| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 197 | | tagDensity | 0.036 | | leniency | 0.071 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2640 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 90.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2640 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "remarkable" | | 1 | "sense of" | | 2 | "coded" | | 3 | "eyebrow" | | 4 | "traced" |
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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 | 154 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 154 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 343 | | 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 | 0 | | markdownWords | 0 | | totalWords | 2640 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 31 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 127 | | wordCount | 1430 | | uniqueNames | 17 | | maxNameDensity | 3.71 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Rory" | | discoveredNames | | Wardour | 1 | | Street | 1 | | Nest | 1 | | Berlin | 1 | | Cardiff | 4 | | Clwb | 1 | | Ifor | 1 | | Bach | 1 | | Rory | 53 | | Vaughan | 2 | | Carys | 43 | | Evan | 3 | | Silas | 11 | | Prague | 1 | | Solicitor | 1 | | London | 1 | | Caz | 1 |
| | persons | | 0 | "Ifor" | | 1 | "Bach" | | 2 | "Rory" | | 3 | "Vaughan" | | 4 | "Carys" | | 5 | "Evan" | | 6 | "Silas" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Nest" | | 3 | "Berlin" | | 4 | "Cardiff" | | 5 | "Clwb" | | 6 | "Prague" | | 7 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared, thin and startled, then folded away" | | 1 | "seemed severe borders, coastlines, roads leading out" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.379 | | wordCount | 2640 | | matches | | 0 | "Not the quick glance people used when they wanted proof of survival, but a patient inventory: wet hair, delivery uniform, scar, shoul" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 343 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 273 | | mean | 9.67 | | std | 11.62 | | cv | 1.201 | | sampleLengths | | 0 | 44 | | 1 | 9 | | 2 | 5 | | 3 | 3 | | 4 | 8 | | 5 | 44 | | 6 | 52 | | 7 | 5 | | 8 | 8 | | 9 | 83 | | 10 | 4 | | 11 | 6 | | 12 | 6 | | 13 | 7 | | 14 | 1 | | 15 | 9 | | 16 | 25 | | 17 | 5 | | 18 | 11 | | 19 | 4 | | 20 | 1 | | 21 | 4 | | 22 | 4 | | 23 | 16 | | 24 | 11 | | 25 | 10 | | 26 | 40 | | 27 | 5 | | 28 | 7 | | 29 | 13 | | 30 | 19 | | 31 | 6 | | 32 | 1 | | 33 | 1 | | 34 | 12 | | 35 | 2 | | 36 | 5 | | 37 | 1 | | 38 | 5 | | 39 | 1 | | 40 | 3 | | 41 | 18 | | 42 | 4 | | 43 | 4 | | 44 | 8 | | 45 | 5 | | 46 | 12 | | 47 | 3 | | 48 | 17 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 235 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 1 | | totalSentences | 343 | | ratio | 0.003 | | matches | | 0 | "Another memory followed: Evan’s thumb moving over her screen while he claimed to check the weather; the telephone striking the wall after she asked for it back; the neat blue bruise under her sleeve at lectures." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1432 | | adjectiveStacks | 1 | | stackExamples | | 0 | "beside newer, fainter marks." |
| | adverbCount | 32 | | adverbRatio | 0.0223463687150838 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002094972067039106 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 343 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 343 | | mean | 7.7 | | std | 7 | | cv | 0.909 | | sampleLengths | | 0 | 17 | | 1 | 27 | | 2 | 9 | | 3 | 5 | | 4 | 3 | | 5 | 8 | | 6 | 5 | | 7 | 4 | | 8 | 35 | | 9 | 5 | | 10 | 7 | | 11 | 25 | | 12 | 15 | | 13 | 5 | | 14 | 8 | | 15 | 6 | | 16 | 7 | | 17 | 3 | | 18 | 22 | | 19 | 8 | | 20 | 7 | | 21 | 13 | | 22 | 17 | | 23 | 4 | | 24 | 6 | | 25 | 6 | | 26 | 7 | | 27 | 1 | | 28 | 9 | | 29 | 9 | | 30 | 16 | | 31 | 5 | | 32 | 11 | | 33 | 4 | | 34 | 1 | | 35 | 4 | | 36 | 4 | | 37 | 8 | | 38 | 8 | | 39 | 11 | | 40 | 10 | | 41 | 3 | | 42 | 12 | | 43 | 25 | | 44 | 5 | | 45 | 7 | | 46 | 13 | | 47 | 11 | | 48 | 8 | | 49 | 6 |
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| 45.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.23615160349854228 | | totalSentences | 343 | | uniqueOpeners | 81 | |
| 89.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 149 | | matches | | 0 | "Only the shoes remained." | | 1 | "Even the maps seemed severe:" | | 2 | "Then the laugh snagged." | | 3 | "Then she slid the card" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 149 | | matches | | 0 | "Their owner had worn an" | | 1 | "Her face stopped Rory beside" | | 2 | "Her old silver nose ring" | | 3 | "She wore a charcoal suit" | | 4 | "His gaze travelled from Rory" | | 5 | "His silver signet ring gave" | | 6 | "She had watched it ring" | | 7 | "Her crescent scar showed white" | | 8 | "She had always called Evan" | | 9 | "It barely moved her face," | | 10 | "Their laughter died when they" | | 11 | "He kept his body angled" | | 12 | "It had gone bitter from" | | 13 | "He kept the past framed" | | 14 | "It looked older than she" | | 15 | "He stood beneath the shelves" |
| | ratio | 0.107 | |
| 43.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 149 | | matches | | 0 | "Aurora shouldered into the Nest" | | 1 | "Silas glanced up from a" | | 2 | "Rory peeled off her jacket." | | 3 | "Water slapped the boards." | | 4 | "Rory noticed the shoes first." | | 5 | "Their owner had worn an" | | 6 | "Rory had once repaired it" | | 7 | "The woman lifted her glass." | | 8 | "Her face stopped Rory beside" | | 9 | "Carys Vaughan had shaved her" | | 10 | "A pale seam ran from" | | 11 | "The round, laughing face Rory" | | 12 | "Her old silver nose ring" | | 13 | "She wore a charcoal suit" | | 14 | "Carys lowered the glass without" | | 15 | "Rory’s wet jacket slid from" | | 16 | "Silas caught the jacket before" | | 17 | "His gaze travelled from Rory" | | 18 | "Carys watched his slight limp" | | 19 | "Carys turned her whisky between" |
| | ratio | 0.832 | |
| 33.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 149 | | matches | | | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "He stood beneath the shelves with his sleeves rolled to his forearms, examining a bottle label as if it contained coded instructions." | | 1 | "Carys’s name sat in sharp black type, solid and formal, stripped of the girl who had written Caz across lecture notes in purple pen." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "Rory muttered (mutter)" | | 1 | "Carys pressed (press)" | | 2 | "Rory murmured (murmur)" |
| | dialogueSentences | 197 | | tagDensity | 0.015 | | leniency | 0.03 | | rawRatio | 1 | | effectiveRatio | 0.03 | |