| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned slowly [slowly]" |
| | dialogueSentences | 53 | | tagDensity | 0.189 | | leniency | 0.377 | | rawRatio | 0.1 | | effectiveRatio | 0.038 | |
| 89.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1482 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "really" |
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| 40.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 59.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1482 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "pristine" | | 2 | "scanning" | | 3 | "processed" | | 4 | "shattered" | | 5 | "etched" | | 6 | "database" | | 7 | "flickered" | | 8 | "footsteps" | | 9 | "whisper" | | 10 | "echoing" | | 11 | "silence" |
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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 | 100 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 100 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1474 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 938 | | uniqueNames | 11 | | maxNameDensity | 1.07 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 10 | | Tube | 1 | | Marcus | 1 | | Wells | 6 | | Chen | 3 | | Oxfords | 1 | | Morris | 4 | | Patek | 1 | | Philippe | 1 | | Humour | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Marcus" | | 2 | "Wells" | | 3 | "Chen" | | 4 | "Oxfords" | | 5 | "Morris" | | 6 | "Patek" | | 7 | "Philippe" | | 8 | "Humour" |
| | places | (empty) | | globalScore | 0.967 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 3 | | matches | | 0 | "quite parse" | | 1 | "seemed increasingly irrelevant to the scene before them" | | 2 | "Something between a whisper and a laugh, echoin" |
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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 | 1474 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 144 | | matches | | 0 | "lights that someone" | | 1 | "screaming that something" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 20.19 | | std | 17.17 | | cv | 0.85 | | sampleLengths | | 0 | 6 | | 1 | 72 | | 2 | 9 | | 3 | 44 | | 4 | 3 | | 5 | 22 | | 6 | 41 | | 7 | 20 | | 8 | 4 | | 9 | 6 | | 10 | 3 | | 11 | 10 | | 12 | 56 | | 13 | 2 | | 14 | 3 | | 15 | 4 | | 16 | 22 | | 17 | 6 | | 18 | 5 | | 19 | 26 | | 20 | 7 | | 21 | 51 | | 22 | 9 | | 23 | 35 | | 24 | 4 | | 25 | 5 | | 26 | 56 | | 27 | 34 | | 28 | 5 | | 29 | 28 | | 30 | 4 | | 31 | 10 | | 32 | 27 | | 33 | 47 | | 34 | 23 | | 35 | 25 | | 36 | 5 | | 37 | 12 | | 38 | 8 | | 39 | 36 | | 40 | 51 | | 41 | 11 | | 42 | 12 | | 43 | 19 | | 44 | 25 | | 45 | 8 | | 46 | 18 | | 47 | 38 | | 48 | 4 | | 49 | 64 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 100 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 158 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 144 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 767 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.027379400260756193 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005215123859191656 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 10.24 | | std | 8.19 | | cv | 0.8 | | sampleLengths | | 0 | 6 | | 1 | 19 | | 2 | 23 | | 3 | 30 | | 4 | 9 | | 5 | 10 | | 6 | 1 | | 7 | 1 | | 8 | 5 | | 9 | 27 | | 10 | 3 | | 11 | 22 | | 12 | 10 | | 13 | 1 | | 14 | 1 | | 15 | 9 | | 16 | 13 | | 17 | 3 | | 18 | 4 | | 19 | 20 | | 20 | 4 | | 21 | 6 | | 22 | 3 | | 23 | 10 | | 24 | 7 | | 25 | 19 | | 26 | 30 | | 27 | 2 | | 28 | 2 | | 29 | 1 | | 30 | 4 | | 31 | 9 | | 32 | 8 | | 33 | 5 | | 34 | 6 | | 35 | 5 | | 36 | 5 | | 37 | 21 | | 38 | 7 | | 39 | 28 | | 40 | 23 | | 41 | 9 | | 42 | 25 | | 43 | 10 | | 44 | 4 | | 45 | 3 | | 46 | 2 | | 47 | 15 | | 48 | 24 | | 49 | 17 |
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| 81.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5208333333333334 | | totalSentences | 144 | | uniqueOpeners | 75 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | 0 | "Still believed in straightforward narratives." |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 82 | | matches | | 0 | "She didn't look up" | | 1 | "She moved her hand lower," | | 2 | "She surveyed the platform, the" | | 3 | "He knelt, angling his torch." | | 4 | "She turned slowly, scanning the" | | 5 | "She shook it off and" | | 6 | "His hands were clean." | | 7 | "She moved closer to the" | | 8 | "She joined him, looking down" | | 9 | "Her mind had snagged on" | | 10 | "She withdrew a small brass" | | 11 | "She'd taken it from evidence" | | 12 | "She tucked it away" | | 13 | "It had moved when she'd" | | 14 | "She stopped at the tunnel" | | 15 | "She turned to face him" | | 16 | "She counted her steps, a" | | 17 | "Her radio crackled." | | 18 | "She pressed the button, her" | | 19 | "She released the radio and" |
| | ratio | 0.256 | |
| 63.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 82 | | matches | | 0 | "The corpse was missing its" | | 1 | "Harlow Quinn crouched beside the" | | 2 | "The abandoned Tube platform stretched" | | 3 | "The victim lay crumpled near" | | 4 | "She didn't look up" | | 5 | "DC Marcus Wells shifted his" | | 6 | "Quinn pressed two fingers to" | | 7 | "She moved her hand lower," | | 8 | "Quinn stood, her joints protesting" | | 9 | "She surveyed the platform, the" | | 10 | "The DC leaned forward, squinting" | | 11 | "Brown leather Oxfords, polished to" | | 12 | "He knelt, angling his torch." | | 13 | "The beam caught the pristine" | | 14 | "Quinn pointed to the grimy" | | 15 | "She turned slowly, scanning the" | | 16 | "Neither did she." | | 17 | "Quinn walked the perimeter of" | | 18 | "The platform had been used" | | 19 | "The air tasted metallic, thick" |
| | ratio | 0.793 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 82 | | matches | | 0 | "Now it pointed over her" | | 1 | "Before Morris died screaming about" |
| | ratio | 0.024 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 4 | | matches | | 0 | "She surveyed the platform, the peeling tile walls covered in graffiti that ranged from crude tags to elaborate murals, the flickering emergency lights that some…" | | 1 | "Her mind had snagged on something Wells had said earlier, something that seemed increasingly irrelevant to the scene before them." | | 2 | "The air was colder here, and that nameless smell was stronger, copper and spices and something that made her teeth ache." | | 3 | "She counted her steps, a habit from her military days that had served her well in the police." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "she announced (announce)" |
| | dialogueSentences | 53 | | tagDensity | 0.038 | | leniency | 0.075 | | rawRatio | 0.5 | | effectiveRatio | 0.038 | |