| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "She looked away [away]" | | 1 | "he asked softly [softly]" |
| | dialogueSentences | 152 | | tagDensity | 0.092 | | leniency | 0.184 | | rawRatio | 0.143 | | effectiveRatio | 0.026 | |
| 93.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2278 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "carefully" | | 2 | "softly" |
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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) | |
| 73.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2278 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "pulse" | | 2 | "weight" | | 3 | "silence" | | 4 | "charm" | | 5 | "measured" | | 6 | "scanned" | | 7 | "velvet" | | 8 | "absolutely" | | 9 | "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 | 157 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 157 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 296 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2278 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1428 | | uniqueNames | 11 | | maxNameDensity | 1.33 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 3 | | Daniel | 15 | | Vaughan | 1 | | Aurora | 19 | | Silas | 7 | | Soho | 1 | | Eva | 3 | | Rory | 1 | | Evan | 1 | | Cardiff | 2 |
| | persons | | 0 | "Raven" | | 1 | "Daniel" | | 2 | "Vaughan" | | 3 | "Aurora" | | 4 | "Silas" | | 5 | "Eva" | | 6 | "Rory" | | 7 | "Evan" |
| | places | | | globalScore | 0.835 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 99 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.439 | | wordCount | 2278 | | matches | | 0 | "not with its nicotine shadows and its old maps puckering on the walls, but it held itself together with a sort of stubborn dignity, lik" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 296 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 215 | | mean | 10.6 | | std | 15.23 | | cv | 1.438 | | sampleLengths | | 0 | 11 | | 1 | 10 | | 2 | 9 | | 3 | 105 | | 4 | 16 | | 5 | 89 | | 6 | 13 | | 7 | 1 | | 8 | 15 | | 9 | 2 | | 10 | 5 | | 11 | 5 | | 12 | 28 | | 13 | 2 | | 14 | 10 | | 15 | 9 | | 16 | 6 | | 17 | 4 | | 18 | 26 | | 19 | 10 | | 20 | 8 | | 21 | 5 | | 22 | 2 | | 23 | 2 | | 24 | 12 | | 25 | 44 | | 26 | 2 | | 27 | 4 | | 28 | 6 | | 29 | 2 | | 30 | 2 | | 31 | 44 | | 32 | 10 | | 33 | 5 | | 34 | 28 | | 35 | 4 | | 36 | 4 | | 37 | 4 | | 38 | 1 | | 39 | 7 | | 40 | 12 | | 41 | 3 | | 42 | 23 | | 43 | 46 | | 44 | 6 | | 45 | 6 | | 46 | 3 | | 47 | 7 | | 48 | 2 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 157 | | matches | | 0 | "were concerned" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 243 | | matches | | 0 | "was listening" | | 1 | "was not listening" | | 2 | "was speaking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 296 | | ratio | 0 | | matches | (empty) | |
| 98.42% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 909 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.041804180418041806 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0044004400440044 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 296 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 296 | | mean | 7.7 | | std | 6.83 | | cv | 0.887 | | sampleLengths | | 0 | 11 | | 1 | 3 | | 2 | 7 | | 3 | 9 | | 4 | 4 | | 5 | 22 | | 6 | 40 | | 7 | 6 | | 8 | 13 | | 9 | 20 | | 10 | 16 | | 11 | 12 | | 12 | 27 | | 13 | 25 | | 14 | 7 | | 15 | 7 | | 16 | 5 | | 17 | 6 | | 18 | 13 | | 19 | 1 | | 20 | 15 | | 21 | 2 | | 22 | 5 | | 23 | 5 | | 24 | 8 | | 25 | 11 | | 26 | 6 | | 27 | 1 | | 28 | 1 | | 29 | 1 | | 30 | 2 | | 31 | 10 | | 32 | 9 | | 33 | 6 | | 34 | 4 | | 35 | 3 | | 36 | 9 | | 37 | 14 | | 38 | 10 | | 39 | 8 | | 40 | 5 | | 41 | 2 | | 42 | 2 | | 43 | 4 | | 44 | 8 | | 45 | 17 | | 46 | 15 | | 47 | 5 | | 48 | 7 | | 49 | 2 |
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| 45.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.28378378378378377 | | totalSentences | 296 | | uniqueOpeners | 84 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 137 | | matches | | 0 | "Somewhere near the jukebox, someone" | | 1 | "Of course he had." | | 2 | "Only her face stayed still." | | 3 | "Once you knew where to" | | 4 | "Then the bookshelf at the" |
| | ratio | 0.036 | |
| 71.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 137 | | matches | | 0 | "She turned too fast." | | 1 | "He had grown into his" | | 2 | "He still had the same" | | 3 | "They gave him away at" | | 4 | "His grin flashed, then checked" | | 5 | "He stooped for the larger" | | 6 | "Her fingers pressed the pulse" | | 7 | "He looked down at her" | | 8 | "She let go." | | 9 | "He raised the glass in" | | 10 | "His hazel eyes moved from" | | 11 | "He never interrupted at once." | | 12 | "He made people show their" | | 13 | "His slight limp showed when" | | 14 | "He had the discreet patience" | | 15 | "She saw it in the" | | 16 | "He laughed at that, low" | | 17 | "He touched the scar in" | | 18 | "He had once kissed her" | | 19 | "She had laughed into his" |
| | ratio | 0.372 | |
| 43.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 137 | | matches | | 0 | "Aurora nearly dropped the tray" | | 1 | "She turned too fast." | | 2 | "Beer sloshed over her knuckles," | | 3 | "The Raven’s Nest never looked" | | 4 | "Men at the bar glanced" | | 5 | "A woman in a red" | | 6 | "He had grown into his" | | 7 | "University Daniel had carried puppy" | | 8 | "This man wore a dark" | | 9 | "A pale seam cut through" | | 10 | "He still had the same" | | 11 | "That was the worst part." | | 12 | "They gave him away at" | | 13 | "Aurora set the tray on" | | 14 | "His grin flashed, then checked" | | 15 | "He stooped for the larger" | | 16 | "Aurora caught his wrist before" | | 17 | "Her fingers pressed the pulse" | | 18 | "He looked down at her" | | 19 | "She let go." |
| | ratio | 0.832 | |
| 72.99% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 137 | | matches | | 0 | "Now he measured exits without" | | 1 | "Before she could answer, the" |
| | ratio | 0.015 | |
| 37.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 8 | | matches | | 0 | "University Daniel had carried puppy fat and loud opinions and a fringe he kept shoving off his forehead as if he had been born to make declarations." | | 1 | "Silas looked up from the far end of the bar, polishing a glass that was already dry." | | 2 | "He had the discreet patience of a man who had made a career out of hearing what people hid inside ordinary words." | | 3 | "He laughed at that, low and surprised, and followed her to a small table under an old photograph of Soho that looked both glamorous and diseased." | | 4 | "In lecture halls, in student kitchens, on the smoke-stained steps outside the library, they had talked as if words could build a better life than the one waitin…" | | 5 | "Eva, who did not believe in half-measures where men were concerned." | | 6 | "At the crescent scar from childhood, pale and innocent beside injuries that did not show." | | 7 | "Before she could answer, the front door opened and two men came in with the special kind of stillness that made noise around them feel staged." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 152 | | tagDensity | 0.079 | | leniency | 0.158 | | rawRatio | 0 | | effectiveRatio | 0 | |