| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 76.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 841 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 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) | |
| 88.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 841 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 67 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 841 | | 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 | 15 | | wordCount | 802 | | uniqueNames | 9 | | maxNameDensity | 0.62 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Sheen | 1 | | Gate | 1 | | Richmond | 1 | | Park | 1 | | Rory | 5 | | Tube | 1 | | Eva | 3 | | October | 1 | | Hel | 1 |
| | persons | | | places | | 0 | "Sheen" | | 1 | "Gate" | | 2 | "Richmond" | | 3 | "Park" | | 4 | "Tube" | | 5 | "Hel" |
| | globalScore | 1 | | windowScore | 1 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 2 | | matches | | 0 | "as if bowing" | | 1 | "light that seemed to seep up from the ground itself" |
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| 81.09% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.189 | | wordCount | 841 | | matches | | 0 | "Not the rush of a startled animal, but the slow, deliberate parting of stems, the way a person walk" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 38.23 | | std | 23.22 | | cv | 0.607 | | sampleLengths | | 0 | 68 | | 1 | 60 | | 2 | 27 | | 3 | 20 | | 4 | 71 | | 5 | 42 | | 6 | 70 | | 7 | 9 | | 8 | 46 | | 9 | 37 | | 10 | 23 | | 11 | 12 | | 12 | 40 | | 13 | 35 | | 14 | 60 | | 15 | 16 | | 16 | 3 | | 17 | 60 | | 18 | 8 | | 19 | 72 | | 20 | 6 | | 21 | 56 |
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| 94.79% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 67 | | matches | | 0 | "been locked" | | 1 | "was starred" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 123 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 802 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.0399002493765586 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007481296758104738 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 11.85 | | std | 8.03 | | cv | 0.678 | | sampleLengths | | 0 | 21 | | 1 | 21 | | 2 | 11 | | 3 | 15 | | 4 | 9 | | 5 | 31 | | 6 | 5 | | 7 | 15 | | 8 | 13 | | 9 | 5 | | 10 | 9 | | 11 | 5 | | 12 | 15 | | 13 | 13 | | 14 | 24 | | 15 | 14 | | 16 | 20 | | 17 | 5 | | 18 | 14 | | 19 | 11 | | 20 | 7 | | 21 | 5 | | 22 | 9 | | 23 | 12 | | 24 | 9 | | 25 | 4 | | 26 | 14 | | 27 | 22 | | 28 | 3 | | 29 | 6 | | 30 | 16 | | 31 | 2 | | 32 | 28 | | 33 | 10 | | 34 | 9 | | 35 | 3 | | 36 | 15 | | 37 | 6 | | 38 | 17 | | 39 | 3 | | 40 | 9 | | 41 | 3 | | 42 | 8 | | 43 | 29 | | 44 | 7 | | 45 | 25 | | 46 | 3 | | 47 | 17 | | 48 | 7 | | 49 | 16 |
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| 80.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5070422535211268 | | totalSentences | 71 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 61 | | matches | | 0 | "Somewhere to the east, a" | | 1 | "Then, from somewhere behind her," | | 2 | "Then she looked." |
| | ratio | 0.049 | |
| 88.85% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 61 | | matches | | 0 | "Her trainers slipped on the" | | 1 | "She stayed crouched in the" | | 2 | "She had not told Eva." | | 3 | "She thumbed her phone torch" | | 4 | "Her breath clouded white in" | | 5 | "she said to nobody" | | 6 | "Their trunks were split and" | | 7 | "She stopped at the boundary." | | 8 | "It was the second week" | | 9 | "She pulled her hand back" | | 10 | "She had checked it at" | | 11 | "She stepped over the line" | | 12 | "She did not turn around" | | 13 | "She lowered the phone a" | | 14 | "She counted the stones again," | | 15 | "She pulled it out from" | | 16 | "It only did that near" | | 17 | "They ended exactly where she" | | 18 | "She kept her eyes on" | | 19 | "Her own breath caught in" |
| | ratio | 0.328 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 61 | | matches | | 0 | "The railings at the Sheen" | | 1 | "Her trainers slipped on the" | | 2 | "She stayed crouched in the" | | 3 | "The pendant lay against her" | | 4 | "She had not told Eva." | | 5 | "Eva would have talked her" | | 6 | "She thumbed her phone torch" | | 7 | "Dew glittered on every blade." | | 8 | "Her breath clouded white in" | | 9 | "she said to nobody" | | 10 | "The path bent north through" | | 11 | "Their trunks were split and" | | 12 | "She stopped at the boundary." | | 13 | "The grass beyond it was" | | 14 | "Foxgloves, bluebells, a pale rambling" | | 15 | "It was the second week" | | 16 | "Nothing should have been blooming." | | 17 | "Rory crouched and touched a" | | 18 | "The petals were cool and" | | 19 | "She pulled her hand back" |
| | ratio | 0.705 | |
| 81.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 61 | | matches | | 0 | "Whoever had given it to" |
| | ratio | 0.016 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "The railings at the Sheen Gate end of Richmond Park had been locked for two hours, which meant Rory climbed them." | | 1 | "Their trunks were split and grey, their lower branches bent low as if bowing." | | 2 | "Beside them, a third line came out of the shadow of the oak and wound a slow circle around her, the prints deep and close together, as though whatever made them…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |