| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1037 | | totalAiIsmAdverbs | 2 | | 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) | |
| 71.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1037 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "wavering" | | 1 | "echoing" | | 2 | "tension" | | 3 | "etched" | | 4 | "familiar" | | 5 | "traced" |
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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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 7 | | markdownWords | 7 | | totalWords | 1023 | | ratio | 0.007 | | matches | | 0 | "deliberate" | | 1 | "gate" | | 2 | "things" | | 3 | "marked" | | 4 | "stretched" | | 5 | "took" | | 6 | "invitation" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 745 | | uniqueNames | 5 | | maxNameDensity | 1.21 | | worstName | "Whitaker" | | maxWindowNameDensity | 2 | | worstWindowName | "Whitaker" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 8 | | Tom | 1 | | Whitaker | 9 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tom" | | 3 | "Whitaker" |
| | places | (empty) | | globalScore | 0.896 | | windowScore | 1 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like instruments, though she could" | | 1 | "as if reaching for something just out of grasp" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1023 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 26.92 | | std | 22.6 | | cv | 0.84 | | sampleLengths | | 0 | 70 | | 1 | 25 | | 2 | 23 | | 3 | 76 | | 4 | 39 | | 5 | 11 | | 6 | 6 | | 7 | 69 | | 8 | 19 | | 9 | 31 | | 10 | 2 | | 11 | 31 | | 12 | 4 | | 13 | 36 | | 14 | 26 | | 15 | 38 | | 16 | 16 | | 17 | 59 | | 18 | 9 | | 19 | 24 | | 20 | 6 | | 21 | 75 | | 22 | 32 | | 23 | 3 | | 24 | 7 | | 25 | 13 | | 26 | 5 | | 27 | 3 | | 28 | 84 | | 29 | 18 | | 30 | 23 | | 31 | 32 | | 32 | 7 | | 33 | 10 | | 34 | 33 | | 35 | 14 | | 36 | 10 | | 37 | 34 |
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| 88.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 83 | | matches | | 0 | "been carved" | | 1 | "been scratched" | | 2 | "been pulled" | | 3 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 141 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 109 | | ratio | 0.055 | | matches | | 0 | "Detective Harlow Quinn stepped over the police tape, her boots crunching on broken glass and something finer—bone fragments, maybe." | | 1 | "The stall’s contents were scattered—jars of unlabelled powders, bundles of dried herbs, a few small brass objects that looked like instruments, though she couldn’t guess for what." | | 2 | "A symbol had been carved into one side—a crescent moon cradling a dagger." | | 3 | "The surface wasn’t just dusty—it was *marked*." | | 4 | "His clothes were ordinary—jeans, a dark jacket, scuffed boots—but his face was wrong." | | 5 | "A wallet, a phone with a cracked screen, and—her fingers brushed something cold." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 756 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03835978835978836 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007936507936507936 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 9.39 | | std | 6.68 | | cv | 0.712 | | sampleLengths | | 0 | 19 | | 1 | 14 | | 2 | 19 | | 3 | 18 | | 4 | 14 | | 5 | 11 | | 6 | 3 | | 7 | 20 | | 8 | 15 | | 9 | 19 | | 10 | 22 | | 11 | 4 | | 12 | 11 | | 13 | 3 | | 14 | 2 | | 15 | 20 | | 16 | 16 | | 17 | 3 | | 18 | 4 | | 19 | 7 | | 20 | 2 | | 21 | 4 | | 22 | 3 | | 23 | 5 | | 24 | 27 | | 25 | 22 | | 26 | 12 | | 27 | 6 | | 28 | 13 | | 29 | 8 | | 30 | 12 | | 31 | 5 | | 32 | 6 | | 33 | 2 | | 34 | 8 | | 35 | 23 | | 36 | 2 | | 37 | 2 | | 38 | 9 | | 39 | 9 | | 40 | 11 | | 41 | 5 | | 42 | 2 | | 43 | 10 | | 44 | 8 | | 45 | 8 | | 46 | 3 | | 47 | 10 | | 48 | 13 | | 49 | 3 |
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| 55.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3577981651376147 | | totalSentences | 109 | | uniqueOpeners | 39 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 76 | | matches | | 0 | "Instead, she moved toward the" | | 1 | "Just that unnatural stillness, like" |
| | ratio | 0.026 | |
| 88.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 76 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "It was quieter." | | 2 | "He didn’t turn as she" | | 3 | "She ignored that." | | 4 | "Her gaze swept the scene." | | 5 | "She exhaled through her nose." | | 6 | "She stood, her sharp jaw" | | 7 | "She pointed to the ground" | | 8 | "She cut herself off" | | 9 | "He held up a small," | | 10 | "Her fingers tightened." | | 11 | "She didn’t look at him." | | 12 | "Her gaze locked onto the" | | 13 | "She turned the token over" | | 14 | "He stared at her." | | 15 | "She didn’t answer." | | 16 | "Her breath fogged as she" | | 17 | "She stood, brushing dust from" | | 18 | "His clothes were ordinary—jeans, a" | | 19 | "His eyes were open, the" |
| | ratio | 0.329 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 76 | | matches | | 0 | "The Tube tunnel stretched ahead," | | 1 | "Water dripped somewhere in the" | | 2 | "Detective Harlow Quinn stepped over" | | 3 | "The air smelled of damp" | | 4 | "A uniformed officer nodded as" | | 5 | "Quinn didn’t reply." | | 6 | "She adjusted the worn leather" | | 7 | "The abandoned station opened up" | | 8 | "A makeshift stall stood in" | | 9 | "The energy here wasn’t the" | | 10 | "It was quieter." | | 11 | "DS Tom Whitaker stood near" | | 12 | "He didn’t turn as she" | | 13 | "Quinn stopped beside him." | | 14 | "She ignored that." | | 15 | "Her gaze swept the scene." | | 16 | "The stall’s contents were scattered—jars" | | 17 | "A compass lay near her" | | 18 | "The needle twitched, just slightly," | | 19 | "Whitaker followed her line of" |
| | ratio | 0.921 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 5 | | matches | | 0 | "The Tube tunnel stretched ahead, lit by flickering emergency bulbs that cast long, wavering shadows against the curved walls." | | 1 | "The air smelled of damp concrete and something older, something that made the hairs on her arms prickle." | | 2 | "A compass lay near her feet, its casing dull with verdigris, its face etched with symbols that weren’t any language she recognised." | | 3 | "The same crescent-and-dagger from the token, repeated in a pattern that made her eyes ache if she traced it too long." | | 4 | "The body lay sprawled near the tracks, one arm outstretched, fingers curled as if reaching for something just out of grasp." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, her sharp jaw tightening" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |