| 76.92% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 29 | | adverbTagCount | 4 | | adverbTags | | 0 | "Liv tapped perfectly [perfectly]" | | 1 | "Liv's voice softened slightly [slightly]" | | 2 | "Rory said quietly [quietly]" | | 3 | "Rory looked around [around]" |
| | dialogueSentences | 65 | | tagDensity | 0.446 | | leniency | 0.892 | | rawRatio | 0.138 | | effectiveRatio | 0.123 | |
| 63.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1243 | | totalAiIsmAdverbs | 9 | | found | | | highlights | | 0 | "softly" | | 1 | "perfectly" | | 2 | "slightly" | | 3 | "quickly" | | 4 | "suddenly" | | 5 | "slowly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 51.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1243 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "perfect" | | 1 | "could feel" | | 2 | "weight" | | 3 | "unspoken" | | 4 | "silence" | | 5 | "reminder" | | 6 | "familiar" | | 7 | "unravel" | | 8 | "glinting" | | 9 | "methodical" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 82 | | matches | (empty) | |
| 90.59% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1235 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 812 | | uniqueNames | 9 | | maxNameDensity | 3.45 | | worstName | "Liv" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Liv" | | discoveredNames | | Rory | 25 | | Silas | 6 | | Raven | 3 | | Nest | 3 | | Cardiff | 1 | | Williams | 1 | | London | 1 | | Liv | 28 | | Corporate | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Williams" | | 5 | "Liv" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1235 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 22.87 | | std | 11.49 | | cv | 0.502 | | sampleLengths | | 0 | 26 | | 1 | 14 | | 2 | 49 | | 3 | 29 | | 4 | 55 | | 5 | 8 | | 6 | 29 | | 7 | 23 | | 8 | 18 | | 9 | 32 | | 10 | 23 | | 11 | 39 | | 12 | 18 | | 13 | 43 | | 14 | 12 | | 15 | 16 | | 16 | 10 | | 17 | 19 | | 18 | 31 | | 19 | 9 | | 20 | 12 | | 21 | 3 | | 22 | 29 | | 23 | 15 | | 24 | 23 | | 25 | 34 | | 26 | 15 | | 27 | 26 | | 28 | 18 | | 29 | 27 | | 30 | 37 | | 31 | 23 | | 32 | 36 | | 33 | 8 | | 34 | 34 | | 35 | 15 | | 36 | 27 | | 37 | 22 | | 38 | 14 | | 39 | 34 | | 40 | 14 | | 41 | 18 | | 42 | 19 | | 43 | 9 | | 44 | 32 | | 45 | 44 | | 46 | 35 | | 47 | 25 | | 48 | 2 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 94.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 118 | | ratio | 0.017 | | matches | | 0 | "But her eyes—they hadn't changed." | | 1 | "Rory could smell Liv's perfume—expensive, nothing like the cheap body spray she used to douse herself in at university." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 820 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 31 | | adverbRatio | 0.03780487804878049 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.01707317073170732 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 10.47 | | std | 6 | | cv | 0.574 | | sampleLengths | | 0 | 11 | | 1 | 15 | | 2 | 14 | | 3 | 3 | | 4 | 22 | | 5 | 2 | | 6 | 1 | | 7 | 21 | | 8 | 12 | | 9 | 17 | | 10 | 2 | | 11 | 20 | | 12 | 19 | | 13 | 5 | | 14 | 9 | | 15 | 8 | | 16 | 8 | | 17 | 21 | | 18 | 17 | | 19 | 6 | | 20 | 12 | | 21 | 6 | | 22 | 25 | | 23 | 7 | | 24 | 2 | | 25 | 2 | | 26 | 19 | | 27 | 13 | | 28 | 17 | | 29 | 9 | | 30 | 11 | | 31 | 7 | | 32 | 18 | | 33 | 16 | | 34 | 5 | | 35 | 4 | | 36 | 8 | | 37 | 4 | | 38 | 9 | | 39 | 7 | | 40 | 6 | | 41 | 4 | | 42 | 8 | | 43 | 11 | | 44 | 11 | | 45 | 7 | | 46 | 12 | | 47 | 1 | | 48 | 9 | | 49 | 10 |
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| 68.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4491525423728814 | | totalSentences | 118 | | uniqueOpeners | 53 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 75 | | matches | | 0 | "he said, using the name" | | 1 | "She couldn't respond." | | 2 | "Her voice cut through the" | | 3 | "Her blonde hair, once perpetually" | | 4 | "He disappeared into the back" | | 5 | "She avoided meeting Liv's eyes," | | 6 | "She turned to leave, then" | | 7 | "She picked up Liv's glass" | | 8 | "She knew without looking who" |
| | ratio | 0.12 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 75 | | matches | | 0 | "The glass slipped from Rory's" | | 1 | "Amber liquid pooled at her" | | 2 | "he said, using the name" | | 3 | "She couldn't respond." | | 4 | "The friend who had pleaded" | | 5 | "Liv said, stepping into the" | | 6 | "Her voice cut through the" | | 7 | "Liv had filled out, traded" | | 8 | "Her blonde hair, once perpetually" | | 9 | "Rory's voice sounded foreign even" | | 10 | "Silas deposited the dustpan and" | | 11 | "He disappeared into the back" | | 12 | "Liv approached the bar, her" | | 13 | "Rory grabbed a cloth and" | | 14 | "Liv slid onto a barstool," | | 15 | "Liv had always been the" | | 16 | "Rory said, dumping the broken" | | 17 | "She avoided meeting Liv's eyes," | | 18 | "Liv tapped perfectly manicured nails" | | 19 | "The old maps and black-and-white" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 3 | | matches | | 0 | "She avoided meeting Liv's eyes, focusing instead on wiping down the spot where the glass had fallen." | | 1 | "The old maps and black-and-white photographs that lined the walls of The Raven's Nest seemed to watch them." | | 2 | "Rory's fingers found the small crescent-shaped scar on her left wrist, a childhood accident that had left a permanent reminder of how quickly things could go wr…" |
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| 73.28% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 29 | | uselessAdditionCount | 3 | | matches | | 0 | "He disappeared, the bookshelf sealing behind him with a soft click" | | 1 | "Liv approached, her heels clicking against the wooden floor" | | 2 | "Rory began, her movements methodical" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 65 | | tagDensity | 0.108 | | leniency | 0.215 | | rawRatio | 0.143 | | effectiveRatio | 0.031 | |