| 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 | 1684 | | 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) | |
| 94.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1684 | | totalAiIsms | 2 | | 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 | 1 | | narrationSentences | 234 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 7 | | hedgeCount | 0 | | narrationSentences | 234 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 234 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1684 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 5 | | matches | | 0 | "Aurora, she corrected." | | 1 | "Eva called me in April, he said." | | 2 | "You were the friend who stayed for tea, she said." | | 3 | "Tell me what happened, he said." | | 4 | "Ask me another question, he said." |
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| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1684 | | uniqueNames | 17 | | maxNameDensity | 2.26 | | worstName | "You" | | maxWindowNameDensity | 5.5 | | worstWindowName | "You" | | discoveredNames | | Soho | 1 | | Rory | 3 | | Raven | 1 | | Nest | 1 | | Golden | 2 | | Empress | 2 | | Blackwood | 1 | | London | 1 | | April | 1 | | Farringdon | 1 | | Cardiff | 4 | | Prague | 2 | | Yu-Fei | 2 | | Pembroke | 1 | | Road | 1 | | Silas | 4 | | You | 38 |
| | persons | | 0 | "Rory" | | 1 | "Blackwood" | | 2 | "Yu-Fei" | | 3 | "Silas" | | 4 | "You" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Golden" | | 3 | "London" | | 4 | "April" | | 5 | "Farringdon" | | 6 | "Cardiff" | | 7 | "Prague" | | 8 | "Pembroke" | | 9 | "Road" |
| | globalScore | 0.372 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 132 | | glossingSentenceCount | 1 | | matches | | 0 | "as if keeping it company" |
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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 | 1684 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 234 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 139 | | mean | 12.12 | | std | 13.02 | | cv | 1.075 | | sampleLengths | | 0 | 78 | | 1 | 33 | | 2 | 10 | | 3 | 35 | | 4 | 3 | | 5 | 8 | | 6 | 3 | | 7 | 37 | | 8 | 4 | | 9 | 2 | | 10 | 5 | | 11 | 21 | | 12 | 23 | | 13 | 2 | | 14 | 1 | | 15 | 12 | | 16 | 4 | | 17 | 9 | | 18 | 10 | | 19 | 53 | | 20 | 1 | | 21 | 4 | | 22 | 4 | | 23 | 5 | | 24 | 6 | | 25 | 11 | | 26 | 40 | | 27 | 5 | | 28 | 8 | | 29 | 8 | | 30 | 23 | | 31 | 32 | | 32 | 2 | | 33 | 8 | | 34 | 1 | | 35 | 32 | | 36 | 34 | | 37 | 26 | | 38 | 8 | | 39 | 6 | | 40 | 6 | | 41 | 6 | | 42 | 38 | | 43 | 5 | | 44 | 54 | | 45 | 8 | | 46 | 6 | | 47 | 5 | | 48 | 8 | | 49 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 234 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 325 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 234 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1690 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.015384615384615385 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000591715976331361 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 234 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 234 | | mean | 7.2 | | std | 4.58 | | cv | 0.637 | | sampleLengths | | 0 | 25 | | 1 | 19 | | 2 | 14 | | 3 | 8 | | 4 | 12 | | 5 | 12 | | 6 | 7 | | 7 | 8 | | 8 | 6 | | 9 | 10 | | 10 | 7 | | 11 | 6 | | 12 | 8 | | 13 | 14 | | 14 | 1 | | 15 | 2 | | 16 | 8 | | 17 | 3 | | 18 | 32 | | 19 | 5 | | 20 | 4 | | 21 | 2 | | 22 | 5 | | 23 | 5 | | 24 | 2 | | 25 | 14 | | 26 | 4 | | 27 | 8 | | 28 | 11 | | 29 | 2 | | 30 | 1 | | 31 | 5 | | 32 | 7 | | 33 | 4 | | 34 | 9 | | 35 | 10 | | 36 | 5 | | 37 | 9 | | 38 | 5 | | 39 | 12 | | 40 | 22 | | 41 | 1 | | 42 | 4 | | 43 | 4 | | 44 | 5 | | 45 | 6 | | 46 | 7 | | 47 | 4 | | 48 | 7 | | 49 | 15 |
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| 35.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 35 | | diversityRatio | 0.23931623931623933 | | totalSentences | 234 | | uniqueOpeners | 56 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 210 | | matches | | 0 | "Then he poured a measure" | | 1 | "Then the duck is going" | | 2 | "Then don’t wish." | | 3 | "Then why is he looking?" | | 4 | "Then why tell me?" | | 5 | "Most useful rooms are not." | | 6 | "Then what am I?" |
| | ratio | 0.033 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 132 | | totalSentences | 210 | | matches | | 0 | "She set the bag on" | | 1 | "She turned when the cloth" | | 2 | "His beard followed the same" | | 3 | "His left foot sat flat" | | 4 | "She felt the name land" | | 5 | "He looked at her for" | | 6 | "He set the tumbler down." | | 7 | "You are in London." | | 8 | "She lifted the damp bag." | | 9 | "He did not laugh." | | 10 | "His hand rested on the" | | 11 | "He leaned toward the counter." | | 12 | "You are not wearing a" | | 13 | "It’s in the pocket." | | 14 | "You used to wear your" | | 15 | "I used to wear a" | | 16 | "She stayed by the duck." | | 17 | "I have a route." | | 18 | "You have fifteen minutes." | | 19 | "Her fingers closed on the" |
| | ratio | 0.629 | |
| 40.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 176 | | totalSentences | 210 | | matches | | 0 | "The green sign above the" | | 1 | "Rain had soaked the paper" | | 2 | "The bar smelled of old" | | 3 | "Maps hung on the walls," | | 4 | "She set the bag on" | | 5 | "A half-eaten plate of duck" | | 6 | "A glass held a dark" | | 7 | "The regular had not come" | | 8 | "She turned when the cloth" | | 9 | "Silas Blackwood stood there, wiping" | | 10 | "His beard followed the same" | | 11 | "His left foot sat flat" | | 12 | "She felt the name land" | | 13 | "Aurora, she corrected." | | 14 | "He looked at her for" | | 15 | "He set the tumbler down." | | 16 | "You are in London." | | 17 | "She lifted the damp bag." | | 18 | "He did not laugh." | | 19 | "His hand rested on the" |
| | ratio | 0.838 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 210 | | matches | | 0 | "Because I own the flat" | | 1 | "If I called, you would" | | 2 | "Before you left." | | 3 | "To the funeral." | | 4 | "Because he likes knowing where" |
| | ratio | 0.024 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 2 | | matches | | 0 | "The left rested near his knee, still, as if keeping it company." | | 1 | "I am the woman who reads contracts for Yu-Fei." |
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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 | |