| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 54 | | tagDensity | 0.315 | | leniency | 0.63 | | rawRatio | 0.059 | | effectiveRatio | 0.037 | |
| 90.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1033 | | 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) | |
| 70.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1033 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "shattered" | | 1 | "silence" | | 2 | "flickered" | | 3 | "perfect" | | 4 | "stomach" | | 5 | "warmth" |
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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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 64 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | 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 | 1 | | markdownWords | 8 | | totalWords | 1033 | | ratio | 0.008 | | matches | | 0 | "he's not worth it, none of them are." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 13.64% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 660 | | uniqueNames | 6 | | maxNameDensity | 2.73 | | worstName | "Eva" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 15 | | Raven | 1 | | Nest | 1 | | Eva | 18 | | Cardiff | 2 | | Polaroid | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Eva" |
| | places | | | globalScore | 0.136 | | windowScore | 0.333 | |
| 78.57% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1033 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 17.81 | | std | 16.06 | | cv | 0.902 | | sampleLengths | | 0 | 52 | | 1 | 1 | | 2 | 35 | | 3 | 7 | | 4 | 2 | | 5 | 63 | | 6 | 5 | | 7 | 22 | | 8 | 7 | | 9 | 3 | | 10 | 38 | | 11 | 9 | | 12 | 2 | | 13 | 7 | | 14 | 5 | | 15 | 24 | | 16 | 36 | | 17 | 5 | | 18 | 3 | | 19 | 4 | | 20 | 30 | | 21 | 31 | | 22 | 6 | | 23 | 19 | | 24 | 7 | | 25 | 6 | | 26 | 49 | | 27 | 4 | | 28 | 37 | | 29 | 12 | | 30 | 43 | | 31 | 29 | | 32 | 2 | | 33 | 3 | | 34 | 4 | | 35 | 6 | | 36 | 51 | | 37 | 11 | | 38 | 8 | | 39 | 27 | | 40 | 11 | | 41 | 17 | | 42 | 16 | | 43 | 25 | | 44 | 5 | | 45 | 32 | | 46 | 11 | | 47 | 7 | | 48 | 6 | | 49 | 33 |
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| 88.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 64 | | matches | | 0 | "been laid" | | 1 | "were gone" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 122 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 663 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.033182503770739065 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.007541478129713424 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.43 | | std | 9.18 | | cv | 0.88 | | sampleLengths | | 0 | 15 | | 1 | 6 | | 2 | 31 | | 3 | 1 | | 4 | 3 | | 5 | 8 | | 6 | 24 | | 7 | 3 | | 8 | 4 | | 9 | 2 | | 10 | 18 | | 11 | 6 | | 12 | 1 | | 13 | 7 | | 14 | 31 | | 15 | 5 | | 16 | 13 | | 17 | 9 | | 18 | 5 | | 19 | 2 | | 20 | 3 | | 21 | 6 | | 22 | 13 | | 23 | 19 | | 24 | 9 | | 25 | 2 | | 26 | 7 | | 27 | 5 | | 28 | 11 | | 29 | 13 | | 30 | 2 | | 31 | 24 | | 32 | 10 | | 33 | 5 | | 34 | 3 | | 35 | 4 | | 36 | 11 | | 37 | 16 | | 38 | 3 | | 39 | 23 | | 40 | 8 | | 41 | 6 | | 42 | 9 | | 43 | 10 | | 44 | 7 | | 45 | 6 | | 46 | 6 | | 47 | 20 | | 48 | 23 | | 49 | 4 |
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| 67.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.43434343434343436 | | totalSentences | 99 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 50 | | matches | | 0 | "Then a smile arrived, careful," | | 1 | "Somewhere behind the bar, the" | | 2 | "Then she was gone, swallowed" |
| | ratio | 0.06 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "She didn't move to clean" | | 1 | "She just stared at the" | | 2 | "She'd cut her hair into" | | 3 | "She watched the blood bead" | | 4 | "She remembered a great deal" | | 5 | "She poured two whiskies without" | | 6 | "She didn't drink." | | 7 | "She finally drank, a small" | | 8 | "She studied her oldest friend," | | 9 | "Her hand was steady." | | 10 | "she said softly" | | 11 | "She looked instead at the" | | 12 | "She moved toward the door," |
| | ratio | 0.26 | |
| 70.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 50 | | matches | | 0 | "The pint glass slipped from" | | 1 | "She didn't move to clean" | | 2 | "She just stared at the" | | 3 | "The woman turned." | | 4 | "Eva crossed the room the" | | 5 | "Heels clicking with purpose instead" | | 6 | "She'd cut her hair into" | | 7 | "Rory crouched, gathering glass with" | | 8 | "Eva tested the name" | | 9 | "A shard bit into Rory's" | | 10 | "She watched the blood bead" | | 11 | "Eva set the gloves down," | | 12 | "She remembered a great deal" | | 13 | "She poured two whiskies without" | | 14 | "Eva lifted the glass, studied" | | 15 | "The walls behind her bristled" | | 16 | "She didn't drink." | | 17 | "A small laugh, brittle" | | 18 | "The question landed harder than" | | 19 | "Eva's smile slipped, just at" |
| | ratio | 0.78 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 35.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 3 | | matches | | 0 | "The mask cracked and behind it Rory glimpsed the girl who'd cried over a broken Polaroid, the girl who'd held her hair back outside a club in Cardiff and whispe…" | | 1 | "The gloves went back on, one finger at a time, and the practised smile returned, full force, blinding, the smile of someone who has learned to weaponise warmth." | | 2 | "Then she was gone, swallowed by the green glow of the neon sign, and Rory stood alone among the maps and the dead, her thumb throbbing, the second whisky untouc…" |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "She moved, heels clicking, then paused with her hand on the brass" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 54 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |