| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 825 | | 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) | |
| 93.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 825 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 825 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.73% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 816 | | uniqueNames | 19 | | maxNameDensity | 1.23 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Detective | 1 | | Harlow | 1 | | Quinn | 10 | | Frith | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Camden | 2 | | Veil | 1 | | Market | 1 | | Tube | 1 | | Morris | 2 | | Metropolitan | 1 | | Police | 1 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Morris" | | 8 | "Police" | | 9 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Detective" | | 2 | "Frith" | | 3 | "Street" | | 4 | "Camden" | | 5 | "Market" |
| | globalScore | 0.887 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 825 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 29.46 | | std | 22.26 | | cv | 0.755 | | sampleLengths | | 0 | 56 | | 1 | 8 | | 2 | 47 | | 3 | 2 | | 4 | 66 | | 5 | 50 | | 6 | 2 | | 7 | 31 | | 8 | 2 | | 9 | 20 | | 10 | 66 | | 11 | 21 | | 12 | 6 | | 13 | 63 | | 14 | 11 | | 15 | 3 | | 16 | 46 | | 17 | 51 | | 18 | 24 | | 19 | 1 | | 20 | 49 | | 21 | 20 | | 22 | 35 | | 23 | 48 | | 24 | 9 | | 25 | 59 | | 26 | 13 | | 27 | 16 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 94 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 822 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.012165450121654502 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0036496350364963502 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 8.78 | | std | 6.68 | | cv | 0.761 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 8 | | 3 | 12 | | 4 | 8 | | 5 | 29 | | 6 | 3 | | 7 | 11 | | 8 | 4 | | 9 | 2 | | 10 | 5 | | 11 | 36 | | 12 | 7 | | 13 | 10 | | 14 | 8 | | 15 | 10 | | 16 | 9 | | 17 | 5 | | 18 | 15 | | 19 | 11 | | 20 | 2 | | 21 | 13 | | 22 | 2 | | 23 | 3 | | 24 | 11 | | 25 | 2 | | 26 | 2 | | 27 | 5 | | 28 | 15 | | 29 | 2 | | 30 | 8 | | 31 | 5 | | 32 | 5 | | 33 | 3 | | 34 | 11 | | 35 | 4 | | 36 | 28 | | 37 | 5 | | 38 | 3 | | 39 | 4 | | 40 | 6 | | 41 | 3 | | 42 | 6 | | 43 | 17 | | 44 | 4 | | 45 | 13 | | 46 | 17 | | 47 | 1 | | 48 | 7 | | 49 | 4 |
| |
| 44.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.35106382978723405 | | totalSentences | 94 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 92.94% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 85 | | matches | | 0 | "She crossed the floor in" | | 1 | "He did not break stride." | | 2 | "He vaulted a skip, landed" | | 3 | "Her boots struck puddles and" | | 4 | "She kept Herrera in sight" | | 5 | "He knew the streets." | | 6 | "He cut through a service" | | 7 | "She kept moving." | | 8 | "He was not in the" | | 9 | "She caught a glimpse of" | | 10 | "She had read the reports." | | 11 | "She had not told anyone" | | 12 | "It was the same smell" | | 13 | "He did not look at" | | 14 | "He looked at her hands." | | 15 | "She had come for a" | | 16 | "She had no token." | | 17 | "She had no backup." | | 18 | "She took a step forward." | | 19 | "Her left wrist ached where" |
| | ratio | 0.318 | |
| 30.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 85 | | matches | | 0 | "The green neon sign above" | | 1 | "Water ran off the brim" | | 2 | "The worn leather watch on" | | 3 | "The suspect had not gone" | | 4 | "Quinn watched the figure slip" | | 5 | "The shelf gave." | | 6 | "A narrow gap opened, smelling" | | 7 | "The figure ducked through." | | 8 | "Military precision governed her steps." | | 9 | "She crossed the floor in" | | 10 | "The air turned colder with" | | 11 | "The stairs ended at a" | | 12 | "A man in a dark" | | 13 | "Olive skin darkened by wet." | | 14 | "A Saint Christopher medallion caught" | | 15 | "A scar ran along his" | | 16 | "Quinn knew the name from" | | 17 | "He did not break stride." | | 18 | "He vaulted a skip, landed" | | 19 | "Her boots struck puddles and" |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "Off-the-books medical care for people who did not exist on paper." | | 1 | "Enchanted goods, banned alchemical substances, information sold by people who did not want police." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |