| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said slowly [slowly]" |
| | dialogueSentences | 51 | | tagDensity | 0.471 | | leniency | 0.941 | | rawRatio | 0.042 | | effectiveRatio | 0.039 | |
| 74.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1160 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "carefully" | | 1 | "nervously" | | 2 | "cautiously" | | 3 | "very" | | 4 | "slowly" |
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| 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) | |
| 13.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1160 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "down her spine" | | count | 1 |
| | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "gloom" | | 1 | "chaotic" | | 2 | "tapestry" | | 3 | "echoing" | | 4 | "silence" | | 5 | "etched" | | 6 | "traced" | | 7 | "furrowed" | | 8 | "familiar" | | 9 | "chill" | | 10 | "down her spine" | | 11 | "scanning" | | 12 | "intensity" | | 13 | "spectral" | | 14 | "whisper" | | 15 | "sinister" |
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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 | 2 | | narrationSentences | 100 | | matches | | 0 | "was desperate" | | 1 | "felt a chill" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 100 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1158 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 829 | | uniqueNames | 12 | | maxNameDensity | 2.53 | | worstName | "Harlow" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 1 | | Tube | 1 | | Davies | 8 | | Patek | 1 | | Philippe | 1 | | Harlow | 21 | | Detective | 1 | | Inspector | 1 | | Mallory | 7 | | Kowalski | 1 | | Eva | 10 | | Aurora | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Davies" | | 2 | "Patek" | | 3 | "Philippe" | | 4 | "Harlow" | | 5 | "Inspector" | | 6 | "Mallory" | | 7 | "Kowalski" | | 8 | "Eva" | | 9 | "Aurora" |
| | places | (empty) | | globalScore | 0.233 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "quite put her finger on it" |
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| 27.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.727 | | wordCount | 1158 | | matches | | 0 | "Not just the usual decay of a forgotten place, but a deeper, unsettling wrongness" | | 1 | "Not a random doodle, but a carefully etched symbol, radiating a subtle, almost imperc" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 23.16 | | std | 14.9 | | cv | 0.644 | | sampleLengths | | 0 | 55 | | 1 | 71 | | 2 | 46 | | 3 | 55 | | 4 | 30 | | 5 | 13 | | 6 | 13 | | 7 | 27 | | 8 | 19 | | 9 | 51 | | 10 | 9 | | 11 | 31 | | 12 | 8 | | 13 | 28 | | 14 | 20 | | 15 | 21 | | 16 | 25 | | 17 | 10 | | 18 | 12 | | 19 | 26 | | 20 | 32 | | 21 | 37 | | 22 | 9 | | 23 | 29 | | 24 | 7 | | 25 | 29 | | 26 | 30 | | 27 | 7 | | 28 | 22 | | 29 | 13 | | 30 | 5 | | 31 | 13 | | 32 | 2 | | 33 | 15 | | 34 | 11 | | 35 | 9 | | 36 | 15 | | 37 | 39 | | 38 | 32 | | 39 | 12 | | 40 | 14 | | 41 | 6 | | 42 | 35 | | 43 | 28 | | 44 | 9 | | 45 | 34 | | 46 | 17 | | 47 | 41 | | 48 | 24 | | 49 | 12 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 100 | | matches | | 0 | "was focused" | | 1 | "being kept" | | 2 | "was obsessed" | | 3 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 148 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 127 | | ratio | 0.008 | | matches | | 0 | "The air tasted of damp concrete and something else—something acrid, like burnt sugar and ozone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 831 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03730445246690734 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.015643802647412757 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.12 | | std | 5.5 | | cv | 0.604 | | sampleLengths | | 0 | 15 | | 1 | 14 | | 2 | 7 | | 3 | 19 | | 4 | 16 | | 5 | 10 | | 6 | 13 | | 7 | 12 | | 8 | 20 | | 9 | 16 | | 10 | 22 | | 11 | 8 | | 12 | 12 | | 13 | 12 | | 14 | 11 | | 15 | 16 | | 16 | 2 | | 17 | 2 | | 18 | 16 | | 19 | 1 | | 20 | 6 | | 21 | 7 | | 22 | 10 | | 23 | 3 | | 24 | 2 | | 25 | 11 | | 26 | 14 | | 27 | 3 | | 28 | 2 | | 29 | 8 | | 30 | 14 | | 31 | 5 | | 32 | 3 | | 33 | 18 | | 34 | 13 | | 35 | 2 | | 36 | 15 | | 37 | 9 | | 38 | 31 | | 39 | 6 | | 40 | 2 | | 41 | 11 | | 42 | 17 | | 43 | 7 | | 44 | 13 | | 45 | 8 | | 46 | 13 | | 47 | 10 | | 48 | 15 | | 49 | 2 |
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| 45.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.33070866141732286 | | totalSentences | 127 | | uniqueOpeners | 42 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | 0 | "Just then, a figure emerged" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 82 | | matches | | 0 | "She swept her gaze across" | | 1 | "He was a young man," | | 2 | "His wallet, thick with cash," | | 3 | "She ran a gloved hand" | | 4 | "She couldn’t quite put her" | | 5 | "She was focused on a" | | 6 | "It was faint, almost invisible" | | 7 | "He was a career man," | | 8 | "She showed him her sketch" | | 9 | "She nervously tucked a strand" | | 10 | "She pointed to Harlow’s sketch" | | 11 | "She knew Aurora had a" | | 12 | "She traced the lines with" | | 13 | "She turned back to the" | | 14 | "It was all starting to" | | 15 | "She noticed something she’d missed" | | 16 | "she called out" | | 17 | "She picked it up, carefully" | | 18 | "It was a compass." | | 19 | "She knew exactly what it" |
| | ratio | 0.28 | |
| 8.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 82 | | matches | | 0 | "The air tasted of damp" | | 1 | "Harlow Quinn wrinkled her nose," | | 2 | "The abandoned Tube station reeked" | | 3 | "She swept her gaze across" | | 4 | "The platform was cavernous, the" | | 5 | "Graffiti, mostly faded tags, covered" | | 6 | "A body lay sprawled near" | | 7 | "Sergeant Davies said, his voice" | | 8 | "He was a young man," | | 9 | "Harlow knelt beside the body," | | 10 | "The man’s expensive watch was" | | 11 | "His wallet, thick with cash," | | 12 | "Davies was right about the" | | 13 | "She ran a gloved hand" | | 14 | "The victim hadn’t put up" | | 15 | "Harlow observed, her voice low" | | 16 | "Harlow said, her gaze sweeping" | | 17 | "Something was missing." | | 18 | "She couldn’t quite put her" | | 19 | "Davies offered, eager to fill" |
| | ratio | 0.902 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | 0 | "While Davies grumbled and fumbled" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "Not just the usual decay of a forgotten place, but a deeper, unsettling wrongness that prickled at her skin." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 7 | | matches | | 0 | "Sergeant Davies said, his voice echoing in the vast space" | | 1 | "Harlow observed, her voice low" | | 2 | "Harlow said, her gaze sweeping the platform again" | | 3 | "Davies offered, eager to fill the silence" | | 4 | "Harlow retorted, her voice hardening" | | 5 | "Mallory asked, his voice skeptical" | | 6 | "she said, her voice barely a whisper" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 20 | | fancyCount | 10 | | fancyTags | | 0 | "Harlow observed (observe)" | | 1 | "Harlow admitted (admit)" | | 2 | "Harlow acknowledged (acknowledge)" | | 3 | "Harlow insisted (insist)" | | 4 | "Harlow retorted (retort)" | | 5 | "Eva explained (explain)" | | 6 | "she murmured (murmur)" | | 7 | "Harlow pressed (press)" | | 8 | "Eva explained (explain)" | | 9 | "Harlow snapped (snap)" |
| | dialogueSentences | 51 | | tagDensity | 0.392 | | leniency | 0.784 | | rawRatio | 0.5 | | effectiveRatio | 0.392 | |