| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1022 | | totalAiIsmAdverbs | 1 | | 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) | |
| 51.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1022 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "churning" | | 1 | "chill" | | 2 | "measured" | | 3 | "rhythmic" | | 4 | "gleaming" | | 5 | "gloom" | | 6 | "silk" | | 7 | "velvet" | | 8 | "depths" |
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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 | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1022 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 871 | | uniqueNames | 11 | | maxNameDensity | 1.15 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Quinn | 10 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Northern | 1 | | Whitechapel | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Tomás | 8 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tube" | | 5 | "Tomás" |
| | places | | | globalScore | 0.926 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1022 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.2 | | std | 21 | | cv | 0.719 | | sampleLengths | | 0 | 27 | | 1 | 81 | | 2 | 17 | | 3 | 58 | | 4 | 2 | | 5 | 28 | | 6 | 13 | | 7 | 61 | | 8 | 25 | | 9 | 58 | | 10 | 25 | | 11 | 3 | | 12 | 27 | | 13 | 21 | | 14 | 47 | | 15 | 17 | | 16 | 25 | | 17 | 17 | | 18 | 22 | | 19 | 15 | | 20 | 76 | | 21 | 4 | | 22 | 12 | | 23 | 12 | | 24 | 26 | | 25 | 14 | | 26 | 18 | | 27 | 7 | | 28 | 36 | | 29 | 57 | | 30 | 63 | | 31 | 17 | | 32 | 16 | | 33 | 54 | | 34 | 21 |
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| 98.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 55 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 57.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 67 | | ratio | 0.03 | | matches | | 0 | "The coroner had ruled it sudden cardiac arrest; Quinn had spent thirty-six months collecting the anomalies the department chose to shred." | | 1 | "Some wore heavy oilskin dusters; others moved beneath shrouds of spun glass and heavy velvet." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 877 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.00798175598631699 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004561003420752566 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 15.25 | | std | 7.71 | | cv | 0.505 | | sampleLengths | | 0 | 12 | | 1 | 15 | | 2 | 13 | | 3 | 14 | | 4 | 22 | | 5 | 18 | | 6 | 14 | | 7 | 17 | | 8 | 16 | | 9 | 16 | | 10 | 26 | | 11 | 2 | | 12 | 9 | | 13 | 19 | | 14 | 13 | | 15 | 5 | | 16 | 7 | | 17 | 25 | | 18 | 24 | | 19 | 5 | | 20 | 20 | | 21 | 21 | | 22 | 37 | | 23 | 5 | | 24 | 20 | | 25 | 3 | | 26 | 14 | | 27 | 13 | | 28 | 21 | | 29 | 13 | | 30 | 17 | | 31 | 17 | | 32 | 17 | | 33 | 7 | | 34 | 18 | | 35 | 17 | | 36 | 10 | | 37 | 12 | | 38 | 15 | | 39 | 10 | | 40 | 9 | | 41 | 36 | | 42 | 21 | | 43 | 4 | | 44 | 12 | | 45 | 12 | | 46 | 26 | | 47 | 14 | | 48 | 8 | | 49 | 10 |
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| 61.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.40298507462686567 | | totalSentences | 67 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 55 | | matches | | 0 | "She kept her brown eyes" | | 1 | "Her boots struck the pavement" | | 2 | "He hugged a scuffed leather" | | 3 | "She rounded the brick corner," | | 4 | "He gripped the silver chain" | | 5 | "He leaped, kicked off the" | | 6 | "She vaulted the timber, wood" | | 7 | "He breathed in ragged gasps," | | 8 | "She stepped into the gloom," | | 9 | "Her sharp jaw was set," | | 10 | "He shook his head, clutching" | | 11 | "Her hand froze against the" | | 12 | "He produced a polished sliver" | | 13 | "He tossed it into a" | | 14 | "Her thumb brushed the worn" | | 15 | "She unsnapped the retention strap" |
| | ratio | 0.291 | |
| 14.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 55 | | matches | | 0 | "Neon signs from tattoo parlors" | | 1 | "Harlow Quinn surged through the" | | 2 | "She kept her brown eyes" | | 3 | "Water pooled in the collars" | | 4 | "Her boots struck the pavement" | | 5 | "The runner took a sharp" | | 6 | "Tomás Herrera did not possess" | | 7 | "He hugged a scuffed leather" | | 8 | "A sliver of copper-toned skin" | | 9 | "Quinn's voice sliced through the" | | 10 | "She rounded the brick corner," | | 11 | "Tomás glanced over his shoulder." | | 12 | "Water plastered dark curls across" | | 13 | "He gripped the silver chain" | | 14 | "He leaped, kicked off the" | | 15 | "Quinn did not break stride." | | 16 | "She vaulted the timber, wood" | | 17 | "The alley ended at a" | | 18 | "Tomás ripped one panel open." | | 19 | "A blast of dry, metallic" |
| | ratio | 0.891 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "He leaped, kicked off the metal ladder, and vaulted clean over a collapsed timber barricade that blocked an alcove beneath a disused railway bridge." | | 1 | "Spray-painted eviction notices and rusted chains had kept the entrance sealed for decades, yet the padlock now hung split, severed by a clean, deliberate sheari…" | | 2 | "Behind her lay the surface: clean procedure, radio backup, rain-slick streets, and a precinct house full of paperwork that would never explain why her partner h…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |