| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 72.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1480 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "very" | | 1 | "suddenly" | | 2 | "sharply" | | 3 | "really" | | 4 | "slightly" |
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| 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) | |
| 62.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1480 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "pawn" | | 2 | "unsettled" | | 3 | "furrowed" | | 4 | "flickered" | | 5 | "pulsed" | | 6 | "throb" | | 7 | "warmth" | | 8 | "silence" | | 9 | "long shadow" |
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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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1480 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 2 | | matches | | 0 | "Instead she was here, because Eva had left a voicemail at eleven forty with nothing on it but wind and then, very faint,…" | | 1 | "Eva, she called." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1468 | | uniqueNames | 18 | | maxNameDensity | 0.61 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Richmond | 2 | | Park | 2 | | September | 1 | | Golden | 1 | | Empress | 1 | | Ham | 1 | | Yu-Fei | 1 | | Eva | 7 | | Rory | 9 | | Heathrow | 1 | | Isolde | 1 | | Brendan | 1 | | Cardiff | 3 | | Pre-Law | 1 | | Evan | 2 | | July | 1 | | Jennifer | 1 | | London | 2 |
| | persons | | 0 | "Empress" | | 1 | "Yu-Fei" | | 2 | "Eva" | | 3 | "Rory" | | 4 | "Isolde" | | 5 | "Brendan" | | 6 | "Evan" | | 7 | "Jennifer" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "September" | | 3 | "Ham" | | 4 | "Heathrow" | | 5 | "Cardiff" | | 6 | "July" | | 7 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1480 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 115 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 44.85 | | std | 37.09 | | cv | 0.827 | | sampleLengths | | 0 | 9 | | 1 | 121 | | 2 | 14 | | 3 | 23 | | 4 | 68 | | 5 | 57 | | 6 | 131 | | 7 | 7 | | 8 | 94 | | 9 | 9 | | 10 | 106 | | 11 | 29 | | 12 | 9 | | 13 | 94 | | 14 | 12 | | 15 | 9 | | 16 | 64 | | 17 | 4 | | 18 | 18 | | 19 | 57 | | 20 | 5 | | 21 | 77 | | 22 | 15 | | 23 | 81 | | 24 | 29 | | 25 | 59 | | 26 | 73 | | 27 | 13 | | 28 | 3 | | 29 | 58 | | 30 | 73 | | 31 | 15 | | 32 | 44 |
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| 80.85% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 115 | | matches | | 0 | "been locked" | | 1 | "been switched" | | 2 | "been trodden" | | 3 | "were lifted" | | 4 | "were ridged" | | 5 | "were gone" | | 6 | "was gone" | | 7 | "was allowed" |
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| 53.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 227 | | matches | | 0 | "was trying" | | 1 | "was cloying" | | 2 | "were not keeping" | | 3 | "were marking" | | 4 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 87 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 3 | | adverbRatio | 0.034482758620689655 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 12.87 | | std | 11.49 | | cv | 0.893 | | sampleLengths | | 0 | 9 | | 1 | 24 | | 2 | 41 | | 3 | 24 | | 4 | 32 | | 5 | 12 | | 6 | 2 | | 7 | 18 | | 8 | 5 | | 9 | 7 | | 10 | 3 | | 11 | 32 | | 12 | 13 | | 13 | 2 | | 14 | 6 | | 15 | 5 | | 16 | 21 | | 17 | 10 | | 18 | 26 | | 19 | 35 | | 20 | 6 | | 21 | 39 | | 22 | 29 | | 23 | 2 | | 24 | 18 | | 25 | 2 | | 26 | 7 | | 27 | 41 | | 28 | 10 | | 29 | 3 | | 30 | 25 | | 31 | 4 | | 32 | 11 | | 33 | 9 | | 34 | 4 | | 35 | 14 | | 36 | 11 | | 37 | 1 | | 38 | 18 | | 39 | 2 | | 40 | 3 | | 41 | 20 | | 42 | 33 | | 43 | 2 | | 44 | 27 | | 45 | 9 | | 46 | 7 | | 47 | 5 | | 48 | 45 | | 49 | 24 |
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| 52.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3826086956521739 | | totalSentences | 115 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 97 | | matches | | 0 | "Instead she was here, because" | | 1 | "Then one thirteen." | | 2 | "Then one fourteen again." | | 3 | "Then, a half second later," | | 4 | "Then the flowers moved." | | 5 | "Just the stone, winking crimson" | | 6 | "Only dark and the press" |
| | ratio | 0.072 | |
| 71.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 97 | | matches | | 0 | "She should have been home" | | 1 | "She had tried to call" | | 2 | "She wheeled her bike through" | | 3 | "She tucked her straight shoulder-length" | | 4 | "Her bright blue eyes took" | | 5 | "She had only been to" | | 6 | "It was a small thing," | | 7 | "She had meant to take" | | 8 | "She had not." | | 9 | "She closed her fist around" | | 10 | "It grew sweeter, heavy the" | | 11 | "Her torch beam caught color" | | 12 | "Their heads were lifted toward" | | 13 | "She rubbed at the small" | | 14 | "They were not proper stone," | | 15 | "She stepped between two of" | | 16 | "She stared at it." | | 17 | "She turned the brightness up," | | 18 | "She slipped it into her" | | 19 | "Her voice sounded wrong to" |
| | ratio | 0.371 | |
| 94.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 97 | | matches | | 0 | "The gate to Richmond Park" | | 1 | "Rory stood with her hand" | | 2 | "The last delivery for the" | | 3 | "She should have been home" | | 4 | "She had tried to call" | | 5 | "She wheeled her bike through" | | 6 | "The sound carried too far." | | 7 | "Richmond Park at night was" | | 8 | "Rory knew that." | | 9 | "London had been switched off." | | 10 | "She tucked her straight shoulder-length" | | 11 | "Her bright blue eyes took" | | 12 | "The path ran pale between" | | 13 | "She had only been to" | | 14 | "Eva had called it Isolde's" | | 15 | "Rory had laughed at the" | | 16 | "That was what people said" | | 17 | "The pendant thumped once against" | | 18 | "It was a small thing," | | 19 | "She had meant to take" |
| | ratio | 0.732 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 97 | | matches | | 0 | "Even at one in the" | | 1 | "Now she followed the memory" | | 2 | "If Eva was hurt out" | | 3 | "If this was someone messing" | | 4 | "If it was neither, she" |
| | ratio | 0.052 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory had laughed at the name until she stepped past the ring of standing stones and felt the temperature change on her skin, until she saw the wildflowers thick…" | | 1 | "The silence that followed was worse because now she could hear the second silence underneath it, the one that had been there all along." | | 2 | "She understood, with the quick out-of-the-box clarity that had once gotten her through moots she had not prepared for, that the stones were not keeping somethin…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |