| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 67 | | tagDensity | 0.015 | | leniency | 0.03 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1940 | | 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) | |
| 81.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1940 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "streaming" | | 1 | "efficient" | | 2 | "familiar" | | 3 | "flicked" | | 4 | "silence" | | 5 | "pulse" | | 6 | "pulsed" |
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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 | 1 | | narrationSentences | 184 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 184 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 250 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1939 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1578 | | uniqueNames | 16 | | maxNameDensity | 1.71 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Quinn | 27 | | Wapping | 1 | | Herrera | 1 | | Tomás | 20 | | Brewer | 1 | | Street | 1 | | Lewisham | 1 | | Morris | 2 | | Christopher | 1 | | Tube | 1 | | London | 2 | | Veil | 1 | | Market | 1 | | Rain | 4 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Tomás" | | 5 | "Morris" | | 6 | "Christopher" | | 7 | "Market" | | 8 | "Rain" |
| | places | | 0 | "Wapping" | | 1 | "Brewer" | | 2 | "Street" | | 3 | "Lewisham" | | 4 | "London" |
| | globalScore | 0.644 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 106 | | glossingSentenceCount | 1 | | matches | | 0 | "as if approaching a frightened animal" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1939 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 250 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 147 | | mean | 13.19 | | std | 16.52 | | cv | 1.252 | | sampleLengths | | 0 | 22 | | 1 | 51 | | 2 | 9 | | 3 | 4 | | 4 | 71 | | 5 | 2 | | 6 | 41 | | 7 | 4 | | 8 | 46 | | 9 | 6 | | 10 | 39 | | 11 | 3 | | 12 | 3 | | 13 | 2 | | 14 | 13 | | 15 | 6 | | 16 | 42 | | 17 | 2 | | 18 | 9 | | 19 | 5 | | 20 | 9 | | 21 | 3 | | 22 | 48 | | 23 | 56 | | 24 | 1 | | 25 | 13 | | 26 | 4 | | 27 | 4 | | 28 | 52 | | 29 | 9 | | 30 | 12 | | 31 | 34 | | 32 | 15 | | 33 | 1 | | 34 | 1 | | 35 | 7 | | 36 | 3 | | 37 | 5 | | 38 | 1 | | 39 | 19 | | 40 | 7 | | 41 | 1 | | 42 | 7 | | 43 | 3 | | 44 | 2 | | 45 | 22 | | 46 | 5 | | 47 | 4 | | 48 | 3 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 184 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 269 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 250 | | ratio | 0.004 | | matches | | 0 | "Tomás had a medic’s body—compact, efficient, no wasted motion." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1586 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Safe, wet, ordinary London" |
| | adverbCount | 35 | | adverbRatio | 0.022068095838587643 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0031525851197982345 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 250 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 250 | | mean | 7.76 | | std | 6.71 | | cv | 0.865 | | sampleLengths | | 0 | 22 | | 1 | 27 | | 2 | 14 | | 3 | 3 | | 4 | 7 | | 5 | 8 | | 6 | 1 | | 7 | 4 | | 8 | 11 | | 9 | 1 | | 10 | 8 | | 11 | 21 | | 12 | 20 | | 13 | 2 | | 14 | 3 | | 15 | 5 | | 16 | 2 | | 17 | 2 | | 18 | 2 | | 19 | 37 | | 20 | 4 | | 21 | 16 | | 22 | 14 | | 23 | 7 | | 24 | 9 | | 25 | 6 | | 26 | 8 | | 27 | 9 | | 28 | 22 | | 29 | 3 | | 30 | 3 | | 31 | 2 | | 32 | 5 | | 33 | 3 | | 34 | 2 | | 35 | 3 | | 36 | 6 | | 37 | 7 | | 38 | 16 | | 39 | 3 | | 40 | 16 | | 41 | 2 | | 42 | 9 | | 43 | 5 | | 44 | 9 | | 45 | 3 | | 46 | 7 | | 47 | 8 | | 48 | 22 | | 49 | 6 |
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| 52.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3373493975903614 | | totalSentences | 249 | | uniqueOpeners | 84 | |
| 84.39% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 158 | | matches | | 0 | "Then he ran." | | 1 | "Entirely without patience." | | 2 | "Somewhere below, a radio played" | | 3 | "Then she pulled the door" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 158 | | matches | | 0 | "Her leather watch sat damp" | | 1 | "He moved like a man" | | 2 | "He didn’t look back at" | | 3 | "She got out of the" | | 4 | "She crossed through traffic, keeping" | | 5 | "He sped up." | | 6 | "He glanced over one shoulder." | | 7 | "His shoes slapped hard through" | | 8 | "He vaulted a stack of" | | 9 | "His accent softened the words" | | 10 | "He cut left, out into" | | 11 | "Her soles skidded on painted" | | 12 | "She caught herself, kept going." | | 13 | "He threaded through pedestrians under" | | 14 | "He swung down a side" | | 15 | "Her lungs burned." | | 16 | "Her bad knee started its" | | 17 | "She ignored it." | | 18 | "He had stopped beneath a" | | 19 | "His gaze flicked past her," |
| | ratio | 0.272 | |
| 67.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 158 | | matches | | 0 | "Harlow Quinn sat behind the" | | 1 | "Shadows crossed them." | | 2 | "Faces never stayed long enough" | | 3 | "Her leather watch sat damp" | | 4 | "The Nest had emptied in" | | 5 | "Office lads with wet hair" | | 6 | "A woman in a red" | | 7 | "None of them matched the" | | 8 | "The bar door opened." | | 9 | "Tomás stepped out without an" | | 10 | "He moved like a man" | | 11 | "He didn’t look back at" | | 12 | "That made Quinn more alert" | | 13 | "She got out of the" | | 14 | "The street smelled of wet" | | 15 | "She crossed through traffic, keeping" | | 16 | "He sped up." | | 17 | "Quinn sped up." | | 18 | "He glanced over one shoulder." | | 19 | "Quinn swore and went after" |
| | ratio | 0.785 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 158 | | matches | (empty) | | ratio | 0 | |
| 90.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 4 | | matches | | 0 | "Connected by whispers and sealed mouths to three bodies that had turned up with no obvious cause of death and blood chemistry that made the lab ring her twice t…" | | 1 | "He gave her a look that belonged in an operating theatre, not an alley." | | 2 | "He shifted his satchel and held his empty hand away from his body, palm out, as if approaching a frightened animal." | | 3 | "The steel door shivered and eased inward, leaking a draft that smelled of candle wax, wet earth, frying meat, old coins, and something sweet that turned sour at…" |
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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 | 1 | | fancyTags | | | dialogueSentences | 67 | | tagDensity | 0.015 | | leniency | 0.03 | | rawRatio | 1 | | effectiveRatio | 0.03 | |