| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 28 | | adverbTagCount | 1 | | adverbTags | | 0 | "He looked around [around]" |
| | dialogueSentences | 72 | | tagDensity | 0.389 | | leniency | 0.778 | | rawRatio | 0.036 | | effectiveRatio | 0.028 | |
| 84.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1308 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1308 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 57 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 93 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1319 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 33 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 600 | | uniqueNames | 11 | | maxNameDensity | 0.67 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 4 | | Silas | 2 | | Tuesday | 1 | | Tuesdays | 1 | | Soho | 1 | | Cardiff | 1 | | Welsh | 1 | | Springbank | 1 | | Thick | 1 | | Rubbed | 1 | | Dylan | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Silas" | | 2 | "Dylan" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "Welsh" |
| | globalScore | 1 | | windowScore | 1 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he owned property" |
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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 | 1319 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 21.62 | | std | 26.18 | | cv | 1.211 | | sampleLengths | | 0 | 61 | | 1 | 13 | | 2 | 9 | | 3 | 6 | | 4 | 16 | | 5 | 70 | | 6 | 4 | | 7 | 24 | | 8 | 3 | | 9 | 29 | | 10 | 8 | | 11 | 31 | | 12 | 5 | | 13 | 18 | | 14 | 7 | | 15 | 27 | | 16 | 11 | | 17 | 8 | | 18 | 16 | | 19 | 12 | | 20 | 2 | | 21 | 30 | | 22 | 16 | | 23 | 46 | | 24 | 5 | | 25 | 86 | | 26 | 5 | | 27 | 6 | | 28 | 21 | | 29 | 6 | | 30 | 32 | | 31 | 6 | | 32 | 6 | | 33 | 1 | | 34 | 66 | | 35 | 4 | | 36 | 16 | | 37 | 1 | | 38 | 42 | | 39 | 3 | | 40 | 118 | | 41 | 4 | | 42 | 7 | | 43 | 26 | | 44 | 111 | | 45 | 34 | | 46 | 5 | | 47 | 2 | | 48 | 6 | | 49 | 7 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 57 | | matches | | 0 | "been replaced" | | 1 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 120 | | matches | | |
| 57.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 101 | | ratio | 0.03 | | matches | | 0 | "He'd got taller, which was absurd — nobody got taller after twenty-two — but he stood differently, that was it, shoulders back where they used to curve inward like a question." | | 1 | "He laughed — one short bark, genuine, and for a second the old face came up through the new one like a body in water." | | 2 | "Behind Aurora, at the back of the room, the bookshelf gave its small dry click — Silas coming out, or going in; she didn't turn to check." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 598 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03678929765886288 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.011705685618729096 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 13.06 | | std | 13.97 | | cv | 1.07 | | sampleLengths | | 0 | 16 | | 1 | 45 | | 2 | 13 | | 3 | 9 | | 4 | 6 | | 5 | 11 | | 6 | 5 | | 7 | 31 | | 8 | 6 | | 9 | 3 | | 10 | 30 | | 11 | 4 | | 12 | 5 | | 13 | 15 | | 14 | 4 | | 15 | 3 | | 16 | 20 | | 17 | 9 | | 18 | 3 | | 19 | 5 | | 20 | 16 | | 21 | 15 | | 22 | 5 | | 23 | 10 | | 24 | 8 | | 25 | 7 | | 26 | 25 | | 27 | 2 | | 28 | 11 | | 29 | 8 | | 30 | 16 | | 31 | 5 | | 32 | 7 | | 33 | 2 | | 34 | 5 | | 35 | 25 | | 36 | 6 | | 37 | 10 | | 38 | 25 | | 39 | 2 | | 40 | 2 | | 41 | 17 | | 42 | 5 | | 43 | 15 | | 44 | 60 | | 45 | 11 | | 46 | 5 | | 47 | 6 | | 48 | 11 | | 49 | 10 |
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| 54.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.38613861386138615 | | totalSentences | 101 | | uniqueOpeners | 39 | |
| 83.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 40 | | matches | | 0 | "Then the door opened and" |
| | ratio | 0.025 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 40 | | matches | | 0 | "She was wiping down the" | | 1 | "she said without looking up" | | 2 | "She set the cloth down." | | 3 | "He'd got taller, which was" | | 4 | "He didn't smile" | | 5 | "He looked at her the" | | 6 | "He came the length of" | | 7 | "She poured him a Springbank" | | 8 | "He turned the glass a" | | 9 | "He laughed — one short" | | 10 | "He set the glass down." | | 11 | "He reached into his coat," | | 12 | "He tapped the card" | | 13 | "He looked up at the" | | 14 | "She picked up the cloth" | | 15 | "He turned the glass" | | 16 | "He drank the rest and" | | 17 | "He looked around the room" | | 18 | "He looked at her properly" | | 19 | "She kept her eyes on" |
| | ratio | 0.625 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 40 | | matches | | 0 | "The green neon buzzed like" | | 1 | "She was wiping down the" | | 2 | "she said without looking up" | | 3 | "The voice went into her" | | 4 | "She set the cloth down." | | 5 | "He'd got taller, which was" | | 6 | "Camel coat, damp at the" | | 7 | "Hair cropped close." | | 8 | "The lanky boy who'd smoked" | | 9 | "He didn't smile" | | 10 | "He looked at her the" | | 11 | "He came the length of" | | 12 | "She poured him a Springbank" | | 13 | "He turned the glass a" | | 14 | "He laughed — one short" | | 15 | "He set the glass down." | | 16 | "He reached into his coat," | | 17 | "He tapped the card" | | 18 | "He looked up at the" | | 19 | "The honesty of it landed" |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 1 | | matches | | 0 | "She was wiping down the far end of the bar because Silas had asked her to, and because it was Tuesday, and Tuesdays in Soho meant three regulars and a man in th…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 28 | | uselessAdditionCount | 1 | | matches | | 0 | "He looked up, the maps, the sepia strangers in their frames" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 2 | | fancyTags | | 0 | "He drank (drink)" | | 1 | "He pressed (press)" |
| | dialogueSentences | 72 | | tagDensity | 0.194 | | leniency | 0.389 | | rawRatio | 0.143 | | effectiveRatio | 0.056 | |