| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said again [again]" |
| | dialogueSentences | 59 | | tagDensity | 0.39 | | leniency | 0.78 | | rawRatio | 0.043 | | effectiveRatio | 0.034 | |
| 86.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1809 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "truly" | | 1 | "really" | | 2 | "softly" | | 3 | "slightly" | | 4 | "slowly" |
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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) | |
| 72.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1809 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "clandestine" | | 1 | "marble" | | 2 | "glinting" | | 3 | "silence" | | 4 | "weight" | | 5 | "flickered" | | 6 | "unspoken" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1803 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 1 | | matches | | 0 | "She opened her mouth, and for the first time in years, she spoke not to defend herself, but to confess." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 948 | | uniqueNames | 18 | | maxNameDensity | 1.79 | | worstName | "Aurora" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Soho | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Prague | 3 | | London | 2 | | Cardiff | 3 | | Blackwood | 1 | | Irish | 1 | | Welsh | 1 | | Yu-Fei | 1 | | Pre-Law | 1 | | Evan | 1 | | Eva | 2 | | Silas | 9 | | Aurora | 17 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Blackwood" | | 4 | "Evan" | | 5 | "Eva" | | 6 | "Silas" | | 7 | "Aurora" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Irish" | | 5 | "Yu-Fei" |
| | globalScore | 0.603 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.555 | | wordCount | 1803 | | matches | | 0 | "not to defend herself, but to confess" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 125 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 29.56 | | std | 22.57 | | cv | 0.763 | | sampleLengths | | 0 | 86 | | 1 | 78 | | 2 | 2 | | 3 | 12 | | 4 | 73 | | 5 | 35 | | 6 | 4 | | 7 | 3 | | 8 | 15 | | 9 | 61 | | 10 | 15 | | 11 | 57 | | 12 | 13 | | 13 | 29 | | 14 | 4 | | 15 | 7 | | 16 | 53 | | 17 | 10 | | 18 | 23 | | 19 | 75 | | 20 | 10 | | 21 | 36 | | 22 | 4 | | 23 | 22 | | 24 | 12 | | 25 | 10 | | 26 | 39 | | 27 | 14 | | 28 | 19 | | 29 | 30 | | 30 | 33 | | 31 | 8 | | 32 | 51 | | 33 | 20 | | 34 | 31 | | 35 | 39 | | 36 | 18 | | 37 | 6 | | 38 | 15 | | 39 | 30 | | 40 | 20 | | 41 | 25 | | 42 | 6 | | 43 | 62 | | 44 | 24 | | 45 | 45 | | 46 | 62 | | 47 | 13 | | 48 | 77 | | 49 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 88 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 169 | | matches | (empty) | |
| 51.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 125 | | ratio | 0.032 | | matches | | 0 | "Black-and-white photographs lined the walls beside creased maps of Prague, London, Cardiff—cities she had walked through but never truly belonged to." | | 1 | "The crescent scar on her left wrist—small, pale, from a childhood accident—showed as she tugged her sleeve back to adjust her cuff." | | 2 | "By day, she delivered meals; by night, she lived in the flat above this bar." | | 3 | "The old maps on the walls, the black-and-white photographs of strangers, the secret door behind the bookshelf—all of it pressed in." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1063 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.027281279397930385 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007525870178739417 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 14.42 | | std | 11.14 | | cv | 0.772 | | sampleLengths | | 0 | 24 | | 1 | 30 | | 2 | 11 | | 3 | 21 | | 4 | 6 | | 5 | 24 | | 6 | 30 | | 7 | 18 | | 8 | 2 | | 9 | 5 | | 10 | 7 | | 11 | 13 | | 12 | 11 | | 13 | 27 | | 14 | 22 | | 15 | 6 | | 16 | 29 | | 17 | 4 | | 18 | 3 | | 19 | 15 | | 20 | 3 | | 21 | 25 | | 22 | 15 | | 23 | 9 | | 24 | 9 | | 25 | 6 | | 26 | 9 | | 27 | 18 | | 28 | 39 | | 29 | 6 | | 30 | 3 | | 31 | 4 | | 32 | 29 | | 33 | 4 | | 34 | 7 | | 35 | 18 | | 36 | 10 | | 37 | 15 | | 38 | 5 | | 39 | 5 | | 40 | 4 | | 41 | 6 | | 42 | 23 | | 43 | 7 | | 44 | 15 | | 45 | 8 | | 46 | 22 | | 47 | 23 | | 48 | 10 | | 49 | 12 |
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| 32.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.216 | | totalSentences | 125 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 31.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 72 | | matches | | 0 | "His grey-streaked auburn hair, tied" | | 1 | "He stood six feet and" | | 2 | "His voice did not lift." | | 3 | "It settled into the room" | | 4 | "Her straight black hair, shoulder-length" | | 5 | "His hazel eyes held her" | | 6 | "She had left Pre-Law behind" | | 7 | "She never entered the Nest" | | 8 | "She never expected him to" | | 9 | "She glanced at the bookshelf" | | 10 | "She had delivered meals to" | | 11 | "She had watched them close" | | 12 | "She never spoke of it." | | 13 | "She never spoke of much." | | 14 | "She looked at him then," | | 15 | "He had been a retired" | | 16 | "He had opened this bar" | | 17 | "She reached for the box," | | 18 | "She pulled her hand back." | | 19 | "She used his nickname softly," |
| | ratio | 0.472 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 72 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora Carter pushed through the" | | 2 | "Silas Blackwood turned from the" | | 3 | "His grey-streaked auburn hair, tied" | | 4 | "He stood six feet and" | | 5 | "The silver signet ring on" | | 6 | "His voice did not lift." | | 7 | "It settled into the room" | | 8 | "Aurora set the box on" | | 9 | "Her straight black hair, shoulder-length" | | 10 | "The crescent scar on her" | | 11 | "Silas did not respond to" | | 12 | "His hazel eyes held her" | | 13 | "Aurora’s jaw tightened." | | 14 | "She had left Pre-Law behind" | | 15 | "She never entered the Nest" | | 16 | "She never expected him to" | | 17 | "Silas leaned on the bar," | | 18 | "The words struck like old" | | 19 | "Aurora pulled back." |
| | ratio | 0.931 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "His grey-streaked auburn hair, tied back in a loose knot, framed a beard trimmed neat against a jaw that had sharpened with the years." | | 1 | "She looked at the green neon, at the old maps, at the photographs of strangers who had become part of the bar’s marrow." | | 2 | "Silas smiled, cold and precise, the kind of smile that came from years of deception and survival." |
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| 81.52% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 2 | | matches | | 0 | "Silas leaned, his hand near hers but not touching" | | 1 | "Silas stepped, the smell of tobacco and old paper near her now" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.271 | | leniency | 0.542 | | rawRatio | 0 | | effectiveRatio | 0 | |