| 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 | 1908 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 71.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1908 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "pulse" | | 1 | "footsteps" | | 2 | "familiar" | | 3 | "weight" | | 4 | "throbbed" | | 5 | "whisper" |
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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 | 203 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 203 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 209 | | 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 | 1908 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1895 | | uniqueNames | 15 | | maxNameDensity | 0.42 | | worstName | "Aurora" | | maxWindowNameDensity | 1 | | worstWindowName | "Richmond" | | discoveredNames | | Richmond | 3 | | Park | 1 | | Carter | 2 | | Heartstone | 2 | | Silas | 2 | | Pen | 1 | | Ponds | 1 | | Golden | 1 | | Empress | 1 | | Cardiff | 2 | | Eva | 5 | | Hackney | 1 | | Yu-Fei | 1 | | Pre-Law | 1 | | Aurora | 8 |
| | persons | | 0 | "Carter" | | 1 | "Heartstone" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "Yu-Fei" | | 5 | "Aurora" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Pen" | | 3 | "Golden" | | 4 | "Cardiff" | | 5 | "Hackney" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | 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 | 1908 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 209 | | matches | | 0 | "learned that much" | | 1 | "understood that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 32.34 | | std | 29.27 | | cv | 0.905 | | sampleLengths | | 0 | 56 | | 1 | 90 | | 2 | 69 | | 3 | 4 | | 4 | 12 | | 5 | 60 | | 6 | 82 | | 7 | 3 | | 8 | 66 | | 9 | 7 | | 10 | 47 | | 11 | 8 | | 12 | 2 | | 13 | 70 | | 14 | 36 | | 15 | 16 | | 16 | 39 | | 17 | 7 | | 18 | 9 | | 19 | 33 | | 20 | 4 | | 21 | 12 | | 22 | 77 | | 23 | 7 | | 24 | 3 | | 25 | 44 | | 26 | 2 | | 27 | 80 | | 28 | 7 | | 29 | 54 | | 30 | 8 | | 31 | 87 | | 32 | 2 | | 33 | 35 | | 34 | 10 | | 35 | 1 | | 36 | 28 | | 37 | 3 | | 38 | 56 | | 39 | 14 | | 40 | 1 | | 41 | 28 | | 42 | 54 | | 43 | 24 | | 44 | 58 | | 45 | 3 | | 46 | 66 | | 47 | 21 | | 48 | 83 | | 49 | 24 |
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| 98.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 203 | | matches | | 0 | "was gone" | | 1 | "was gone" | | 2 | "was gone" | | 3 | "been given" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 296 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 209 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1898 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.02107481559536354 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0015806111696522655 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 209 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 209 | | mean | 9.13 | | std | 8.61 | | cv | 0.944 | | sampleLengths | | 0 | 9 | | 1 | 14 | | 2 | 10 | | 3 | 23 | | 4 | 16 | | 5 | 20 | | 6 | 18 | | 7 | 7 | | 8 | 11 | | 9 | 18 | | 10 | 10 | | 11 | 2 | | 12 | 3 | | 13 | 6 | | 14 | 8 | | 15 | 20 | | 16 | 5 | | 17 | 15 | | 18 | 4 | | 19 | 12 | | 20 | 3 | | 21 | 15 | | 22 | 7 | | 23 | 20 | | 24 | 15 | | 25 | 22 | | 26 | 10 | | 27 | 4 | | 28 | 4 | | 29 | 6 | | 30 | 26 | | 31 | 4 | | 32 | 6 | | 33 | 3 | | 34 | 14 | | 35 | 25 | | 36 | 6 | | 37 | 1 | | 38 | 12 | | 39 | 6 | | 40 | 2 | | 41 | 7 | | 42 | 3 | | 43 | 1 | | 44 | 18 | | 45 | 8 | | 46 | 5 | | 47 | 1 | | 48 | 10 | | 49 | 1 |
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| 35.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 31 | | diversityRatio | 0.27751196172248804 | | totalSentences | 209 | | uniqueOpeners | 58 | |
| 58.82% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 170 | | matches | | 0 | "Then it was a stone." | | 1 | "Then it was a gap." | | 2 | "Then they gained." |
| | ratio | 0.018 | |
| 76.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 170 | | matches | | 0 | "It had woken her at" | | 1 | "She had come to find" | | 2 | "She had come to put" | | 3 | "She followed the path that" | | 4 | "She had them every night" | | 5 | "Her own steps, the wet" | | 6 | "She stopped and listened." | | 7 | "She kept her torch in" | | 8 | "She had a barrister for" | | 9 | "She had patience now." | | 10 | "They were oak, or had" | | 11 | "They should not have been" | | 12 | "She counted the stones again." | | 13 | "She turned a slow circle" | | 14 | "She let her breath out" | | 15 | "She walked the inner edge" | | 16 | "She saw more trees." | | 17 | "She crouched and pinched a" | | 18 | "It was real." | | 19 | "It left a smear of" |
| | ratio | 0.359 | |
| 27.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 147 | | totalSentences | 170 | | matches | | 0 | "The chain on Richmond Park's" | | 1 | "Aurora Carter slipped through the" | | 2 | "London's orange wash died at" | | 3 | "The Heartstone sat against her" | | 4 | "It had woken her at" | | 5 | "Tonight it remembered what it" | | 6 | "She had come to find" | | 7 | "She had come to put" | | 8 | "She followed the path that" | | 9 | "Richmond at this hour owed" | | 10 | "She had them every night" | | 11 | "Here the air held nothing." | | 12 | "Her own steps, the wet" | | 13 | "She stopped and listened." | | 14 | "A fox barked once, far" | | 15 | "The path narrowed." | | 16 | "Oaks closed over it, their" | | 17 | "She kept her torch in" | | 18 | "The stone gave her light" | | 19 | "The crescent scar on her" |
| | ratio | 0.865 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 170 | | matches | (empty) | | ratio | 0 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 70 | | technicalSentenceCount | 6 | | matches | | 0 | "Oaks closed over it, their branches knitting a roof that cut the moon to scraps." | | 1 | "The same oaks, the same dark, as if the grove had folded the park and put it elsewhere." | | 2 | "She turned her head a fraction and caught motion at the edge of her sight, a pale smear between two stones that was gone when she faced it." | | 3 | "It was a dark that had depth, a dark that moved, as if something large had rolled over in its sleep far below the roots." | | 4 | "Her mind, which had carried her through Pre-Law and out the other side and across the M4 to a flat that smelled of beer and frying oil, offered her nothing she …" | | 5 | "There was only the stone, and the dark it carried, and the sound of something vast turning over in the earth beneath the wildflowers, closer now, close enough t…" |
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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 | |