| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 855 | | 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) | |
| 23.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 855 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "flickered" | | 1 | "facade" | | 2 | "depths" | | 3 | "etched" | | 4 | "intricate" | | 5 | "standard" | | 6 | "echoes" | | 7 | "reverberated" | | 8 | "silence" | | 9 | "gloom" | | 10 | "scanning" | | 11 | "weight" |
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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 | 52 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 65 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 855 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 739 | | uniqueNames | 12 | | maxNameDensity | 0.41 | | worstName | "London" | | maxWindowNameDensity | 1 | | worstWindowName | "London" | | discoveredNames | | Greek | 1 | | Street | 1 | | London | 3 | | Metropolitan | 1 | | Police | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Police" | | 1 | "Morris" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "London" | | 3 | "Metropolitan" | | 4 | "Soho" | | 5 | "Raven" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | 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 | 855 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 27.58 | | std | 21.45 | | cv | 0.778 | | sampleLengths | | 0 | 21 | | 1 | 3 | | 2 | 56 | | 3 | 56 | | 4 | 38 | | 5 | 12 | | 6 | 62 | | 7 | 3 | | 8 | 26 | | 9 | 10 | | 10 | 4 | | 11 | 5 | | 12 | 71 | | 13 | 27 | | 14 | 48 | | 15 | 5 | | 16 | 61 | | 17 | 18 | | 18 | 16 | | 19 | 15 | | 20 | 17 | | 21 | 42 | | 22 | 7 | | 23 | 11 | | 24 | 2 | | 25 | 50 | | 26 | 63 | | 27 | 35 | | 28 | 3 | | 29 | 33 | | 30 | 35 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 52 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 65 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 742 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.0215633423180593 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006738544474393531 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 65 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 65 | | mean | 13.15 | | std | 6.63 | | cv | 0.504 | | sampleLengths | | 0 | 6 | | 1 | 15 | | 2 | 3 | | 3 | 4 | | 4 | 20 | | 5 | 10 | | 6 | 11 | | 7 | 11 | | 8 | 15 | | 9 | 20 | | 10 | 21 | | 11 | 4 | | 12 | 18 | | 13 | 16 | | 14 | 5 | | 15 | 7 | | 16 | 13 | | 17 | 19 | | 18 | 17 | | 19 | 13 | | 20 | 3 | | 21 | 18 | | 22 | 8 | | 23 | 10 | | 24 | 4 | | 25 | 5 | | 26 | 14 | | 27 | 17 | | 28 | 24 | | 29 | 16 | | 30 | 9 | | 31 | 18 | | 32 | 5 | | 33 | 12 | | 34 | 31 | | 35 | 5 | | 36 | 3 | | 37 | 25 | | 38 | 17 | | 39 | 16 | | 40 | 18 | | 41 | 16 | | 42 | 15 | | 43 | 17 | | 44 | 7 | | 45 | 17 | | 46 | 9 | | 47 | 9 | | 48 | 7 | | 49 | 11 |
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| 77.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.49230769230769234 | | totalSentences | 65 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 89.23% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 52 | | matches | | 0 | "He vaulted over a rusted" | | 1 | "My lungs burned with every" | | 2 | "I closed the distance." | | 3 | "I hit the wood shoulder-first." | | 4 | "I stepped into the dim" | | 5 | "He didn't even look up" | | 6 | "I bypassed the bartender entirely," | | 7 | "I shoved the revolving bookcase" | | 8 | "I stared at the bone." | | 9 | "My left hand tightened around" | | 10 | "I spun around." | | 11 | "I looked down the dark" | | 12 | "My fingers tightened on the" | | 13 | "I stepped past the former" | | 14 | "My boots crunched on loose" | | 15 | "I tracked the wet footprints" | | 16 | "I raised my service weapon," |
| | ratio | 0.327 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 52 | | matches | | 0 | "Boot leather slammed against wet" | | 1 | "A dark trench coat flared" | | 2 | "The target didn't slow." | | 3 | "He vaulted over a rusted" | | 4 | "My lungs burned with every" | | 5 | "Neon signs hummed overhead, bleeding" | | 6 | "A distinctive green neon sign" | | 7 | "The runner changed direction sharply," | | 8 | "I closed the distance." | | 9 | "The metal handrail was cold" | | 10 | "A heavy oak door swung" | | 11 | "I hit the wood shoulder-first." | | 12 | "The lock splintered with a" | | 13 | "I stepped into the dim" | | 14 | "The walls were covered with" | | 15 | "A bartender with a scarred" | | 16 | "He didn't even look up" | | 17 | "the man said" | | 18 | "A heavy bookshelf groaned against" | | 19 | "I bypassed the bartender entirely," |
| | ratio | 0.846 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 6 | | matches | | 0 | "He vaulted over a rusted iron barrier, his trainers skidding on the greasy London drizzle before biting into the asphalt." | | 1 | "The walls were covered with faded maps and black-and-white photographs of a London that died decades ago." | | 2 | "Three years had passed since I lost DS Morris to a case that defied every standard department protocol, a case involving hidden tunnels and things that bled dar…" | | 3 | "The tunnel opened up into a cavernous, subterranean expanse that defied London's geography." | | 4 | "Gas lamps flickered along rusted iron columns, casting long, writhing shadows across hundreds of hooded figures trading in items that belonged in medieval grimo…" | | 5 | "The runner was weaving through the dense crowd ahead, his silhouette vanishing behind a massive canvas tent." |
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| 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 | 14 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |