| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 46 | | tagDensity | 0.37 | | leniency | 0.739 | | rawRatio | 0.059 | | effectiveRatio | 0.043 | |
| 86.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1073 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "carefully" | | 2 | "suddenly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 81.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1073 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "echoing" | | 2 | "scanning" | | 3 | "footsteps" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1067 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 699 | | uniqueNames | 19 | | maxNameDensity | 2.15 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Camden | 1 | | Matthews | 5 | | Dr | 3 | | Patel | 3 | | Italian | 1 | | Museum | 1 | | Research | 1 | | Department | 1 | | Archives | 1 | | Division | 1 | | Morris | 2 | | Play | 1 | | Eva | 12 | | London | 1 | | Met | 1 | | Bermondsey | 1 | | Seven | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Matthews" | | 3 | "Dr" | | 4 | "Patel" | | 5 | "Museum" | | 6 | "Department" | | 7 | "Morris" | | 8 | "Eva" | | 9 | "Met" | | 10 | "Seven" |
| | places | | | globalScore | 0.427 | | windowScore | 0.5 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like an expensive suit beneath str" | | 1 | "quite match any weapon the Met could identify" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.937 | | wordCount | 1067 | | matches | | 0 | "not north but directly at the corpse" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 110 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 24.25 | | std | 14.09 | | cv | 0.581 | | sampleLengths | | 0 | 58 | | 1 | 28 | | 2 | 46 | | 3 | 41 | | 4 | 25 | | 5 | 28 | | 6 | 22 | | 7 | 21 | | 8 | 39 | | 9 | 7 | | 10 | 20 | | 11 | 20 | | 12 | 7 | | 13 | 33 | | 14 | 19 | | 15 | 27 | | 16 | 11 | | 17 | 35 | | 18 | 17 | | 19 | 4 | | 20 | 21 | | 21 | 30 | | 22 | 29 | | 23 | 1 | | 24 | 18 | | 25 | 61 | | 26 | 16 | | 27 | 29 | | 28 | 16 | | 29 | 14 | | 30 | 29 | | 31 | 21 | | 32 | 9 | | 33 | 42 | | 34 | 14 | | 35 | 27 | | 36 | 58 | | 37 | 16 | | 38 | 30 | | 39 | 5 | | 40 | 33 | | 41 | 20 | | 42 | 13 | | 43 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 123 | | matches | | |
| 64.94% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 110 | | ratio | 0.027 | | matches | | 0 | "The air tasted of mould and something else—copper pennies and burnt matches." | | 1 | "She checked his shoes—Italian leather, barely scuffed." | | 2 | "There—scratches in the concrete that looked random until you saw the pattern." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 548 | | adjectiveStacks | 1 | | stackExamples | | 0 | "illuminating graffiti-covered tiles" |
| | adverbCount | 13 | | adverbRatio | 0.023722627737226276 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005474452554744526 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 9.7 | | std | 5.11 | | cv | 0.527 | | sampleLengths | | 0 | 17 | | 1 | 20 | | 2 | 21 | | 3 | 15 | | 4 | 13 | | 5 | 12 | | 6 | 12 | | 7 | 22 | | 8 | 9 | | 9 | 15 | | 10 | 2 | | 11 | 8 | | 12 | 5 | | 13 | 1 | | 14 | 1 | | 15 | 9 | | 16 | 5 | | 17 | 11 | | 18 | 10 | | 19 | 3 | | 20 | 8 | | 21 | 7 | | 22 | 13 | | 23 | 9 | | 24 | 10 | | 25 | 11 | | 26 | 6 | | 27 | 14 | | 28 | 9 | | 29 | 10 | | 30 | 7 | | 31 | 12 | | 32 | 8 | | 33 | 6 | | 34 | 14 | | 35 | 7 | | 36 | 8 | | 37 | 17 | | 38 | 8 | | 39 | 12 | | 40 | 7 | | 41 | 14 | | 42 | 13 | | 43 | 5 | | 44 | 6 | | 45 | 16 | | 46 | 19 | | 47 | 4 | | 48 | 5 | | 49 | 8 |
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| 92.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5727272727272728 | | totalSentences | 110 | | uniqueOpeners | 63 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Then they'd found his body," |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 74 | | matches | | 0 | "Her torch beam cut through" | | 1 | "She checked his shoes—Italian leather," | | 2 | "She stood, scanning the platform" | | 3 | "She turned the bag, watching" | | 4 | "She'd thought he was losing" | | 5 | "She kept her tone flat," | | 6 | "She kicked aside an empty" | | 7 | "She turned back to Eva" | | 8 | "She checked her watch, an" |
| | ratio | 0.122 | |
| 81.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 74 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The abandoned Camden tube station" | | 2 | "Her torch beam cut through" | | 3 | "PC Matthews shifted his weight," | | 4 | "Quinn descended the rusted stairs," | | 5 | "The air tasted of mould" | | 6 | "The victim lay twisted, arms" | | 7 | "Quinn crouched, studying the expression" | | 8 | "Terror, yes, but something more." | | 9 | "The coroner, Dr" | | 10 | "Patel, gestured with his pen." | | 11 | "Quinn pulled on latex gloves" | | 12 | "She checked his shoes—Italian leather," | | 13 | "She stood, scanning the platform" | | 14 | "Matthews nodded toward a scatter" | | 15 | "Quinn picked up the evidence" | | 16 | "The needle pointed not north" | | 17 | "She turned the bag, watching" | | 18 | "A scuff of footsteps interrupted" | | 19 | "A figure emerged from the" |
| | ratio | 0.757 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Her torch beam cut through the darkness, illuminating graffiti-covered tiles that hadn't seen proper light since the line closed in 1954." |
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| 36.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 3 | | matches | | 0 | "PC Matthews shifted, boots scraping against broken glass" | | 1 | "Quinn kept, professional" | | 2 | "She kept, skeptical" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.022 | | leniency | 0.043 | | rawRatio | 0 | | effectiveRatio | 0 | |