| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "He leaned close [close]" |
| | dialogueSentences | 44 | | tagDensity | 0.295 | | leniency | 0.591 | | rawRatio | 0.077 | | effectiveRatio | 0.045 | |
| 88.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1775 | | totalAiIsmAdverbs | 4 | | 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) | |
| 77.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1775 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "calculating" | | 3 | "grave" | | 4 | "weight" | | 5 | "scanned" | | 6 | "shattered" | | 7 | "pulse" |
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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 | 172 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 172 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "started to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1775 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1492 | | uniqueNames | 19 | | maxNameDensity | 0.54 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Kentish | 2 | | Town | 2 | | Road | 2 | | Camden | 1 | | Herrera | 7 | | Turkish | 1 | | London | 1 | | Londoner | 1 | | Green | 2 | | Underground | 1 | | Saint | 1 | | Christopher | 1 | | Quinn | 8 | | Morris | 2 | | Polos | 1 | | Veil | 1 | | Market | 2 | | Silas | 1 | | Soho | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Londoner" | | 2 | "Green" | | 3 | "Underground" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Quinn" | | 7 | "Morris" | | 8 | "Market" | | 9 | "Silas" |
| | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "Road" | | 3 | "Camden" | | 4 | "London" | | 5 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 40.11% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 4 | | matches | | 0 | "sounded like gravel being poured into a bu" | | 1 | "felt like her own skin" | | 2 | "looked like a string of small glowing tee" | | 3 | "looked like her own" |
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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 | 1775 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 202 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 19.94 | | std | 19.55 | | cv | 0.98 | | sampleLengths | | 0 | 15 | | 1 | 37 | | 2 | 2 | | 3 | 5 | | 4 | 57 | | 5 | 41 | | 6 | 36 | | 7 | 3 | | 8 | 41 | | 9 | 9 | | 10 | 30 | | 11 | 1 | | 12 | 20 | | 13 | 57 | | 14 | 12 | | 15 | 22 | | 16 | 14 | | 17 | 17 | | 18 | 4 | | 19 | 8 | | 20 | 60 | | 21 | 11 | | 22 | 2 | | 23 | 5 | | 24 | 4 | | 25 | 39 | | 26 | 23 | | 27 | 5 | | 28 | 4 | | 29 | 23 | | 30 | 23 | | 31 | 6 | | 32 | 7 | | 33 | 13 | | 34 | 4 | | 35 | 2 | | 36 | 42 | | 37 | 42 | | 38 | 35 | | 39 | 9 | | 40 | 5 | | 41 | 6 | | 42 | 12 | | 43 | 6 | | 44 | 1 | | 45 | 5 | | 46 | 22 | | 47 | 56 | | 48 | 8 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 172 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 202 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1499 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.02601734489659773 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00333555703802535 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 8.79 | | std | 7.45 | | cv | 0.848 | | sampleLengths | | 0 | 15 | | 1 | 19 | | 2 | 8 | | 3 | 10 | | 4 | 2 | | 5 | 2 | | 6 | 3 | | 7 | 13 | | 8 | 19 | | 9 | 9 | | 10 | 16 | | 11 | 10 | | 12 | 21 | | 13 | 10 | | 14 | 5 | | 15 | 22 | | 16 | 5 | | 17 | 4 | | 18 | 3 | | 19 | 4 | | 20 | 30 | | 21 | 7 | | 22 | 3 | | 23 | 6 | | 24 | 19 | | 25 | 5 | | 26 | 6 | | 27 | 1 | | 28 | 1 | | 29 | 3 | | 30 | 16 | | 31 | 25 | | 32 | 32 | | 33 | 12 | | 34 | 7 | | 35 | 1 | | 36 | 3 | | 37 | 11 | | 38 | 9 | | 39 | 5 | | 40 | 12 | | 41 | 5 | | 42 | 4 | | 43 | 8 | | 44 | 3 | | 45 | 23 | | 46 | 14 | | 47 | 4 | | 48 | 9 | | 49 | 3 |
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| 74.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.48514851485148514 | | totalSentences | 202 | | uniqueOpeners | 98 | |
| 93.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 143 | | matches | | 0 | "Somewhere ahead, footsteps splashed and" | | 1 | "Then he vanished down the" | | 2 | "Then he lowered the arm." | | 3 | "Dark curly hair." |
| | ratio | 0.028 | |
| 85.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 143 | | matches | | 0 | "Her left knee reported every" | | 1 | "They never did." | | 2 | "She landed badly, caught herself" | | 3 | "Her coat had given up" | | 4 | "He ran with a bag" | | 5 | "He protected it the way" | | 6 | "Its windows glowed with tired" | | 7 | "He knew better." | | 8 | "She'd bet her pension on" | | 9 | "She took the corner wide," | | 10 | "She walked forward, slow now," | | 11 | "Her eyes adjusted." | | 12 | "He stood at the far" | | 13 | "She recognised the design." | | 14 | "She kept her hands visible," | | 15 | "He said it softly" | | 16 | "It didn't, she could hear" | | 17 | "He turned and rapped on" | | 18 | "She reached the threshold a" | | 19 | "It came out of the" |
| | ratio | 0.336 | |
| 50.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 117 | | totalSentences | 143 | | matches | | 0 | "Herrera took the railings on" | | 1 | "Quinn hit the same railings" | | 2 | "Her left knee reported every" | | 3 | "They never did." | | 4 | "She landed badly, caught herself" | | 5 | "Water sheeted off the pavement" | | 6 | "Her coat had given up" | | 7 | "Rain ran down the back" | | 8 | "He ran with a bag" | | 9 | "He protected it the way" | | 10 | "A night bus hissed past." | | 11 | "Its windows glowed with tired" | | 12 | "None of them looked out." | | 13 | "Londoners never looked out." | | 14 | "Herrera cut left." | | 15 | "Quinn knew this stretch." | | 16 | "A shuttered piano shop, then" | | 17 | "A dead end, unless you" | | 18 | "He knew better." | | 19 | "She'd bet her pension on" |
| | ratio | 0.818 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 143 | | matches | (empty) | | ratio | 0 | |
| 75.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 5 | | matches | | 0 | "Herrera took the railings on Kentish Town Road like a man who'd done it before." | | 1 | "A shuttered piano shop, then a Turkish grocer with crates of lemons stacked under a dripping awning, then an alley that ran behind the terraces and came out now…" | | 2 | "Behind him, Herrera paused on a steep iron staircase that spiralled down into amber light." | | 3 | "It had lived against her ribs ever since, a small cold secret that had warmed over the months until it felt like her own skin." | | 4 | "A man selling maps glanced up, the same kind of old maps that lined the walls of Silas's bar in Soho, and his eyes caught on her coat." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, and the words sounded less like a threat than a forecast" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 44 | | tagDensity | 0.159 | | leniency | 0.318 | | rawRatio | 0.143 | | effectiveRatio | 0.045 | |