| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 128 | | tagDensity | 0.195 | | leniency | 0.391 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2115 | | 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) | |
| 97.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2115 | | totalAiIsms | 1 | | 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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 133 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 236 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 42 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 113 | | wordCount | 1293 | | uniqueNames | 12 | | maxNameDensity | 3.48 | | worstName | "Aurora" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Silas | 11 | | Eva | 44 | | Aurora | 45 | | London | 1 | | Cardiff | 2 | | Carter | 2 | | Christmas | 2 | | Eve | 1 | | Swansea | 1 | | Jennifer | 1 |
| | persons | | 0 | "Nest" | | 1 | "Silas" | | 2 | "Eva" | | 3 | "Aurora" | | 4 | "Jennifer" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Swansea" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2115 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 236 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 147 | | mean | 14.39 | | std | 17.19 | | cv | 1.195 | | sampleLengths | | 0 | 65 | | 1 | 7 | | 2 | 7 | | 3 | 8 | | 4 | 6 | | 5 | 34 | | 6 | 12 | | 7 | 2 | | 8 | 72 | | 9 | 6 | | 10 | 6 | | 11 | 2 | | 12 | 6 | | 13 | 54 | | 14 | 7 | | 15 | 73 | | 16 | 1 | | 17 | 9 | | 18 | 11 | | 19 | 6 | | 20 | 12 | | 21 | 3 | | 22 | 8 | | 23 | 9 | | 24 | 4 | | 25 | 11 | | 26 | 27 | | 27 | 9 | | 28 | 15 | | 29 | 14 | | 30 | 4 | | 31 | 11 | | 32 | 5 | | 33 | 17 | | 34 | 9 | | 35 | 3 | | 36 | 7 | | 37 | 7 | | 38 | 2 | | 39 | 25 | | 40 | 9 | | 41 | 19 | | 42 | 76 | | 43 | 10 | | 44 | 4 | | 45 | 2 | | 46 | 1 | | 47 | 4 | | 48 | 3 | | 49 | 12 |
| |
| 97.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 133 | | matches | | 0 | "were gone" | | 1 | "been covered" | | 2 | "been opened" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 219 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 236 | | ratio | 0.004 | | matches | | 0 | "She lived upstairs; the coat hook had become hers through use rather than invitation." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1295 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.016216216216216217 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0015444015444015444 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 236 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 236 | | mean | 8.96 | | std | 6.31 | | cv | 0.704 | | sampleLengths | | 0 | 10 | | 1 | 25 | | 2 | 14 | | 3 | 16 | | 4 | 7 | | 5 | 6 | | 6 | 1 | | 7 | 8 | | 8 | 6 | | 9 | 17 | | 10 | 17 | | 11 | 6 | | 12 | 6 | | 13 | 2 | | 14 | 7 | | 15 | 12 | | 16 | 14 | | 17 | 10 | | 18 | 12 | | 19 | 17 | | 20 | 4 | | 21 | 2 | | 22 | 6 | | 23 | 2 | | 24 | 6 | | 25 | 9 | | 26 | 9 | | 27 | 18 | | 28 | 18 | | 29 | 7 | | 30 | 9 | | 31 | 11 | | 32 | 16 | | 33 | 11 | | 34 | 26 | | 35 | 1 | | 36 | 8 | | 37 | 1 | | 38 | 7 | | 39 | 4 | | 40 | 6 | | 41 | 12 | | 42 | 3 | | 43 | 8 | | 44 | 9 | | 45 | 4 | | 46 | 8 | | 47 | 3 | | 48 | 8 | | 49 | 9 |
| |
| 41.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.23728813559322035 | | totalSentences | 236 | | uniqueOpeners | 56 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 119 | | matches | | 0 | "He stood behind the counter" | | 1 | "He set the bag on" | | 2 | "She lived upstairs; the coat" | | 3 | "Her hair had been cut" | | 4 | "She wore a dark jacket" | | 5 | "He crossed to the cupboard" | | 6 | "His left leg dragged a" | | 7 | "She could remember the cheap" | | 8 | "She had thrown it out" | | 9 | "She didn’t sound angry, which" | | 10 | "His grey-streaked head nearly touched" | | 11 | "He gave them the privacy" | | 12 | "They had never spoken about" | | 13 | "She tapped the back of" | | 14 | "She turned the newspaper face" | | 15 | "He was pulling a pint," | | 16 | "It had been opened and" | | 17 | "She looked at Eva’s hand" | | 18 | "He saw the photograph, then" | | 19 | "he told them" |
| | ratio | 0.21 | |
| 14.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 119 | | matches | | 0 | "The green neon sign gave" | | 1 | "Aurora saw it reflected in" | | 2 | "The pale rectangle it left" | | 3 | "Silas glanced at the bare" | | 4 | "He stood behind the counter" | | 5 | "The silver signet ring on" | | 6 | "Aurora told him" | | 7 | "He set the bag on" | | 8 | "Aurora slipped off her damp" | | 9 | "She lived upstairs; the coat" | | 10 | "The Nest smelled of beer," | | 11 | "A woman in a red" | | 12 | "Silas unpacked the cartons." | | 13 | "The woman in the red" | | 14 | "Aurora knew the movement before" | | 15 | "The careful fold at the" | | 16 | "Eva looked at her across" | | 17 | "Her hair had been cut" | | 18 | "A narrow line of white" | | 19 | "Aurora remembered long brown plaits" |
| | ratio | 0.891 | |
| 42.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 119 | | matches | | 0 | "By the time Aurora arrived," |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "She pressed her fingertips against the folded newspaper as though to keep it flat." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 18 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 128 | | tagDensity | 0.141 | | leniency | 0.281 | | rawRatio | 0 | | effectiveRatio | 0 | |