| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "a voice rumbled deep [deep]" |
| | dialogueSentences | 30 | | tagDensity | 0.367 | | leniency | 0.733 | | rawRatio | 0.091 | | effectiveRatio | 0.067 | |
| 95.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1079 | | 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) | |
| 58.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1079 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "throbbed" | | 1 | "gleaming" | | 2 | "echoing" | | 3 | "silence" | | 4 | "rhythmic" | | 5 | "echoed" | | 6 | "weight" | | 7 | "whisper" | | 8 | "constructed" |
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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 | 1 | | narrationSentences | 85 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 6 | | totalWords | 1080 | | ratio | 0.006 | | matches | | 0 | "London Underground - Closed." | | 1 | "Drip. Drop." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 898 | | uniqueNames | 13 | | maxNameDensity | 1.67 | | worstName | "Herrera" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 15 | | Harlow | 15 | | Quinn | 1 | | Saint | 1 | | Christopher | 2 | | Italian | 1 | | Camden | 1 | | Underground | 1 | | Tube | 1 | | Morris | 1 | | Glock | 2 | | London | 2 | | St | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Morris" | | 5 | "Glock" |
| | places | | | globalScore | 0.665 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 1080 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 17.14 | | std | 13.59 | | cv | 0.793 | | sampleLengths | | 0 | 22 | | 1 | 24 | | 2 | 9 | | 3 | 24 | | 4 | 71 | | 5 | 19 | | 6 | 32 | | 7 | 8 | | 8 | 32 | | 9 | 7 | | 10 | 50 | | 11 | 13 | | 12 | 53 | | 13 | 36 | | 14 | 20 | | 15 | 12 | | 16 | 21 | | 17 | 22 | | 18 | 11 | | 19 | 15 | | 20 | 9 | | 21 | 9 | | 22 | 25 | | 23 | 10 | | 24 | 5 | | 25 | 7 | | 26 | 17 | | 27 | 11 | | 28 | 9 | | 29 | 17 | | 30 | 16 | | 31 | 7 | | 32 | 37 | | 33 | 11 | | 34 | 3 | | 35 | 11 | | 36 | 20 | | 37 | 4 | | 38 | 10 | | 39 | 7 | | 40 | 7 | | 41 | 11 | | 42 | 5 | | 43 | 9 | | 44 | 58 | | 45 | 4 | | 46 | 14 | | 47 | 17 | | 48 | 4 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 151 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 104 | | ratio | 0.01 | | matches | | 0 | "A yellow sign, faded and spray-painted with jagged graffiti, read: *London Underground - Closed.*" |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 910 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, iron-reinforced oak" |
| | adverbCount | 14 | | adverbRatio | 0.015384615384615385 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005494505494505495 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.38 | | std | 5.77 | | cv | 0.556 | | sampleLengths | | 0 | 22 | | 1 | 8 | | 2 | 16 | | 3 | 9 | | 4 | 4 | | 5 | 20 | | 6 | 4 | | 7 | 8 | | 8 | 22 | | 9 | 11 | | 10 | 15 | | 11 | 7 | | 12 | 4 | | 13 | 19 | | 14 | 17 | | 15 | 15 | | 16 | 8 | | 17 | 18 | | 18 | 14 | | 19 | 7 | | 20 | 6 | | 21 | 22 | | 22 | 10 | | 23 | 3 | | 24 | 9 | | 25 | 13 | | 26 | 6 | | 27 | 20 | | 28 | 10 | | 29 | 1 | | 30 | 16 | | 31 | 10 | | 32 | 9 | | 33 | 12 | | 34 | 3 | | 35 | 1 | | 36 | 1 | | 37 | 20 | | 38 | 12 | | 39 | 21 | | 40 | 6 | | 41 | 16 | | 42 | 11 | | 43 | 6 | | 44 | 9 | | 45 | 9 | | 46 | 9 | | 47 | 3 | | 48 | 22 | | 49 | 10 |
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| 66.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.41346153846153844 | | totalSentences | 104 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 80 | | matches | | 0 | "She vaulted the obstacle, landing" | | 1 | "Her shout ricocheted off the" | | 2 | "He rounded a sharp corner," | | 3 | "Her forty-one-year-old knees throbbed with" | | 4 | "She kept her gaze locked" | | 5 | "He was a paramedic, not" | | 6 | "They darted past a shuttered" | | 7 | "He scrambled toward a concrete" | | 8 | "She had seen the claw" | | 9 | "She drew her Glock, her" | | 10 | "Her boots made no sound" | | 11 | "She pressed her back against" | | 12 | "He held up a small," | | 13 | "He slowly turned his head" | | 14 | "She grabbed Herrera's arm." | | 15 | "Her grip was iron, her" | | 16 | "She kept her weapon pressed" | | 17 | "Her fingers lost their feeling," | | 18 | "She spun, her free elbow" |
| | ratio | 0.238 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 80 | | matches | | 0 | "Tomás Herrera’s trainer clipped the" | | 1 | "Detective Harlow Quinn did not" | | 2 | "She vaulted the obstacle, landing" | | 3 | "Her shout ricocheted off the" | | 4 | "Herrera did not freeze." | | 5 | "He rounded a sharp corner," | | 6 | "Harlow checked her watch." | | 7 | "The worn leather strap chafed" | | 8 | "Her forty-one-year-old knees throbbed with" | | 9 | "She kept her gaze locked" | | 10 | "Herrera was fast, but he" | | 11 | "He was a paramedic, not" | | 12 | "They darted past a shuttered" | | 13 | "Herrera leaped over a low" | | 14 | "He scrambled toward a concrete" | | 15 | "Harlow halted at the lip" | | 16 | "The rusted iron gate at" | | 17 | "A yellow sign, faded and" | | 18 | "The higher-ups still called Morris’s" | | 19 | "Harlow knew better." |
| | ratio | 0.85 | |
| 62.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 80 | | matches | | | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 1 | | matches | | 0 | "He scrambled toward a concrete stairwell that descended into the earth, disappearing beneath the surface." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 3 | | matches | | 0 | "the gatekeeper whispered, his tongue clicking against black teeth" | | 1 | "Herrera said, his voice dropping to a harsh whisper" | | 2 | "Herrera whispered, his face pale under the amber torches" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 6 | | fancyTags | | 0 | "Her shout (shout)" | | 1 | "the gatekeeper sneered (sneer)" | | 2 | "the gatekeeper whispered (whisper)" | | 3 | "Herrera whispered (whisper)" | | 4 | "the butcher hissed (hiss)" | | 5 | "Herrera yelled (yell)" |
| | dialogueSentences | 30 | | tagDensity | 0.267 | | leniency | 0.533 | | rawRatio | 0.75 | | effectiveRatio | 0.4 | |