| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1058 | | totalAiIsmAdverbs | 3 | | 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) | |
| 66.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1058 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "grave" | | 1 | "flickered" | | 2 | "familiar" | | 3 | "effortless" | | 4 | "pulsed" | | 5 | "pulse" | | 6 | "echo" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 25 | | totalWords | 1058 | | ratio | 0.024 | | matches | | 0 | "Don't make promises. Don't eat anything. Don't" | | 1 | "don't, for the love of God, look at it directly if it asks you to." | | 2 | "Aurora." | | 3 | "Aren't you?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 978 | | uniqueNames | 8 | | maxNameDensity | 0.41 | | worstName | "Eva" | | maxWindowNameDensity | 1 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 4 | | October | 1 | | Yu-Fei | 1 | | Cardiff | 1 | | Evan | 2 | | Which | 1 | | God | 1 | | Three | 1 |
| | persons | | 0 | "Eva" | | 1 | "Yu-Fei" | | 2 | "Evan" | | 3 | "God" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 51.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like petrol" | | 1 | "looked like a held breath" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.945 | | wordCount | 1058 | | matches | | 0 | "Not the friendly warm of a hot drink on a cold morning, but the slow, patient warm of something waking up" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 25.19 | | std | 22.87 | | cv | 0.908 | | sampleLengths | | 0 | 6 | | 1 | 55 | | 2 | 3 | | 3 | 23 | | 4 | 47 | | 5 | 36 | | 6 | 33 | | 7 | 16 | | 8 | 31 | | 9 | 3 | | 10 | 16 | | 11 | 72 | | 12 | 8 | | 13 | 12 | | 14 | 4 | | 15 | 13 | | 16 | 60 | | 17 | 9 | | 18 | 54 | | 19 | 38 | | 20 | 6 | | 21 | 20 | | 22 | 26 | | 23 | 60 | | 24 | 5 | | 25 | 8 | | 26 | 22 | | 27 | 7 | | 28 | 8 | | 29 | 99 | | 30 | 37 | | 31 | 1 | | 32 | 2 | | 33 | 7 | | 34 | 61 | | 35 | 10 | | 36 | 49 | | 37 | 16 | | 38 | 5 | | 39 | 33 | | 40 | 33 | | 41 | 4 |
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| 94.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 96 | | matches | | 0 | "was meant" | | 1 | "been shut" | | 2 | "was gone" |
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| 31.22% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 158 | | matches | | 0 | "was drinking" | | 1 | "was already walking" | | 2 | "was standing" | | 3 | "was using" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 89.51% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 981 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 51 | | adverbRatio | 0.05198776758409786 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01529051987767584 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 10.37 | | std | 9.9 | | cv | 0.954 | | sampleLengths | | 0 | 6 | | 1 | 14 | | 2 | 6 | | 3 | 35 | | 4 | 3 | | 5 | 14 | | 6 | 9 | | 7 | 16 | | 8 | 17 | | 9 | 3 | | 10 | 1 | | 11 | 10 | | 12 | 7 | | 13 | 8 | | 14 | 21 | | 15 | 21 | | 16 | 12 | | 17 | 5 | | 18 | 11 | | 19 | 10 | | 20 | 3 | | 21 | 1 | | 22 | 17 | | 23 | 3 | | 24 | 4 | | 25 | 12 | | 26 | 3 | | 27 | 3 | | 28 | 21 | | 29 | 45 | | 30 | 8 | | 31 | 4 | | 32 | 1 | | 33 | 5 | | 34 | 2 | | 35 | 4 | | 36 | 13 | | 37 | 7 | | 38 | 24 | | 39 | 18 | | 40 | 11 | | 41 | 9 | | 42 | 7 | | 43 | 1 | | 44 | 15 | | 45 | 31 | | 46 | 4 | | 47 | 4 | | 48 | 30 | | 49 | 6 |
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| 57.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4019607843137255 | | totalSentences | 102 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 75 | | matches | | 0 | "Just a patch of turf," | | 1 | "Then it shrank." | | 2 | "Then it was gone, and" | | 3 | "Only the ring of flattened" |
| | ratio | 0.053 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 75 | | matches | | 0 | "She counted six." | | 1 | "Her voice came out too" | | 2 | "She pressed her knuckles against" | | 3 | "She kept her voice low," | | 4 | "She stepped past the sixth" | | 5 | "She shook it" | | 6 | "She watched the circle on" | | 7 | "She swung the torch." | | 8 | "They folded in a neat," | | 9 | "She followed the wave with" | | 10 | "It had the shape of" | | 11 | "She did not move." | | 12 | "She did not breathe." | | 13 | "She had learned both skills" | | 14 | "She could see the gap" | | 15 | "She started walking." | | 16 | "She'd turned around twice to" | | 17 | "Her mouth had gone dry" | | 18 | "She ran her tongue along" | | 19 | "It said it the way" |
| | ratio | 0.293 | |
| 93.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 75 | | matches | | 0 | "The third oak came up" | | 1 | "Rory counted them again under" | | 2 | "She counted six." | | 3 | "Her voice came out too" | | 4 | "The torch beam slid across" | | 5 | "Bluebells in October." | | 6 | "A clutch of snowdrops that" | | 7 | "She pressed her knuckles against" | | 8 | "The pendant beneath her jumper" | | 9 | "She kept her voice low," | | 10 | "The grove did not answer." | | 11 | "That, at least, was what" | | 12 | "She stepped past the sixth" | | 13 | "The torch flickered." | | 14 | "She shook it" | | 15 | "The beam steadied." | | 16 | "She watched the circle on" | | 17 | "Something moved at the edge" | | 18 | "She swung the torch." | | 19 | "A low hanging branch, maybe." |
| | ratio | 0.733 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 75 | | matches | | 0 | "To her left now." | | 1 | "Now there were only trees," |
| | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "Seven ancient oaks ringed the grove, one for each of something Eva hadn't bothered to finish explaining before shoving her out the door with a torch and a flask…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "she breathed (breathe)" |
| | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.5 | | effectiveRatio | 0.308 | |