| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 3 | | adverbTags | | 0 | "Detective Inspector Marsh stood well [well]" | | 1 | "She turned back [back]" | | 2 | "She crouched again [again]" |
| | dialogueSentences | 70 | | tagDensity | 0.371 | | leniency | 0.743 | | rawRatio | 0.115 | | effectiveRatio | 0.086 | |
| 92.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1270 | | 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) | |
| 64.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1270 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "marble" | | 1 | "perfect" | | 2 | "echoing" | | 3 | "measured" | | 4 | "footsteps" | | 5 | "echoed" | | 6 | "chill" | | 7 | "pulsed" |
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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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 167 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1259 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 769 | | uniqueNames | 11 | | maxNameDensity | 2.21 | | worstName | "Marsh" | | maxWindowNameDensity | 4 | | worstWindowName | "Marsh" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Tube | 1 | | Inspector | 1 | | Marsh | 17 | | Rolex | 1 | | Three | 1 | | Market | 1 | | Impossible | 1 | | Ice | 1 | | October | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marsh" | | 3 | "Rolex" | | 4 | "Market" | | 5 | "Ice" | | 6 | "October" |
| | places | | | globalScore | 0.395 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "y, stopping at seemingly random directions" |
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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 | 1259 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 167 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 16.57 | | std | 12.13 | | cv | 0.732 | | sampleLengths | | 0 | 14 | | 1 | 50 | | 2 | 24 | | 3 | 30 | | 4 | 8 | | 5 | 23 | | 6 | 44 | | 7 | 20 | | 8 | 39 | | 9 | 25 | | 10 | 6 | | 11 | 10 | | 12 | 33 | | 13 | 4 | | 14 | 42 | | 15 | 18 | | 16 | 10 | | 17 | 7 | | 18 | 18 | | 19 | 34 | | 20 | 34 | | 21 | 4 | | 22 | 2 | | 23 | 38 | | 24 | 15 | | 25 | 4 | | 26 | 19 | | 27 | 12 | | 28 | 1 | | 29 | 17 | | 30 | 6 | | 31 | 15 | | 32 | 8 | | 33 | 4 | | 34 | 17 | | 35 | 26 | | 36 | 4 | | 37 | 14 | | 38 | 3 | | 39 | 23 | | 40 | 7 | | 41 | 20 | | 42 | 3 | | 43 | 46 | | 44 | 8 | | 45 | 32 | | 46 | 2 | | 47 | 4 | | 48 | 15 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 144 | | matches | | |
| 57.31% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 167 | | ratio | 0.03 | | matches | | 0 | "The victim—male, mid-thirties, expensive suit—lay sprawled across the platform edge, one arm dangling toward the tracks." | | 1 | "Pockets turned out, but not robbed—his Rolex still gleamed on his wrist." | | 2 | "The brass casing bore strange markings—symbols that made her eyes water to look at directly." | | 3 | "Her footsteps echoed wrong—too many beats for each step." | | 4 | "But the face—the face was hunger given form." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 433 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.020785219399538105 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.006928406466512702 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 167 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 167 | | mean | 7.54 | | std | 5.26 | | cv | 0.697 | | sampleLengths | | 0 | 7 | | 1 | 7 | | 2 | 24 | | 3 | 16 | | 4 | 8 | | 5 | 1 | | 6 | 1 | | 7 | 17 | | 8 | 7 | | 9 | 3 | | 10 | 8 | | 11 | 19 | | 12 | 8 | | 13 | 11 | | 14 | 12 | | 15 | 8 | | 16 | 2 | | 17 | 12 | | 18 | 9 | | 19 | 13 | | 20 | 14 | | 21 | 6 | | 22 | 11 | | 23 | 28 | | 24 | 9 | | 25 | 10 | | 26 | 6 | | 27 | 6 | | 28 | 6 | | 29 | 4 | | 30 | 11 | | 31 | 5 | | 32 | 7 | | 33 | 5 | | 34 | 5 | | 35 | 4 | | 36 | 11 | | 37 | 2 | | 38 | 6 | | 39 | 8 | | 40 | 15 | | 41 | 9 | | 42 | 9 | | 43 | 4 | | 44 | 6 | | 45 | 7 | | 46 | 11 | | 47 | 7 | | 48 | 7 | | 49 | 21 |
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| 89.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5568862275449101 | | totalSentences | 167 | | uniqueOpeners | 93 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Then, from somewhere far too" | | 1 | "Just platform and shadow and" | | 2 | "Already sweating despite the October" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 107 | | matches | | 0 | "His blood formed a perfect" | | 1 | "She'd worked with Marsh for" | | 2 | "She pulled on latex gloves," | | 3 | "She'd seen enough crime scenes" | | 4 | "She gestured to the rusted" | | 5 | "She photographed the wounds" | | 6 | "She lifted the victim's hand" | | 7 | "His fingers clutched something small." | | 8 | "She pried the object free" | | 9 | "It spun lazily, stopping at" | | 10 | "She turned back to the" | | 11 | "She walked the blood circle's" | | 12 | "Her footsteps echoed wrong—too many" | | 13 | "She crouched again, torch beam" | | 14 | "She indicated the gap" | | 15 | "They didn't just die." | | 16 | "She clicked on her torch" | | 17 | "She felt it too." | | 18 | "He was shaking." | | 19 | "She pulled it out, and" |
| | ratio | 0.262 | |
| 34.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 107 | | matches | | 0 | "Blood doesn't pool like that" | | 1 | "Detective Harlow Quinn crouched beside" | | 2 | "The victim—male, mid-thirties, expensive suit—lay" | | 3 | "His blood formed a perfect" | | 4 | "Detective Inspector Marsh stood well" | | 5 | "Quinn's jaw tightened." | | 6 | "She'd worked with Marsh for" | | 7 | "Marsh laughed, the sound echoing" | | 8 | "She pulled on latex gloves," | | 9 | "Pockets turned out, but not" | | 10 | "The blood circle measured exactly" | | 11 | "She'd seen enough crime scenes" | | 12 | "She gestured to the rusted" | | 13 | "Marsh checked his watch" | | 14 | "Quinn stood, her military bearing" | | 15 | "This was one of those" | | 16 | "Marsh squinted from his safe" | | 17 | "She photographed the wounds" | | 18 | "She lifted the victim's hand" | | 19 | "His fingers clutched something small." |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 1 | | matches | | 0 | "The thing tilted its head, studying them with eyes that weren't there." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 1 | | matches | | 0 | "Marsh laughed, the sound echoing off the curved tunnel walls" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Marsh laughed (laugh)" | | 1 | "the creature continued (continue)" |
| | dialogueSentences | 70 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 1 | | effectiveRatio | 0.057 | |