| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said finally [finally]" |
| | dialogueSentences | 41 | | tagDensity | 0.366 | | leniency | 0.732 | | rawRatio | 0.067 | | effectiveRatio | 0.049 | |
| 87.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 823 | | totalAiIsmAdverbs | 2 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 823 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "scanned" | | 1 | "weight" | | 2 | "measured" | | 3 | "flickered" | | 4 | "unreadable" | | 5 | "radiant" | | 6 | "glint" | | 7 | "tracing" | | 8 | "flicked" | | 9 | "clandestine" | | 10 | "facade" | | 11 | "silence" | | 12 | "tension" | | 13 | "unspoken" | | 14 | "tangible" | | 15 | "jaw clenched" | | 16 | "predator" | | 17 | "pulse" | | 18 | "quickened" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 816 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 68.63% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 553 | | uniqueNames | 8 | | maxNameDensity | 1.63 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Blackwood | 1 | | Spymaster | 2 | | Aurora | 9 | | Silas | 9 | | Thunder | 1 |
| | persons | | 0 | "Nest" | | 1 | "Carter" | | 2 | "Blackwood" | | 3 | "Spymaster" | | 4 | "Aurora" | | 5 | "Silas" | | 6 | "Thunder" |
| | places | | | globalScore | 0.686 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 77.45% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.225 | | wordCount | 816 | | matches | | 0 | "Not the radiant girl he remembered, but no less formidable" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 16 | | std | 12.15 | | cv | 0.759 | | sampleLengths | | 0 | 57 | | 1 | 1 | | 2 | 54 | | 3 | 15 | | 4 | 44 | | 5 | 34 | | 6 | 7 | | 7 | 16 | | 8 | 23 | | 9 | 12 | | 10 | 14 | | 11 | 17 | | 12 | 13 | | 13 | 21 | | 14 | 47 | | 15 | 10 | | 16 | 11 | | 17 | 17 | | 18 | 15 | | 19 | 10 | | 20 | 4 | | 21 | 16 | | 22 | 11 | | 23 | 11 | | 24 | 9 | | 25 | 16 | | 26 | 22 | | 27 | 10 | | 28 | 15 | | 29 | 17 | | 30 | 10 | | 31 | 14 | | 32 | 8 | | 33 | 10 | | 34 | 14 | | 35 | 14 | | 36 | 17 | | 37 | 4 | | 38 | 8 | | 39 | 31 | | 40 | 4 | | 41 | 14 | | 42 | 11 | | 43 | 32 | | 44 | 12 | | 45 | 3 | | 46 | 8 | | 47 | 10 | | 48 | 7 | | 49 | 7 |
| |
| 99.51% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 61 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 97 | | matches | (empty) | |
| 45.45% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 88 | | ratio | 0.034 | | matches | | 0 | "He scrutinised her—the sharp line of her jaw, the straight black hair that grazed her shoulders, the small crescent scar on her wrist peeking from the sleeve." | | 1 | "For a heartbeat, the years of separation were tangible—thick and suffocating—before Aurora turned abruptly." | | 2 | "They both glanced up—an unexpected presence, unfamiliar, whose eyes flicked between them like a predator smelling weakness." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 322 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.018633540372670808 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006211180124223602 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 9.27 | | std | 5.76 | | cv | 0.621 | | sampleLengths | | 0 | 21 | | 1 | 22 | | 2 | 14 | | 3 | 1 | | 4 | 18 | | 5 | 17 | | 6 | 19 | | 7 | 14 | | 8 | 1 | | 9 | 27 | | 10 | 10 | | 11 | 7 | | 12 | 34 | | 13 | 7 | | 14 | 8 | | 15 | 8 | | 16 | 12 | | 17 | 11 | | 18 | 6 | | 19 | 6 | | 20 | 14 | | 21 | 17 | | 22 | 12 | | 23 | 1 | | 24 | 7 | | 25 | 7 | | 26 | 7 | | 27 | 19 | | 28 | 17 | | 29 | 11 | | 30 | 10 | | 31 | 11 | | 32 | 11 | | 33 | 6 | | 34 | 7 | | 35 | 8 | | 36 | 4 | | 37 | 6 | | 38 | 4 | | 39 | 10 | | 40 | 6 | | 41 | 11 | | 42 | 5 | | 43 | 6 | | 44 | 4 | | 45 | 5 | | 46 | 9 | | 47 | 7 | | 48 | 10 | | 49 | 12 |
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| 78.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4772727272727273 | | totalSentences | 88 | | uniqueOpeners | 42 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | 0 | "Definitely not the girl who" |
| | ratio | 0.018 | |
| 81.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 55 | | matches | | 0 | "His hazel eyes flickered with" | | 1 | "He scrutinised her—the sharp line" | | 2 | "He nodded towards her wrist," | | 3 | "She allowed a brief, dry" | | 4 | "She lifted her eyes sharply" | | 5 | "He drained his glass, his" | | 6 | "He looked her over again" | | 7 | "Her fingers curled reflexively." | | 8 | "She tapped the scar on" | | 9 | "He nodded, the old pain" | | 10 | "She exhaled slowly." | | 11 | "He took a rag, polishing" | | 12 | "she said finally" | | 13 | "she whispered, eyes distant" | | 14 | "she shot back, the old" | | 15 | "Her jaw clenched." | | 16 | "She hesitated, fingers brushing the" | | 17 | "They both glanced up—an unexpected" | | 18 | "she said, voice low" |
| | ratio | 0.345 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 55 | | matches | | 0 | "The door to The Raven’s" | | 1 | "A pair of bright blue" | | 2 | "Aurora Carter froze by the" | | 3 | "The voice came, quiet but" | | 4 | "Silas Blackwood, the Spymaster himself," | | 5 | "His hazel eyes flickered with" | | 6 | "Aurora’s breath hiccupped before she" | | 7 | "He scrutinised her—the sharp line" | | 8 | "The bar’s green neon sign" | | 9 | "Aurora shrugged, tracing the rim" | | 10 | "He nodded towards her wrist," | | 11 | "She allowed a brief, dry" | | 12 | "The room felt smaller, the" | | 13 | "Silas said, voice dropping an" | | 14 | "She lifted her eyes sharply" | | 15 | "He drained his glass, his" | | 16 | "The bar was littered with" | | 17 | "Aurora’s eyes flicked to the" | | 18 | "A silence stretched, holding more" | | 19 | "Aurora caught the glint that" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "He scrutinised her—the sharp line of her jaw, the straight black hair that grazed her shoulders, the small crescent scar on her wrist peeking from the sleeve." | | 1 | "Aurora caught the glint that betrayed regret beneath his careful tone." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 4 | | matches | | 0 | "Silas said, voice dropping an octave," | | 1 | "she whispered, eyes distant" | | 2 | "she shot back, the old stubbornness sparking bright again" | | 3 | "she said, voice low" |
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| 76.83% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 3 | | fancyTags | | 0 | "Silas murmured (murmur)" | | 1 | "she admitted (admit)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 41 | | tagDensity | 0.195 | | leniency | 0.39 | | rawRatio | 0.375 | | effectiveRatio | 0.146 | |