| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 975 | | 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) | |
| 69.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 975 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "quickened" | | 1 | "measured" | | 2 | "facade" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "scanned" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 89 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 970 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 966 | | uniqueNames | 17 | | maxNameDensity | 0.93 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Herrera | 9 | | Soho | 2 | | Friday | 2 | | Wardour | 1 | | Street | 1 | | Morris | 3 | | Camden | 1 | | London | 2 | | Jubilee | 1 | | Underground | 1 | | Quinn | 8 | | Glock | 1 | | Saint | 1 | | Christopher | 1 | | Eighteen | 3 |
| | persons | | 0 | "Raven" | | 1 | "Herrera" | | 2 | "Morris" | | 3 | "Quinn" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Friday" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "Camden" | | 5 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | 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 | 970 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 27.71 | | std | 23.86 | | cv | 0.861 | | sampleLengths | | 0 | 12 | | 1 | 65 | | 2 | 2 | | 3 | 7 | | 4 | 73 | | 5 | 11 | | 6 | 43 | | 7 | 5 | | 8 | 41 | | 9 | 5 | | 10 | 2 | | 11 | 67 | | 12 | 9 | | 13 | 4 | | 14 | 66 | | 15 | 28 | | 16 | 50 | | 17 | 8 | | 18 | 35 | | 19 | 48 | | 20 | 4 | | 21 | 37 | | 22 | 8 | | 23 | 6 | | 24 | 77 | | 25 | 53 | | 26 | 15 | | 27 | 6 | | 28 | 58 | | 29 | 36 | | 30 | 22 | | 31 | 3 | | 32 | 10 | | 33 | 40 | | 34 | 14 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 88 | | matches | | 0 | "been taught" | | 1 | "been painted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 154 | | matches | | 0 | "was running" | | 1 | "was going" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 89 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 971 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.015447991761071062 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003089598352214212 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 89 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 89 | | mean | 10.9 | | std | 9.36 | | cv | 0.858 | | sampleLengths | | 0 | 12 | | 1 | 52 | | 2 | 1 | | 3 | 2 | | 4 | 10 | | 5 | 2 | | 6 | 7 | | 7 | 17 | | 8 | 11 | | 9 | 20 | | 10 | 25 | | 11 | 6 | | 12 | 2 | | 13 | 3 | | 14 | 16 | | 15 | 3 | | 16 | 2 | | 17 | 15 | | 18 | 4 | | 19 | 3 | | 20 | 5 | | 21 | 4 | | 22 | 18 | | 23 | 19 | | 24 | 4 | | 25 | 1 | | 26 | 2 | | 27 | 26 | | 28 | 5 | | 29 | 16 | | 30 | 20 | | 31 | 4 | | 32 | 2 | | 33 | 3 | | 34 | 4 | | 35 | 21 | | 36 | 14 | | 37 | 8 | | 38 | 23 | | 39 | 6 | | 40 | 10 | | 41 | 4 | | 42 | 4 | | 43 | 4 | | 44 | 25 | | 45 | 9 | | 46 | 16 | | 47 | 8 | | 48 | 17 | | 49 | 10 |
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| 61.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.42696629213483145 | | totalSentences | 89 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 93.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 76 | | matches | | 0 | "She'd been watching the green" | | 1 | "She moved before he reached" | | 2 | "He turned north on Wardour" | | 3 | "She'd had three separate tipoffs" | | 4 | "She didn't laugh anymore." | | 5 | "She quickened her pace." | | 6 | "She vaulted them without slowing." | | 7 | "He was fast, younger, but" | | 8 | "She paced herself, kept her" | | 9 | "She didn't shout" | | 10 | "He did not stop." | | 11 | "He flew across the junction" | | 12 | "He knew where he was" | | 13 | "He had a destination." | | 14 | "She stopped at the top" | | 15 | "She thought of the Jubilee" | | 16 | "She thought of Morris." | | 17 | "Her jaw tightened." | | 18 | "Her hand moved to the" | | 19 | "She descended anyway, one hand" |
| | ratio | 0.316 | |
| 78.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 76 | | matches | | 0 | "Quinn spotted him the moment" | | 1 | "She'd been watching the green" | | 2 | "A medallion catching the bar's" | | 3 | "She moved before he reached" | | 4 | "Herrera moved fast for someone" | | 5 | "Quinn matched his pace thirty" | | 6 | "He turned north on Wardour" | | 7 | "She'd had three separate tipoffs" | | 8 | "The kind of case that," | | 9 | "She didn't laugh anymore." | | 10 | "Herrera ducked left without warning." | | 11 | "She quickened her pace." | | 12 | "The alley he'd chosen was" | | 13 | "The alley spat them both" | | 14 | "She vaulted them without slowing." | | 15 | "He was fast, younger, but" | | 16 | "She paced herself, kept her" | | 17 | "She didn't shout" | | 18 | "He did not stop." | | 19 | "He flew across the junction" |
| | ratio | 0.763 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "By the time she reached" |
| | ratio | 0.013 | |
| 17.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 7 | | matches | | 0 | "She'd been watching the green neon sign from across the street for forty minutes, her back pressed against the cold brick of a shuttered newsagent, when the doo…" | | 1 | "Head down, shoulders tucked, weaving through the Friday night crowd with a paramedic's ease, stepping around bodies without breaking stride." | | 2 | "The rain was heavier here, falling in sheets that flattened her salt-and-pepper hair against her skull and filled her shoes within a block." | | 3 | "The smell hit her first: burnt herbs, copper, something beneath that she had no name for, a low note that sat behind the sinuses like pressure before a storm." | | 4 | "A woman with chalk-white skin bartered in a language that wasn't any language Quinn's ear could classify." | | 5 | "And then the other thing, the smaller, quieter thing that had been growing in her chest since Morris died on a case that the official report still described as …" | | 6 | "He turned and walked calmly through a door at the far end that had no business existing in a walled-up tube station, and the door swung shut behind him with the…" |
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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 | |