| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1999 | | 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) | |
| 42.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1999 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "traced" | | 1 | "footsteps" | | 2 | "measured" | | 3 | "weight" | | 4 | "silence" | | 5 | "echoed" | | 6 | "tension" | | 7 | "warmth" | | 8 | "flickered" | | 9 | "throbbed" | | 10 | "unspoken" | | 11 | "pulsed" |
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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 | 266 | | matches | (empty) | |
| 56.93% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 15 | | hedgeCount | 1 | | narrationSentences | 266 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "think" | | 3 | "watch" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 266 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1999 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1999 | | uniqueNames | 14 | | maxNameDensity | 2.1 | | worstName | "You" | | maxWindowNameDensity | 5.5 | | worstWindowName | "You" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Soho | 2 | | Cardiff | 2 | | University | 1 | | London | 1 | | Brighton | 1 | | Margate | 1 | | Dover | 1 | | Paddington | 1 | | Rory | 13 | | Silas | 3 | | You | 42 | | Left | 3 |
| | persons | | | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" | | 3 | "Brighton" | | 4 | "Margate" | | 5 | "Dover" |
| | globalScore | 0.449 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 148 | | 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 | 1999 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 266 | | matches | | 0 | "wear that jacket" | | 1 | "care that you" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 37.72 | | std | 28.15 | | cv | 0.746 | | sampleLengths | | 0 | 143 | | 1 | 33 | | 2 | 84 | | 3 | 41 | | 4 | 4 | | 5 | 25 | | 6 | 5 | | 7 | 10 | | 8 | 61 | | 9 | 5 | | 10 | 2 | | 11 | 22 | | 12 | 29 | | 13 | 13 | | 14 | 26 | | 15 | 16 | | 16 | 19 | | 17 | 40 | | 18 | 44 | | 19 | 21 | | 20 | 35 | | 21 | 16 | | 22 | 13 | | 23 | 42 | | 24 | 26 | | 25 | 11 | | 26 | 54 | | 27 | 50 | | 28 | 39 | | 29 | 28 | | 30 | 55 | | 31 | 39 | | 32 | 14 | | 33 | 21 | | 34 | 63 | | 35 | 39 | | 36 | 9 | | 37 | 31 | | 38 | 45 | | 39 | 52 | | 40 | 40 | | 41 | 59 | | 42 | 27 | | 43 | 104 | | 44 | 56 | | 45 | 57 | | 46 | 49 | | 47 | 30 | | 48 | 2 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 266 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 424 | | matches | | 0 | "were testing" | | 1 | "were holding" | | 2 | "were saving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 266 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2003 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.016974538192710935 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0029955067398901645 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 266 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 266 | | mean | 7.52 | | std | 4.72 | | cv | 0.628 | | sampleLengths | | 0 | 19 | | 1 | 21 | | 2 | 24 | | 3 | 15 | | 4 | 5 | | 5 | 10 | | 6 | 12 | | 7 | 22 | | 8 | 5 | | 9 | 10 | | 10 | 5 | | 11 | 11 | | 12 | 5 | | 13 | 6 | | 14 | 2 | | 15 | 4 | | 16 | 6 | | 17 | 12 | | 18 | 26 | | 19 | 15 | | 20 | 7 | | 21 | 9 | | 22 | 9 | | 23 | 5 | | 24 | 4 | | 25 | 6 | | 26 | 4 | | 27 | 14 | | 28 | 8 | | 29 | 4 | | 30 | 10 | | 31 | 7 | | 32 | 4 | | 33 | 4 | | 34 | 5 | | 35 | 10 | | 36 | 8 | | 37 | 18 | | 38 | 18 | | 39 | 4 | | 40 | 6 | | 41 | 7 | | 42 | 5 | | 43 | 2 | | 44 | 3 | | 45 | 4 | | 46 | 3 | | 47 | 3 | | 48 | 3 | | 49 | 6 |
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| 25.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 84 | | diversityRatio | 0.12781954887218044 | | totalSentences | 266 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 260 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 161 | | totalSentences | 260 | | matches | | 0 | "She traced the crescent scar" | | 1 | "He did not look up." | | 2 | "He never did when regulars" | | 3 | "He shook water from a" | | 4 | "His shoulders sat square beneath" | | 5 | "His hair had darkened, swept" | | 6 | "His hands, once restless, hung" | | 7 | "He turned toward the booth." | | 8 | "His eyes found hers." | | 9 | "He did not smile." | | 10 | "He walked forward, boots quiet" | | 11 | "You kept the hair." | | 12 | "He reached for the tumbler" | | 13 | "His fingers brushed the rim," | | 14 | "He took a sip." | | 15 | "You look like a stranger." | | 16 | "He ran a thumb along" | | 17 | "He had sharpened himself into" | | 18 | "You stopped answering the calls." | | 19 | "You just vanished." |
| | ratio | 0.619 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 245 | | totalSentences | 260 | | matches | | 0 | "The green neon from the" | | 1 | "Rory sat in the corner" | | 2 | "She traced the crescent scar" | | 3 | "The Golden Empress delivery bag" | | 4 | "The bar breathed around her." | | 5 | "Silas moved behind the mahogany" | | 6 | "He did not look up." | | 7 | "He never did when regulars" | | 8 | "The heavy door groaned open." | | 9 | "Footsteps measured across the threshold." | | 10 | "The leather soles did not" | | 11 | "Rory lifted her head." | | 12 | "Gideon stood near the coat" | | 13 | "He shook water from a" | | 14 | "His shoulders sat square beneath" | | 15 | "His hair had darkened, swept" | | 16 | "A clean line ran along" | | 17 | "His hands, once restless, hung" | | 18 | "The change sat heavy in" | | 19 | "He turned toward the booth." |
| | ratio | 0.942 | |
| 38.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 260 | | matches | | 0 | "Even when the floor gives" | | 1 | "If I stayed, I would" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 64 | | technicalSentenceCount | 3 | | matches | | 0 | "His shoulders sat square beneath the fabric, spine rigid, posture carved from something harder than the restless boy who used to sprint across Cardiff Universit…" | | 1 | "The boy who wore thrift store sweaters three seasons straight had armored himself in order." | | 2 | "She looked at his coat, his tied tie, the firm stamp on the notebook, the straight lines of a man who had learned to build walls to survive the absence of a per…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |