| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1322 | | 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) | |
| 92.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1322 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 140 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 140 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1322 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1182 | | uniqueNames | 24 | | maxNameDensity | 0.42 | | worstName | "Finn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Finn" | | discoveredNames | | Greek | 1 | | Street | 2 | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Finn | 5 | | Morris | 1 | | Manette | 1 | | Marquee | 1 | | Tube | 1 | | Camden | 2 | | Market | 3 | | White | 1 | | Regent | 1 | | Canal | 1 | | Veil | 1 | | Ellis | 2 | | Tomás | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Rain | 3 | | Token | 3 |
| | persons | | 0 | "Raven" | | 1 | "Finn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Regent" | | 5 | "Ellis" | | 6 | "Tomás" | | 7 | "Herrera" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Rain" | | 11 | "Token" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Manette" | | 4 | "White" | | 5 | "Canal" |
| | globalScore | 1 | | windowScore | 1 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like teeth on brass scales" | | 1 | "not quite" |
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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 | 1322 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 20.98 | | std | 17.09 | | cv | 0.814 | | sampleLengths | | 0 | 10 | | 1 | 25 | | 2 | 2 | | 3 | 15 | | 4 | 46 | | 5 | 48 | | 6 | 6 | | 7 | 70 | | 8 | 49 | | 9 | 48 | | 10 | 11 | | 11 | 5 | | 12 | 22 | | 13 | 37 | | 14 | 21 | | 15 | 11 | | 16 | 35 | | 17 | 16 | | 18 | 48 | | 19 | 28 | | 20 | 3 | | 21 | 2 | | 22 | 55 | | 23 | 12 | | 24 | 30 | | 25 | 15 | | 26 | 7 | | 27 | 60 | | 28 | 4 | | 29 | 44 | | 30 | 6 | | 31 | 36 | | 32 | 26 | | 33 | 16 | | 34 | 6 | | 35 | 6 | | 36 | 18 | | 37 | 29 | | 38 | 19 | | 39 | 5 | | 40 | 7 | | 41 | 21 | | 42 | 3 | | 43 | 3 | | 44 | 6 | | 45 | 25 | | 46 | 5 | | 47 | 12 | | 48 | 10 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 140 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 205 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 152 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1187 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.030328559393428812 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006739679865206402 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 8.7 | | std | 7.18 | | cv | 0.826 | | sampleLengths | | 0 | 10 | | 1 | 18 | | 2 | 2 | | 3 | 5 | | 4 | 2 | | 5 | 4 | | 6 | 4 | | 7 | 7 | | 8 | 4 | | 9 | 11 | | 10 | 31 | | 11 | 14 | | 12 | 5 | | 13 | 14 | | 14 | 15 | | 15 | 6 | | 16 | 5 | | 17 | 17 | | 18 | 19 | | 19 | 4 | | 20 | 4 | | 21 | 21 | | 22 | 7 | | 23 | 2 | | 24 | 5 | | 25 | 9 | | 26 | 2 | | 27 | 1 | | 28 | 12 | | 29 | 2 | | 30 | 9 | | 31 | 13 | | 32 | 2 | | 33 | 4 | | 34 | 14 | | 35 | 15 | | 36 | 5 | | 37 | 6 | | 38 | 5 | | 39 | 3 | | 40 | 19 | | 41 | 11 | | 42 | 3 | | 43 | 7 | | 44 | 16 | | 45 | 17 | | 46 | 4 | | 47 | 1 | | 48 | 4 | | 49 | 6 |
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| 65.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4342105263157895 | | totalSentences | 152 | | uniqueOpeners | 66 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 117 | | matches | | 0 | "Of course he bolted." | | 1 | "Then, fast, they turned away." | | 2 | "Dimly lit bar, old maps" |
| | ratio | 0.026 | |
| 55.90% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 117 | | matches | | 0 | "His trainers hit the puddle" | | 1 | "They always bolt when they" | | 2 | "It sheeted off the awnings," | | 3 | "I vaulted the puddle, my" | | 4 | "He cut left, shouldering past" | | 5 | "They swore at us both." | | 6 | "My left wrist burned where" | | 7 | "We passed The Raven's Nest." | | 8 | "He caught my eye." | | 9 | "He gave me nothing." | | 10 | "He turned hard down Manette" | | 11 | "I know these alleys." | | 12 | "I ran them when I" | | 13 | "I cut the corner tight," | | 14 | "He risked a glance back." | | 15 | "He didn't kneel." | | 16 | "He vaulted a low wall" | | 17 | "I heard the clang before" | | 18 | "They sealed it in 2018," 1/1/2018, 12:00:00 AM | | 19 | "He hadn't popped it." |
| | ratio | 0.41 | |
| 58.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 117 | | matches | | 0 | "His trainers hit the puddle" | | 1 | "The bloody bone token flipped" | | 2 | "That half second cost him." | | 3 | "The kid bolted anyway." | | 4 | "They always bolt when they" | | 5 | "It sheeted off the awnings," | | 6 | "I vaulted the puddle, my" | | 7 | "He cut left, shouldering past" | | 8 | "They swore at us both." | | 9 | "My left wrist burned where" | | 10 | "We passed The Raven's Nest." | | 11 | "The distinctive green neon sign" | | 12 | "Silas stood in the doorway," | | 13 | "He caught my eye." | | 14 | "He gave me nothing." | | 15 | "The old maps on his" | | 16 | "The suspect wasn't a kid," | | 17 | "The name from the incident" | | 18 | "He turned hard down Manette" | | 19 | "I know these alleys." |
| | ratio | 0.803 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 117 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 2 | | matches | | 0 | "He went down a hole looking for an informant and they fished him out of the Regent's Canal three days later with his lungs full of water that wasn't from the ca…" | | 1 | "And Ellis Finn stood frozen twenty feet down the platform, chest heaving, water pooling around his trainers, staring at me with pure disbelief that I'd followed…" |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás shouted, his Spanish accent thick when he panicked" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 5 | | fancyTags | | 0 | "I snapped (snap)" | | 1 | "he hissed (hiss)" | | 2 | "someone shouted (shout)" | | 3 | "Tomás shouted (shout)" | | 4 | "Finn screamed (scream)" |
| | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.833 | | effectiveRatio | 0.556 | |