| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "His eyes glowed faintly [faintly]" |
| | dialogueSentences | 30 | | tagDensity | 0.367 | | leniency | 0.733 | | rawRatio | 0.091 | | effectiveRatio | 0.067 | |
| 96.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1398 | | 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) | |
| 64.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1398 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "carried the weight" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | |
| | highlights | | 0 | "echoed" | | 1 | "flickered" | | 2 | "silence" | | 3 | "carried the weight" | | 4 | "grave" | | 5 | "standard" | | 6 | "pounding" | | 7 | "pulse" |
| |
| 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 | 147 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 147 | | filterMatches | | | hedgeMatches | | |
| 99.29% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 166 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0.006 | | matches | | 0 | "Fingers gripping那里的痕迹." |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1398 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1152 | | uniqueNames | 18 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Brewer | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 10 | | Shaftesbury | 1 | | Avenue | 1 | | London | 4 | | Soho | 2 | | European | 1 | | Korova | 2 | | Blood | 1 | | Underground | 1 | | Transport | 1 | | Saint | 2 | | Christopher | 2 | | Thames | 1 | | Silence | 1 | | Morris | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Blood" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "London" | | 5 | "Soho" | | 6 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 89.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed carved from pale wood" | | 1 | "something like concern" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1398 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 166 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 21.51 | | std | 16.15 | | cv | 0.751 | | sampleLengths | | 0 | 50 | | 1 | 13 | | 2 | 31 | | 3 | 2 | | 4 | 31 | | 5 | 18 | | 6 | 6 | | 7 | 50 | | 8 | 16 | | 9 | 22 | | 10 | 2 | | 11 | 48 | | 12 | 12 | | 13 | 25 | | 14 | 5 | | 15 | 42 | | 16 | 17 | | 17 | 13 | | 18 | 48 | | 19 | 3 | | 20 | 29 | | 21 | 2 | | 22 | 35 | | 23 | 48 | | 24 | 5 | | 25 | 27 | | 26 | 25 | | 27 | 13 | | 28 | 5 | | 29 | 41 | | 30 | 48 | | 31 | 10 | | 32 | 6 | | 33 | 38 | | 34 | 8 | | 35 | 60 | | 36 | 3 | | 37 | 51 | | 38 | 13 | | 39 | 32 | | 40 | 20 | | 41 | 1 | | 42 | 44 | | 43 | 26 | | 44 | 4 | | 45 | 37 | | 46 | 6 | | 47 | 36 | | 48 | 17 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 147 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 210 | | matches | | 0 | "were looking" | | 1 | "was coming" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 166 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1158 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.025906735751295335 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0034542314335060447 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 166 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 166 | | mean | 8.42 | | std | 6.1 | | cv | 0.725 | | sampleLengths | | 0 | 20 | | 1 | 30 | | 2 | 4 | | 3 | 9 | | 4 | 7 | | 5 | 3 | | 6 | 21 | | 7 | 2 | | 8 | 9 | | 9 | 10 | | 10 | 4 | | 11 | 8 | | 12 | 9 | | 13 | 6 | | 14 | 2 | | 15 | 1 | | 16 | 6 | | 17 | 3 | | 18 | 11 | | 19 | 1 | | 20 | 13 | | 21 | 3 | | 22 | 4 | | 23 | 15 | | 24 | 5 | | 25 | 11 | | 26 | 3 | | 27 | 10 | | 28 | 9 | | 29 | 2 | | 30 | 22 | | 31 | 8 | | 32 | 18 | | 33 | 8 | | 34 | 4 | | 35 | 15 | | 36 | 10 | | 37 | 4 | | 38 | 1 | | 39 | 18 | | 40 | 11 | | 41 | 1 | | 42 | 1 | | 43 | 6 | | 44 | 5 | | 45 | 4 | | 46 | 13 | | 47 | 13 | | 48 | 14 | | 49 | 15 |
| |
| 90.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5662650602409639 | | totalSentences | 166 | | uniqueOpeners | 94 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 119 | | matches | | 0 | "Just stone steps slick with" | | 1 | "Just a waterlogged man and" | | 2 | "Then she took the stairs" | | 3 | "Then a figure emerged from" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 119 | | matches | | 0 | "She rounded the corner and" | | 1 | "She looked up." | | 2 | "she called out" | | 3 | "He reached into his jacket," | | 4 | "He released the rung, dropped" | | 5 | "She stood there for three" | | 6 | "She kept her hand on" | | 7 | "Her backup had radioed in" | | 8 | "Her torch beam caught the" | | 9 | "She recognised a few from" | | 10 | "She didn't carry keepsakes." | | 11 | "She could turn back." | | 12 | "She tucked it into her" | | 13 | "Her torch flickered once, twice," | | 14 | "She killed the torch and" | | 15 | "They weren't human." | | 16 | "They knew she didn't belong." | | 17 | "She kept her voice level" | | 18 | "He wore a waistcoat that" | | 19 | "His voice carried the weight" |
| | ratio | 0.185 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 119 | | matches | | 0 | "The suspect had vanished around" | | 1 | "The click of a gate" | | 2 | "Metal on metal." | | 3 | "She rounded the corner and" | | 4 | "The words echoed off rain-slicked" | | 5 | "Quinn pulled her torch, swept" | | 6 | "She looked up." | | 7 | "The suspect hung from the" | | 8 | "she called out" | | 9 | "Blood on his teeth from" | | 10 | "He reached into his jacket," | | 11 | "A finger bone, filed smooth" | | 12 | "Quinn kept her torch steady" | | 13 | "He released the rung, dropped" | | 14 | "The fire escape shuddered." | | 15 | "She stood there for three" | | 16 | "The worn leather of her" | | 17 | "Morris had worn the same" | | 18 | "Morris was dead now." | | 19 | "Quinn clipped her torch back" |
| | ratio | 0.639 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 4 | | matches | | 0 | "Just a waterlogged man and a silver disc that should have protected him from drowning." | | 1 | "Said every good detective needed something that reminded them time was finite." | | 2 | "Market stalls stretched in rows like a medieval bazaar, lit by paper lanterns that cast everything in sickly yellow." | | 3 | "He wore a waistcoat that had been fashionable a century ago, his hair oiled back from a face that seemed carved from pale wood." |
| |
| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "The words echoed, useless" | | 1 | "toothed man had, his numerous teeth chattering" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "toothed man had (have)" |
| | dialogueSentences | 30 | | tagDensity | 0.033 | | leniency | 0.067 | | rawRatio | 1 | | effectiveRatio | 0.067 | |