| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "She hesitated then [then]" |
| | dialogueSentences | 50 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.063 | | effectiveRatio | 0.04 | |
| 96.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1333 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 88.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1333 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "etched" | | 1 | "grave" | | 2 | "weight" |
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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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1333 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 843 | | uniqueNames | 6 | | maxNameDensity | 1.3 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 11 | | Camden | 2 | | Tube | 1 | | Whitlock | 4 | | Eva | 10 | | Kowalski | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Whitlock" | | 2 | "Eva" | | 3 | "Kowalski" |
| | places | | | globalScore | 0.848 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | 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 | 1333 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 107 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 24.24 | | std | 25.2 | | cv | 1.04 | | sampleLengths | | 0 | 4 | | 1 | 77 | | 2 | 35 | | 3 | 8 | | 4 | 21 | | 5 | 17 | | 6 | 3 | | 7 | 7 | | 8 | 88 | | 9 | 24 | | 10 | 74 | | 11 | 3 | | 12 | 40 | | 13 | 6 | | 14 | 13 | | 15 | 90 | | 16 | 4 | | 17 | 36 | | 18 | 2 | | 19 | 61 | | 20 | 3 | | 21 | 33 | | 22 | 37 | | 23 | 4 | | 24 | 1 | | 25 | 18 | | 26 | 66 | | 27 | 1 | | 28 | 78 | | 29 | 3 | | 30 | 18 | | 31 | 48 | | 32 | 1 | | 33 | 54 | | 34 | 12 | | 35 | 7 | | 36 | 55 | | 37 | 52 | | 38 | 8 | | 39 | 18 | | 40 | 2 | | 41 | 15 | | 42 | 26 | | 43 | 1 | | 44 | 29 | | 45 | 6 | | 46 | 13 | | 47 | 2 | | 48 | 2 | | 49 | 2 |
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| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "been introduced" | | 1 | "were curled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 141 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 107 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Small, curly red hair," |
| | adverbCount | 32 | | adverbRatio | 0.037914691943127965 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009478672985781991 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 12.46 | | std | 10.27 | | cv | 0.825 | | sampleLengths | | 0 | 4 | | 1 | 17 | | 2 | 31 | | 3 | 5 | | 4 | 24 | | 5 | 20 | | 6 | 15 | | 7 | 8 | | 8 | 21 | | 9 | 14 | | 10 | 3 | | 11 | 3 | | 12 | 7 | | 13 | 2 | | 14 | 39 | | 15 | 10 | | 16 | 7 | | 17 | 5 | | 18 | 25 | | 19 | 5 | | 20 | 19 | | 21 | 21 | | 22 | 53 | | 23 | 3 | | 24 | 24 | | 25 | 16 | | 26 | 6 | | 27 | 13 | | 28 | 12 | | 29 | 28 | | 30 | 23 | | 31 | 27 | | 32 | 4 | | 33 | 14 | | 34 | 22 | | 35 | 2 | | 36 | 7 | | 37 | 10 | | 38 | 24 | | 39 | 6 | | 40 | 14 | | 41 | 3 | | 42 | 11 | | 43 | 22 | | 44 | 9 | | 45 | 14 | | 46 | 14 | | 47 | 4 | | 48 | 1 | | 49 | 13 |
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| 89.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5514018691588785 | | totalSentences | 107 | | uniqueOpeners | 59 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 68 | | matches | | 0 | "Always the back wall of" | | 1 | "Faintly, unmistakably warm, the way" | | 2 | "Somewhere beyond the wall, something" |
| | ratio | 0.044 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 68 | | matches | | 0 | "He sat in the middle" | | 1 | "She swept her torch low" | | 2 | "She shook her head" | | 3 | "She stood with her hands" | | 4 | "She tucked a curl behind" | | 5 | "She looked at the wall" | | 6 | "She crouched beside Eva" | | 7 | "Her eyes had gone to" | | 8 | "She held it up to" | | 9 | "She hesitated, then the words" | | 10 | "She bagged the tally and" | | 11 | "She flipped it open." | | 12 | "She turned on her heel," | | 13 | "She walked three paces along" | | 14 | "She laid her palm flat" | | 15 | "He came, sighing, and stood" | | 16 | "She nodded at the wall." | | 17 | "He rapped the brick twice" | | 18 | "He smirked and turned to" |
| | ratio | 0.279 | |
| 62.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 68 | | matches | | 0 | "The dead man drowned." | | 1 | "Harlow Quinn crouched over the" | | 2 | "The platform stretched away into" | | 3 | "DS Whitlock picked his way" | | 4 | "Quinn stood and turned a" | | 5 | "The dust lay over the" | | 6 | "Nothing had walked to him." | | 7 | "He sat in the middle" | | 8 | "Whitlock rubbed his jaw" | | 9 | "She swept her torch low" | | 10 | "She shook her head" | | 11 | "Whitlock opened his mouth, and" | | 12 | "Quinn left him to it" | | 13 | "She stood with her hands" | | 14 | "She tucked a curl behind" | | 15 | "Eva led her to the" | | 16 | "The photographer stepped aside, and" | | 17 | "The strokes were jagged, overlapping," | | 18 | "A fine white dust of" | | 19 | "Eva crouched, glasses slipping down" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "The dust lay over the platform like fresh snow, marred only by the boards they had laid and the trail of boot prints near the entrance stairs, the urban explore…" | | 1 | "And in the inside breast pocket, a small brass compass, its casing furred green with verdigris, its face etched with fine sigils that matched the wall." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "DS Whitlock picked, his paper suit rustling" | | 1 | "Eva crouched, glasses slipping down her nose" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.04 | | leniency | 0.08 | | rawRatio | 0 | | effectiveRatio | 0 | |