 23 | 11 | | 24 | 12 | | 25 | 53 | | 26 | 4 | | 27 | 20 | | 28 | 11 | | 29 | 2 | | 30 | 4 | | 31 | 8 | | 32 | 3 | | 33 | 52 | | 34 | 17 | | 35 | 41 | | 36 | 8 | | 37 | 5 | | 38 | 4 | | 39 | 5 | | 40 | 8 | | 41 | 7 | | 42 | 40 | | 43 | 8 | | 44 | 8 | | 45 | 43 | | 46 | 4 | | 47 | 6 | | 48 | 6 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 206 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 244 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 224 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1512 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sweet underneath, crushed leaves" |
| | adverbCount | 42 | | adverbRatio | 0.027777777777777776 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.001984126984126984 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 224 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 224 | | mean | 7.23 | | std | 5.56 | | cv | 0.769 | | sampleLengths | | 0 | 30 | | 1 | 6 | | 2 | 27 | | 3 | 11 | | 4 | 4 | | 5 | 2 | | 6 | 2 | | 7 | 4 | | 8 | 3 | | 9 | 14 | | 10 | 8 | | 11 | 18 | | 12 | 19 | | 13 | 2 | | 14 | 1 | | 15 | 4 | | 16 | 1 | | 17 | 3 | | 18 | 2 | | 19 | 2 | | 20 | 14 | | 21 | 11 | | 22 | 8 | | 23 | 10 | | 24 | 6 | | 25 | 16 | | 26 | 7 | | 27 | 20 | | 28 | 2 | | 29 | 6 | | 30 | 2 | | 31 | 4 | | 32 | 12 | | 33 | 12 | | 34 | 2 | | 35 | 14 | | 36 | 17 | | 37 | 4 | | 38 | 3 | | 39 | 4 | | 40 | 7 | | 41 | 11 | | 42 | 6 | | 43 | 6 | | 44 | 12 | | 45 | 8 | | 46 | 5 | | 47 | 8 | | 48 | 20 | | 49 | 4 |
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| 52.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.34375 | | totalSentences | 224 | | uniqueOpeners | 77 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 175 | | matches | | 0 | "Then the pendant began warming" | | 1 | "Then her torch passed over" | | 2 | "Somewhere to her right, beyond" | | 3 | "Then, from the trees, her" | | 4 | "Then she noticed the roots." | | 5 | "Then the pendant burned." |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 175 | | matches | | 0 | "Her phone showed 01:17." | | 1 | "Its deep crimson centre held" | | 2 | "Her own breath rasped inside" | | 3 | "She carried a torch in" | | 4 | "It had not reached his" | | 5 | "She left the path." | | 6 | "Her voice died two feet" | | 7 | "She kept walking." | | 8 | "She did not laugh now." | | 9 | "Her phone vibrated in her" | | 10 | "YOU ARE LATE." | | 11 | "She stared at it." | | 12 | "Her thumb hovered over the" | | 13 | "She stepped into the clearing." | | 14 | "Their stems did not break." | | 15 | "They bent, then lifted again" | | 16 | "It no longer looked white." | | 17 | "Her phone buzzed." | | 18 | "She looked up." | | 19 | "She moved closer to the" |
| | ratio | 0.229 | |
| 68.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 137 | | totalSentences | 175 | | matches | | 0 | "Aurora left her bicycle chained" | | 1 | "The park had shut hours" | | 2 | "A council sign beside the" | | 3 | "Her phone showed 01:17." | | 4 | "The Heartstone pendant rested against" | | 5 | "Its deep crimson centre held" | | 6 | "Each pulse travelled through the" | | 7 | "The note had appeared under" | | 8 | "THE OAKS THAT STAND." | | 9 | "The paper smelled of rainwater" | | 10 | "Aurora had spent three hours" | | 11 | "The city had not vanished" | | 12 | "London glimmered behind the trees" | | 13 | "Traffic sighed somewhere beyond the" | | 14 | "A train clattered across tracks" | | 15 | "The silence that followed had" | | 16 | "Water dripped from branches." | | 17 | "A leaf fell through the" | | 18 | "Her own breath rasped inside" | | 19 | "She carried a torch in" |
| | ratio | 0.783 | |
| 57.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 175 | | matches | | 0 | "Now she walked deeper into" | | 1 | "Now, Aurora drew the torch" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 2 | | matches | | 0 | "A train clattered across tracks far off, then cut short as if someone had shut a door over the sound." | | 1 | "Each sat at a different angle, as if some giant hand had shoved them into the earth and forgotten to straighten them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 54.76% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Eva had (have)" |
| | dialogueSentences | 21 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.667 | | effectiveRatio | 0.19 | |