| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "Owen said finally [finally]" | | 1 | "Owen asked quietly [quietly]" |
| | dialogueSentences | 36 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.125 | | effectiveRatio | 0.111 | |
| 81.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1097 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "very" |
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| 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) | |
| 86.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1097 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "familiar" | | 1 | "flicked" | | 2 | "throbbed" |
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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 | 62 | | matches | (empty) | |
| 73.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 9 | | totalWords | 1097 | | ratio | 0.008 | | matches | | 0 | "I see you, and I'm not going to ask." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 809 | | uniqueNames | 13 | | maxNameDensity | 0.87 | | worstName | "Owen" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Owen" | | discoveredNames | | Aurora | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | Pryce | 1 | | Eva | 1 | | Evan | 1 | | Canton | 1 | | Silas | 4 | | Owen | 7 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Raven" | | 3 | "Pryce" | | 4 | "Eva" | | 5 | "Evan" | | 6 | "Silas" | | 7 | "Owen" |
| | places | | 0 | "Golden" | | 1 | "Cardiff" | | 2 | "Canton" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like something she could walk thro" |
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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 | 1097 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 26.76 | | std | 24.64 | | cv | 0.921 | | sampleLengths | | 0 | 78 | | 1 | 75 | | 2 | 4 | | 3 | 76 | | 4 | 4 | | 5 | 33 | | 6 | 2 | | 7 | 43 | | 8 | 6 | | 9 | 4 | | 10 | 38 | | 11 | 20 | | 12 | 6 | | 13 | 3 | | 14 | 56 | | 15 | 6 | | 16 | 8 | | 17 | 54 | | 18 | 5 | | 19 | 43 | | 20 | 79 | | 21 | 12 | | 22 | 30 | | 23 | 29 | | 24 | 72 | | 25 | 25 | | 26 | 6 | | 27 | 18 | | 28 | 18 | | 29 | 2 | | 30 | 13 | | 31 | 40 | | 32 | 12 | | 33 | 19 | | 34 | 3 | | 35 | 10 | | 36 | 55 | | 37 | 6 | | 38 | 2 | | 39 | 55 | | 40 | 27 |
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| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 62 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 136 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 814 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.036855036855036855 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012285012285012284 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 13.38 | | std | 9.76 | | cv | 0.729 | | sampleLengths | | 0 | 27 | | 1 | 18 | | 2 | 12 | | 3 | 21 | | 4 | 6 | | 5 | 27 | | 6 | 19 | | 7 | 23 | | 8 | 4 | | 9 | 26 | | 10 | 26 | | 11 | 8 | | 12 | 6 | | 13 | 10 | | 14 | 4 | | 15 | 8 | | 16 | 14 | | 17 | 11 | | 18 | 2 | | 19 | 33 | | 20 | 10 | | 21 | 6 | | 22 | 4 | | 23 | 5 | | 24 | 33 | | 25 | 7 | | 26 | 6 | | 27 | 7 | | 28 | 6 | | 29 | 3 | | 30 | 23 | | 31 | 7 | | 32 | 3 | | 33 | 23 | | 34 | 6 | | 35 | 8 | | 36 | 30 | | 37 | 24 | | 38 | 5 | | 39 | 17 | | 40 | 26 | | 41 | 8 | | 42 | 13 | | 43 | 20 | | 44 | 38 | | 45 | 12 | | 46 | 11 | | 47 | 19 | | 48 | 7 | | 49 | 9 |
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| 61.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4268292682926829 | | totalSentences | 82 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 56 | | matches | | 0 | "Instead she pushed through the" | | 1 | "Somewhere in the back a" | | 2 | "Instead she felt the familiar" | | 3 | "Instead she turned her glass" |
| | ratio | 0.071 | |
| 27.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 56 | | matches | | 0 | "Her delivery bag hung heavy" | | 1 | "She had meant to go" | | 2 | "He glanced up, nodded once," | | 3 | "She followed his look." | | 4 | "He had been handsome at" | | 5 | "She heard it land in" | | 6 | "He stood, and she saw" | | 7 | "She sat across from him." | | 8 | "His gaze drifted, not rudely," | | 9 | "She had stopped noticing it" | | 10 | "He had not." | | 11 | "She knew it from the" | | 12 | "He turned his pint glass" | | 13 | "He said it without self-pity," | | 14 | "She had expected anger, if" | | 15 | "She thought of her father" | | 16 | "She thought of Evan's hand" | | 17 | "she said, and heard how" | | 18 | "He looked up" | | 19 | "She could have made something" |
| | ratio | 0.482 | |
| 85.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 56 | | matches | | 0 | "The green neon above the" | | 1 | "Her delivery bag hung heavy" | | 2 | "She had meant to go" | | 3 | "Silas was behind the bar," | | 4 | "He glanced up, nodded once," | | 5 | "She followed his look." | | 6 | "A man sat with his" | | 7 | "He had been handsome at" | | 8 | "Owen Pryce said" | | 9 | "Nobody had called her that" | | 10 | "She heard it land in" | | 11 | "He stood, and she saw" | | 12 | "She sat across from him." | | 13 | "Silas set a glass of" | | 14 | "The rain ticked against the" | | 15 | "Owen said finally" | | 16 | "His gaze drifted, not rudely," | | 17 | "She had stopped noticing it" | | 18 | "He had not." | | 19 | "She knew it from the" |
| | ratio | 0.75 | |
| 89.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 56 | | matches | | 0 | "Now his face had settled" |
| | ratio | 0.018 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 4 | | matches | | 0 | "Old maps curled on the walls, and black-and-white photographs of men in overcoats watched from the frames with the patience of people who knew how things ended." | | 1 | "She knew it from the way his eyes settled there, as if the mark were a sentence he had once learned by heart." | | 2 | "She thought of her father in his good suit, the courtroom voice that had gone quiet at the kitchen table." | | 3 | "She felt the old fence splinter under her palm, the blood warm down her arm, a small boy running with her through a street that had since been paved over and re…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0 | | effectiveRatio | 0 | |