| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 3 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" | | 1 | "She pulled back [back]" | | 2 | "he asked finally [finally]" |
| | dialogueSentences | 68 | | tagDensity | 0.279 | | leniency | 0.559 | | rawRatio | 0.158 | | effectiveRatio | 0.088 | |
| 81.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1628 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "lightly" | | 2 | "slowly" | | 3 | "really" |
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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) | |
| 69.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1628 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "scanned" | | 1 | "flickered" | | 2 | "measured" | | 3 | "whisper" | | 4 | "silence" | | 5 | "charm" | | 6 | "weight" | | 7 | "perfect" | | 8 | "footsteps" | | 9 | "familiar" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 142 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1617 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1182 | | uniqueNames | 10 | | maxNameDensity | 0.34 | | worstName | "Silas" | | maxWindowNameDensity | 1 | | worstWindowName | "Silas" | | discoveredNames | | London | 1 | | Carter | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Silas | 4 | | Cardiff | 1 | | Brendan | 1 | | Liam | 2 | | Aurora | 3 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Brendan" | | 5 | "Liam" | | 6 | "Aurora" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 5 | | matches | | 0 | "not quite a downpour, not quite a drizzle, just enough to make the pavement gleam under the streetlights and send the evening crowd ducking into doorways" | | 1 | "not quite a drizzle, just enough to make the pavement gleam under the streetlights and send the evening crowd ducking into doorways" | | 2 | "quite reach his eyes" | | 3 | "quite know how to file" | | 4 | "not quite to a whisper but close" | | 5 | "appeared beside her, leaning on his good leg, and said nothing for a long moment" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.618 | | wordCount | 1617 | | matches | | 0 | "not quite to a whisper but close" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 142 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 21.56 | | std | 22.94 | | cv | 1.064 | | sampleLengths | | 0 | 102 | | 1 | 7 | | 2 | 87 | | 3 | 38 | | 4 | 3 | | 5 | 1 | | 6 | 48 | | 7 | 1 | | 8 | 41 | | 9 | 31 | | 10 | 45 | | 11 | 16 | | 12 | 2 | | 13 | 36 | | 14 | 16 | | 15 | 2 | | 16 | 2 | | 17 | 56 | | 18 | 11 | | 19 | 1 | | 20 | 3 | | 21 | 15 | | 22 | 56 | | 23 | 19 | | 24 | 26 | | 25 | 5 | | 26 | 8 | | 27 | 49 | | 28 | 48 | | 29 | 6 | | 30 | 11 | | 31 | 10 | | 32 | 27 | | 33 | 3 | | 34 | 21 | | 35 | 97 | | 36 | 9 | | 37 | 8 | | 38 | 5 | | 39 | 11 | | 40 | 32 | | 41 | 4 | | 42 | 14 | | 43 | 18 | | 44 | 14 | | 45 | 13 | | 46 | 1 | | 47 | 2 | | 48 | 67 | | 49 | 2 |
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| 97.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 93 | | matches | | 0 | "was married" | | 1 | "was gone" |
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| 81.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 225 | | matches | | 0 | "were waiting" | | 1 | "wasn’t waiting" | | 2 | "was taking" | | 3 | "was coming" |
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| 2.01% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 142 | | ratio | 0.049 | | matches | | 0 | "The rain had settled into that particular London rhythm—not quite a downpour, not quite a drizzle, just enough to make the pavement gleam under the streetlights and send the evening crowd ducking into doorways." | | 1 | "The bell above the door chimed—a tinny, off-key note that Silas refused to fix—and a man stepped in out of the wet." | | 2 | "He turned at the sound of her voice, and for a moment his face was a blank canvas—polite, neutral, the expression of a man who had been to too many networking events and learned to hide his reactions." | | 3 | "He’d learned to drink like that, too—acquired taste, acquired posture, acquired life." | | 4 | "She remembered that about him—the careful consideration before every response, the way he treated conversation like a chess game." | | 5 | "His wedding ring caught the light—plain gold, no fuss." | | 6 | "That was part of his charm and part of his failure—he knew when to stop asking, but he never knew when to start." |
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| 91.53% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1191 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 51 | | adverbRatio | 0.042821158690176324 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008396305625524769 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 142 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 142 | | mean | 11.39 | | std | 9.42 | | cv | 0.828 | | sampleLengths | | 0 | 34 | | 1 | 30 | | 2 | 38 | | 3 | 5 | | 4 | 2 | | 5 | 22 | | 6 | 24 | | 7 | 16 | | 8 | 7 | | 9 | 18 | | 10 | 10 | | 11 | 28 | | 12 | 3 | | 13 | 1 | | 14 | 38 | | 15 | 6 | | 16 | 2 | | 17 | 2 | | 18 | 1 | | 19 | 12 | | 20 | 9 | | 21 | 20 | | 22 | 9 | | 23 | 22 | | 24 | 16 | | 25 | 29 | | 26 | 15 | | 27 | 1 | | 28 | 2 | | 29 | 14 | | 30 | 22 | | 31 | 10 | | 32 | 6 | | 33 | 2 | | 34 | 2 | | 35 | 23 | | 36 | 18 | | 37 | 3 | | 38 | 12 | | 39 | 11 | | 40 | 1 | | 41 | 3 | | 42 | 15 | | 43 | 18 | | 44 | 19 | | 45 | 12 | | 46 | 7 | | 47 | 17 | | 48 | 2 | | 49 | 22 |
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| 47.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.34507042253521125 | | totalSentences | 142 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 82 | | matches | | 0 | "Then it clicked." | | 1 | "Then recognition flickered, and something" | | 2 | "Somewhere behind her, she heard" | | 3 | "Then he was gone, swallowed" |
| | ratio | 0.049 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 82 | | matches | | 0 | "She wasn’t waiting for anything." | | 1 | "He shook his umbrella with" | | 2 | "His suit was charcoal grey," | | 3 | "His shoes were polished despite" | | 4 | "His hair was the same" | | 5 | "It took her a full" | | 6 | "He turned at the sound" | | 7 | "She set the glass down" | | 8 | "She gestured at the bar" | | 9 | "He smiled, but it didn’t" | | 10 | "She said it lightly, but" | | 11 | "She watched him take a" | | 12 | "He trailed off, and she" | | 13 | "He gestured vaguely at her" | | 14 | "She laughed, and it came" | | 15 | "He ordered a single malt," | | 16 | "He didn’t wince." | | 17 | "He’d learned to drink like" | | 18 | "he said, setting the glass" | | 19 | "He nodded slowly, the way" |
| | ratio | 0.683 | |
| 14.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 82 | | matches | | 0 | "The rain had settled into" | | 1 | "Aurora Carter stood behind the" | | 2 | "The green neon sign outside" | | 3 | "She wasn’t waiting for anything." | | 4 | "The bell above the door" | | 5 | "He shook his umbrella with" | | 6 | "His suit was charcoal grey," | | 7 | "His shoes were polished despite" | | 8 | "His hair was the same" | | 9 | "It took her a full" | | 10 | "He turned at the sound" | | 11 | "She set the glass down" | | 12 | "She gestured at the bar" | | 13 | "He smiled, but it didn’t" | | 14 | "She said it lightly, but" | | 15 | "She watched him take a" | | 16 | "He trailed off, and she" | | 17 | "He gestured vaguely at her" | | 18 | "She laughed, and it came" | | 19 | "He ordered a single malt," |
| | ratio | 0.89 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "Aurora Carter stood behind the bar of The Raven’s Nest, wiping a glass with a rag that had seen better days, watching the condensation bead and slide down the w…" | | 1 | "He turned at the sound of her voice, and for a moment his face was a blank canvas—polite, neutral, the expression of a man who had been to too many networking e…" | | 2 | "He ordered a single malt, neat, and she poured it with the practiced hand of someone who had done this a thousand times." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "She said, but the words sat heavy in her mouth" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 68 | | tagDensity | 0.147 | | leniency | 0.294 | | rawRatio | 0 | | effectiveRatio | 0 | |