| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1240 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 71.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1240 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "loomed" | | 1 | "thundered" | | 2 | "jaw clenched" | | 3 | "scanned" | | 4 | "flickered" | | 5 | "pulsed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 168 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 168 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 182 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1240 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.87% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1103 | | uniqueNames | 22 | | maxNameDensity | 1.72 | | worstName | "Herrera" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Quinn | 17 | | Herrera | 19 | | Saint | 2 | | Christopher | 2 | | Berwick | 1 | | Street | 2 | | Underground | 1 | | Northern | 1 | | Town | 1 | | Camden | 3 | | Station | 1 | | Disused | 1 | | Access | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 | | Rain | 5 | | Water | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Station" | | 5 | "Morris" | | 6 | "Rain" | | 7 | "Water" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "Berwick" | | 3 | "Street" | | 4 | "Underground" | | 5 | "Town" | | 6 | "Camden" |
| | globalScore | 0.639 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | 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 | 1240 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 182 | | matches | (empty) | |
| 66.48% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 38.75 | | std | 14.83 | | cv | 0.383 | | sampleLengths | | 0 | 40 | | 1 | 57 | | 2 | 4 | | 3 | 45 | | 4 | 50 | | 5 | 36 | | 6 | 41 | | 7 | 37 | | 8 | 36 | | 9 | 30 | | 10 | 54 | | 11 | 41 | | 12 | 32 | | 13 | 41 | | 14 | 46 | | 15 | 32 | | 16 | 38 | | 17 | 43 | | 18 | 58 | | 19 | 40 | | 20 | 34 | | 21 | 28 | | 22 | 25 | | 23 | 80 | | 24 | 34 | | 25 | 41 | | 26 | 61 | | 27 | 48 | | 28 | 41 | | 29 | 13 | | 30 | 27 | | 31 | 7 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 168 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 212 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 182 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1108 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.008122743682310469 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0018050541516245488 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 182 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 182 | | mean | 6.81 | | std | 3.94 | | cv | 0.579 | | sampleLengths | | 0 | 8 | | 1 | 12 | | 2 | 6 | | 3 | 5 | | 4 | 9 | | 5 | 10 | | 6 | 9 | | 7 | 10 | | 8 | 3 | | 9 | 13 | | 10 | 7 | | 11 | 5 | | 12 | 4 | | 13 | 8 | | 14 | 7 | | 15 | 9 | | 16 | 8 | | 17 | 13 | | 18 | 5 | | 19 | 5 | | 20 | 13 | | 21 | 4 | | 22 | 6 | | 23 | 9 | | 24 | 6 | | 25 | 2 | | 26 | 2 | | 27 | 5 | | 28 | 5 | | 29 | 7 | | 30 | 4 | | 31 | 13 | | 32 | 10 | | 33 | 2 | | 34 | 10 | | 35 | 4 | | 36 | 4 | | 37 | 11 | | 38 | 5 | | 39 | 4 | | 40 | 4 | | 41 | 6 | | 42 | 3 | | 43 | 15 | | 44 | 6 | | 45 | 3 | | 46 | 6 | | 47 | 4 | | 48 | 3 | | 49 | 9 |
| |
| 56.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.36813186813186816 | | totalSentences | 182 | | uniqueOpeners | 67 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 149 | | matches | | 0 | "She checked the worn leather" | | 1 | "Her brown eyes fixed on" | | 2 | "Her salt-and-pepper crop stuck to" | | 3 | "Her sharp jaw set hard." | | 4 | "His left sleeve rode up" | | 5 | "Her stride ate the distance." | | 6 | "His warm brown eyes met" | | 7 | "Her boots slapped standing water." | | 8 | "She cut around its bonnet" | | 9 | "He hit the ground and" | | 10 | "Her voice cracked through the" | | 11 | "He ran with his head" | | 12 | "His arms pumped." | | 13 | "His medallion bounced against his" | | 14 | "Her lungs burned." | | 15 | "She tasted copper and exhaust." | | 16 | "She caught her balance on" | | 17 | "She fumbled her radio from" | | 18 | "She shoved the radio back." | | 19 | "Her fingers brushed his sleeve." |
| | ratio | 0.268 | |
| 27.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 129 | | totalSentences | 149 | | matches | | 0 | "Bass thumped through the door" | | 1 | "Harlow Quinn stood across the" | | 2 | "Water ran off the canvas" | | 3 | "She checked the worn leather" | | 4 | "Her brown eyes fixed on" | | 5 | "Her salt-and-pepper crop stuck to" | | 6 | "Her sharp jaw set hard." | | 7 | "The side door opened." | | 8 | "Tomás Herrera stepped out with" | | 9 | "Olive skin shone wet under" | | 10 | "A Saint Christopher medallion flashed" | | 11 | "His left sleeve rode up" | | 12 | "Quinn pushed off the wall." | | 13 | "Her stride ate the distance." | | 14 | "Military precision lived in her" | | 15 | "Herrera froze for half a" | | 16 | "His warm brown eyes met" | | 17 | "Her boots slapped standing water." | | 18 | "Spray shot up her trousers." | | 19 | "The streetlights smeared into long" |
| | ratio | 0.866 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 149 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |