| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 95 | | tagDensity | 0.126 | | leniency | 0.253 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2135 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "slowly" |
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| 80.00% | 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) | |
| 71.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2135 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "gleaming" | | 1 | "charm" | | 2 | "velvet" | | 3 | "dance" | | 4 | "etched" | | 5 | "weight" | | 6 | "glint" | | 7 | "traced" | | 8 | "scanning" | | 9 | "shattered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 162 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 162 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 245 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2135 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 74 | | wordCount | 1436 | | uniqueNames | 9 | | maxNameDensity | 2.09 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 30 | | Malik | 1 | | Patel | 25 | | Vale | 1 | | Eva | 13 | | Kowalski | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Malik" | | 4 | "Patel" | | 5 | "Eva" | | 6 | "Kowalski" |
| | places | | | globalScore | 0.455 | | windowScore | 0.167 | |
| 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 | 0.468 | | wordCount | 2135 | | matches | | 0 | "Not on our books for much, but Customs have him in two antique smuggling reports" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 245 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 130 | | mean | 16.42 | | std | 16.74 | | cv | 1.019 | | sampleLengths | | 0 | 30 | | 1 | 9 | | 2 | 77 | | 3 | 11 | | 4 | 2 | | 5 | 9 | | 6 | 57 | | 7 | 7 | | 8 | 17 | | 9 | 8 | | 10 | 16 | | 11 | 7 | | 12 | 13 | | 13 | 19 | | 14 | 59 | | 15 | 3 | | 16 | 52 | | 17 | 12 | | 18 | 10 | | 19 | 19 | | 20 | 79 | | 21 | 5 | | 22 | 2 | | 23 | 3 | | 24 | 21 | | 25 | 33 | | 26 | 11 | | 27 | 1 | | 28 | 16 | | 29 | 20 | | 30 | 6 | | 31 | 7 | | 32 | 5 | | 33 | 13 | | 34 | 85 | | 35 | 6 | | 36 | 3 | | 37 | 2 | | 38 | 8 | | 39 | 17 | | 40 | 7 | | 41 | 26 | | 42 | 3 | | 43 | 2 | | 44 | 50 | | 45 | 10 | | 46 | 3 | | 47 | 2 | | 48 | 12 | | 49 | 38 |
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| 98.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 162 | | matches | | 0 | "been cracked" | | 1 | "been ripped" | | 2 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 251 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 245 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1439 | | adjectiveStacks | 1 | | stackExamples | | 0 | "other lay open beside his" |
| | adverbCount | 33 | | adverbRatio | 0.022932592077831826 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004169562195969423 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 245 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 245 | | mean | 8.71 | | std | 7.39 | | cv | 0.848 | | sampleLengths | | 0 | 30 | | 1 | 9 | | 2 | 6 | | 3 | 4 | | 4 | 26 | | 5 | 9 | | 6 | 16 | | 7 | 16 | | 8 | 11 | | 9 | 2 | | 10 | 9 | | 11 | 14 | | 12 | 13 | | 13 | 10 | | 14 | 20 | | 15 | 7 | | 16 | 17 | | 17 | 8 | | 18 | 16 | | 19 | 7 | | 20 | 13 | | 21 | 3 | | 22 | 5 | | 23 | 2 | | 24 | 6 | | 25 | 3 | | 26 | 9 | | 27 | 18 | | 28 | 15 | | 29 | 17 | | 30 | 3 | | 31 | 29 | | 32 | 23 | | 33 | 6 | | 34 | 6 | | 35 | 5 | | 36 | 5 | | 37 | 8 | | 38 | 11 | | 39 | 21 | | 40 | 15 | | 41 | 22 | | 42 | 6 | | 43 | 15 | | 44 | 5 | | 45 | 2 | | 46 | 3 | | 47 | 4 | | 48 | 17 | | 49 | 6 |
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| 46.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.30612244897959184 | | totalSentences | 245 | | uniqueOpeners | 75 | |
| 23.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 143 | | matches | | | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 143 | | matches | | 0 | "She ducked under the police" | | 1 | "She gave him a curt" | | 2 | "Her worn leather watch rubbed" | | 3 | "He wore his tie loose" | | 4 | "Her mouth flattened." | | 5 | "She crouched by the nearest" | | 6 | "It spread in a ring" | | 7 | "She crouched and leaned in" | | 8 | "He shrugged one shoulder." | | 9 | "She rose and turned in" | | 10 | "He snorted once." | | 11 | "She ignored him and crossed" | | 12 | "She bent and watched the" | | 13 | "She moved past the stall" | | 14 | "She crouched, touched nothing, and" | | 15 | "It cut straight across the" | | 16 | "She glanced at the ceiling." | | 17 | "He did, with visible impatience." | | 18 | "She angled her chin at" | | 19 | "She walked to the middle" |
| | ratio | 0.224 | |
| 33.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 143 | | matches | | 0 | "The stairs down to the" | | 1 | "She ducked under the police" | | 2 | "The station had once swallowed" | | 3 | "The tiled walls still held" | | 4 | "A constable near the stairwell" | | 5 | "She gave him a curt" | | 6 | "Her worn leather watch rubbed" | | 7 | "He wore his tie loose" | | 8 | "A body bag waited on" | | 9 | "Patel spotted her and spread" | | 10 | "Quinn’s eyes swept the platform" | | 11 | "Patel tilted his head toward" | | 12 | "Quinn stepped closer." | | 13 | "The smell hit her first." | | 14 | "Her mouth flattened." | | 15 | "She crouched by the nearest" | | 16 | "Shelves made from scavenged timber" | | 17 | "A handwritten price list clung" | | 18 | "Every item sat undisturbed except" | | 19 | "Patel jerked his chin at" |
| | ratio | 0.853 | |
| 34.97% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 143 | | matches | | 0 | "Now it swallowed secrets." |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 2 | | matches | | 0 | "Yet the dust near the body stayed untouched in a wide ring, as if something had landed and lifted off again without so much as brushing the grime." | | 1 | "Just a shift, a break in the stillness that made the stale air behind the platform feel thinner, as if the station had inhaled and forgotten how to breathe back…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Patel jerked, face pale beneath a knitted hood" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 95 | | tagDensity | 0.074 | | leniency | 0.147 | | rawRatio | 0.143 | | effectiveRatio | 0.021 | |